2026 Summer Undergraduate Research Fellowship Project Descriptions

Faculty Mentor:  Nathan Lenssen| Applied Mathematics and Statistics

Project Abstract: 

The global oceans are critical for determining the climate and weather over land. The amount of energy the ocean transfers to the atmosphere is determined to first order by the sea surface temperature; warmer oceans provide more energy to the atmosphere. Recent work has shown that commonly-used observational ocean datasets contain biased estimates of temperature in the early 20th century. Working with scientists in the UK and at NCAR in Boulder, we have run atmospheric simulations in a global climate model using both biased and bias-corrected sea surface temperatures and are curious how this change in sea surface temperature pattern affects climate over land. In particular, we are curious if these improved sea surface temperature simulations better represent the dust bowl, a multi-year drought event in the central United States. In this project, we will compare these two sets of simulations to assess changes in temperature, precipitation, soil moisture, and wind across the great plains to determine if one set of simulations better matches the observed evolution of the climate during this extreme event.

Student’s role and learning objectives: 

The student will gain invaluable mapping experience. As stated above, there is a bit of flexibility in what the undergrad student would want to focus on. Examples of past projects include surficial deposit mapping, and fault/joint mapping and structural analysis. One could also focus on specific rock types, structures, or metamorphic or igneous characteristics. Ideally the student would present at our undergraduate student research fair in February, in addition to the University-wide Undergraduate Research conference. The student would receive extensive mentoring by myself and my graduate students. Especially while in the field (typically about 4 days a week during the summer), they would work directly with a graduate student at all times, and with me when I join. In addition, I typically set up meetings in the office throughout the summer with the student(s) as needed and as the field schedule allows.

Faculty Mentor:  Kaveh Fathian | Applied Mathematics and Statistics

Project Abstract: 

This project aims to design and implement a fully automated pipeline for processing soil samples for efficient and reliable mineral detection. The system will consist of commercial automation components (e.g., pumps, actuators, conveyors, sensors, controllers) with custom-designed mechanical, electrical, and software modules.

The student will help us refine the automation pipeline architecture through design reviews, vendor consultations, and prototype testing. A major component of the project involves purchasing, integrating, and evaluating off-the-shelf automation modules for the pipeline. For modules without an existing commercial solution, the student will design and fabricate custom modules using CAD, 3D printing, machine shop tools, embedded systems, PLCs, and microcontrollers. The student will lead component acquisition, system assembly, and integration into a functional prototype.

The immediate goal is to deliver a fully operational automation prototype that demonstrates end-to-end soil sample processing by the end of summer. Long-term, this project aims to evolve into a research platform supporting advanced data analysis and machine learning for mineral detection, with potential translation into a startup company serving mining and soil analysis laboratories.

Student’s role and learning objectives: 

By participating in this project, students will:
– Learn how to design a complete automation architecture, from high-level system blueprint to detailed mechanical, electrical, and software implementation
– Evaluate and integrate commercial automation components, developing the ability to interpret vendor documentation, compare specifications, and make informed engineering trade-offs
– Develop cross-domain technical skills in mechanical design (CAD, fabrication), electrical systems (actuators, sensors, PLCs), and programming (controllers, embedded systems)
– Lead procurement and vendor engagement, gaining experience in professional communication, quotation analysis, and technical negotiation
– Assemble and validate a complex multi-component system, performing iterative testing, debugging, and refinement under real project deadlines
– Practice independent engineering judgment, proposing solutions before seeking feedback and refining designs collaboratively
– Build a foundation for advanced research and innovation, with potential progression toward PhD-level work involving novel machine learning methods for automated mineral detection

Faculty Mentor:  Ebru Bozdag | Applied Mathematics and Statistics

Project Abstract: 

Advances in high-performance computing, combined with developments in numerical methods, have enabled unprecedented simulations of seismic wave propagation in realistic 3D Earth models, refining our understanding of Earth’s inner dynamics and earthquakes and mitigating related hazards. Meanwhile, quantum computing opens new frontiers in scientific computing, offering promising runtime improvements for numerous computational problems, while it remains relatively new in seismology to be explored. Recent studies suggest that quantum computing has the potential to accelerate wave simulations exponentially compared to classical CPU/GPU computers, which is essential for large-scale imaging problems, not only limited to illuminating Earth’s interior and dynamics but also to address industrial and engineering problems, medical imaging, etc., utilizing 3D numerical wave simulations and potential big data. Following recent attempts in seismic problems (Schade et al. 2024; Bösch et al. 2025), we will investigate the quantum computing of 1D elastic wave simulations in heterogeneous media. To this end, we will first review the theoretical formulation of the problem, which involves transforming the discrete elastic wave equation into a Schrödinger equation. For simplicity, we will start with a finite-difference discretization of the 1D wave equation and then explore how to combine it with spectral-element methods, ultimately demonstrating wave simulations on quantum computers to lay the foundations for 2D/3D simulations and, in the future, for conducting optimization problems on quantum computers. The project will be carried out in collaboration with Prof. Steve Pankavich (Mines – AMS) and Prof. Andreas Fichtner’s group at ETH Zurich.

Student’s role and learning objectives: 

Within this project, the student will gain experience and advance his background in the following areas:

– Mastering elastic wave theory
– Experience in numerical simulations of seismic wave propagation with finite difference and spectral-element methods
– Working on the theoretical foundation of the solution of the wave equation on quantum computers (i.e., Schrödinger equation)
– Gaining hands-on experience in quantum computing with 1D wave simulations.

As part of this research, the student will also gain experience in

– Conducting scientific research,
– Gaining experience in scientific writing, potentially submitting the research products to peer-reviewed publications,
– Presenting research results in conferences and/or workshops.

Prof. Ebru Bozdag will coordinate the research and provide daily/weekly supervision, working directly with the student throughout the research program. The student will also be co-advised by Prof. Steve Pankavich (Mines – AMS) and collaborate with Prof. Andreas Fichtner’s group at ETH Zurich.

Faculty Mentor:  John Griesmer | Applied Mathematics and Statistics

Project Abstract: 

Given two sets of real numbers A and B, the sumset A + B is the set of numbers that can be formed by adding an element of A to an element of B. Symbolically, A + B := { a + b : a is in A, b is in B}. For example, if A = { 0, 1, 3 } and B = { 2, 4, 6 }, then A + B = { 2, 3, 4, 5, 6, 7, 9 }. One of the major branches of additive combinatorics studies the structure of sets whose sumset is small relative to the size of A and B. For example, if A and B are nonempty sets of integers, then |A + B| >= |A| + |B| – 1, and it is an easy exercise to prove that if |A + B| = |A| + |B| – 1, then |A| = 1, |B| =1, or A and B are arithmetic progressions with the same common difference.

The Freiman-Ruzsa theorem is a powerful generalization of this fact, which characterizes sets of integers A satisfying |A + A| < K|A|, where K is a fixed constant. Obtaining an optimal description of such sets is the goal of the Polynomial Freiman-Ruzsa conjecture (also known as Marton’s conjecture). After a series of breakthroughs in the late 1990s through 2024, the conjecture was settled in the finite field setting by Tim Gowers, Ben Green, Freddie Manners, and Terence Tao. The conjecture remains open in the integers.

This project is aimed at understanding the state-of-the-art harmonic analysis techniques used to prove bounds for the Freiman-Ruzsa theorem, as well as the new entropy methods introduced in the Gowers-Green-Manners-Tao (GGMT) approach. The student will investigate obstructions to adapting the latter technique in the integers, and propose new approaches. The end result will be a clear explanation of an analogue of the GGMT technique for the integers, why it fails to produce the desired bounds, and an assessment of whether a fundamentally different approach is necessary.

Student’s role and learning objectives: 

The student will begin learning the basics of harmonic analysis in additive combinatorics, including Fourier transforms, Bogoliouboff’s theorem and Folner’s theorems on difference sets, and Furstenberg’s proof of and inverse sumset theorems, as covered in textbooks such as Nathanson’s Inverse Problems and the Geometry of Sumsets, or Tao and Vu’s Additive Combinatorics. Specifically: Vosper’s Theorem and Kneser’s theorem for finite abelian groups, and Ruzsa’s proof of Freiman’s theorem. The mentor and student will meet biweekly for lectures, with the student doing directed readings and exercises. Once sufficient background is acquired, the student and mentor will work through the Gowers-Green-Manners-Tao paper together and attempt to prove (or disprove) the polynomial Freiman-Ruzsa conjecture in the integers. Any obstructions to applying existing methods will be carefully described in a short paper written by the student.

Faculty Mentor:  Debra Carney | Applied Mathematics and Statistics

Project Abstract: 

This NSF funded project aims to study an Inquiry Orientated Dynamical Systems and Modeling curriculum for future prospective teachers. The project has three goals: (1) to create an open-source semester where modeling applications include a variety of environmental, ecological, sustainability, and social issues of interest to students, (2) to investigate the impact of this newly developed curriculum on prospective secondary teachers’ knowledge of content intimately connected to high school mathematics, the ways in which they engage in the eight Common Core State Standards for Mathematical Practice, their beliefs about learning and teaching mathematics, and contributions to their emerging professional practices; and (3) to build human capacity among the potential community of users and those who are interested in modifying upper-division mathematics courses for prospective teachers through various outreach endeavors. This design-based research project involves data collection at three different universities: Colorado School of Mines, San Diego State University, and Western Kentucky University. Classroom experiment data includes student artifacts, video recordings, and survey data. Data collection and curriculum building will have concluded by the end of the spring 2026 semester and the project will move into the next two phases. At that time work will shift to data analysis (both quantitative and qualitative), dissemination, and outreach.

Student’s role and learning objectives: 

Students will have an opportunity to participate in an NSF funded, cross-university mathematics education research project and with team of researchers including faculty, graduate students, and undergraduate students. The three principal investigators for the project are Prof. Chris Rasmussen (SDSU), Prof. Nick Fortune (WKU), and Prof. Debra Carney (Mines) and will provide mentoring along with the rest of the team. Students can expect to attend weekly project zoom meetings throughout the summer with the larger project team. Some independent work will occur as well. Students will gain experience with (1) mathematics education qualitative research and data analysis methods; (2) scientific writing, potentially leading to peer-reviewed publications; (3) presenting research results in conferences and/or workshops. Student researchers may also support the project’s outreach efforts by contributing to the teacher materials and creating a project website.

Faculty Mentor:  Matthew Crane | Applied Mathematics and Statistics

Project Abstract: 

Conductive nanoparticles have incredible properties, including enhanced optical absorption under certain wavelengths of light, a property that stems from a localized surface plasmon resonance or LSPR. These LSPRs depend on the nanoparticle’s composition, size, and geometry to enhance light absorption. As a result, metallic nanoparticle optical properties can be engineered by adjusting these properties. LSPRs of nanoparticles also create unique near field profiles that concentrate light around the nanoparticle in hot spots, which can be used to drive chemistry, sense particles, scatter light, and more. In addition, the LSPR enhanced optical response of scattering and absorption is polarization dependent, creating unique interactions with different polarizations of light, including, for properly designed nanostructures, changing the polarization of scattered light. By tuning the nanoscale features of these particles, especially their near-field responses to light, we can control macroscopic phenomena, like how data is transmitted and stored for next-generation computing applications. Creating these computing devices with tailored optical responses thus requires new methods to tune their structures with nanometer-scale precision.

In this project, we will synthesize new plasmonic materials by adding reactive single atoms onto their surfaces. These single-atom sites can drive efficient chemistry that breaks traditional scaling relationships. We will study how synergy between plasmonic substrates that are active for CO2 chemistry and single-atoms active for CO2 chemistry can lead to enhanced outcomes. Students will synthesize and characterize these new materials. Then, they will investigate how light changes the reactivity of CO2 on these materials under different conditions to understand the mechanisms the drive reactivity and selectivity.

Student’s role and learning objectives: 

In this project, we will synthesize new plasmonic materials by adding reactive single atoms onto their surfaces. These single-atom sites can drive efficient chemistry that breaks traditional scaling relationships. We will study how synergy between plasmonic substrates that are active for CO2 chemistry and single-atoms active for CO2 chemistry can lead to enhanced outcomes. Students will synthesize and characterize these new materials. Then, they will investigate how light changes the reactivity of CO2 on these materials under different conditions to understand the mechanisms the drive reactivity and selectivity. Students will learn all of these synthesis and characterization skills as well as how to read published manuscripts, prepare data into figures, and communicate scientific results.

The student will conduct research under the guidance of the graduate students and should expect to interact and learn from them every time they are in the lab. The student will meet with Dr. Crane and or a graduate student mentor once a week to set short term goals, discuss progress and present findings. The student will be expected to attend weekly group meetings.

Faculty Mentor:  Anuj Chauhan| Chemical and Biological Engineering

Project Abstract: 

Sustained delivery of drugs via skin is useful in overcoming low bioavailability and high first pass metabolism. However low skin permeability is a significant barrier particularly for hydrophilic or large molecular weight drugs. We propose to develop light sensitive hydrogels which can warm up in response to light. The elevated temperature will trigger release of drugs and additionally increase drug permeability in skin. The light sensitivity will be achieved by loading the hydrogels with gold nanoparticles which absorb light due to the local surface plasmon resonance. The project will start with designing gold nanoparticle loaded hydrogels and measuring response to light. Also, hydrogels will be loaded with drugs of interest to measure the effect of temperature increase on release profiles. Finally, ex vivo drug delivery across skin will be measured to demonstrate that light exposure can increase delivery.

Student’s role and learning objectives: 

1. Students will learn to design hydrogels including encapsulation of gold nanoparticles and drugs.
2. Students will also learn to use uv-vis spectrophotometry to measure drug release profiles.
3. Students will also learn to design the ex vivo experiment to measure drug transport across skin or skin mimics.

Faculty Mentor: Carolyn Koh | Chemical and Biological Engineering

Project Abstract: 

Interfacial properties of clathate hydrates are important in carbon capture applications, including carbon dioxide transportation, storage, sequestration. Furthermore, the interfacial techniques (interfacial tension, wettability, emulsion stability) are important in a wide range of research areas. The goals of this project are to measure and analyze the interfacial properties and crystal growth behavior of clathrate hydrate systems for carbon capture applications.

Student’s role and learning objectives: 

Weekly meetings with Prof. C. Koh and a graduate student in the hydrate center. C. Koh & the graduate student co-mentor will advise the student with the formulation, design, methods, analysis of the research in the project.

Faculty Mentor: Carolyn Koh | Chemical and Biological Engineering

Project Abstract: 

Nanofluids systems that are thermodynamically or kinetically stabilized with chemical additives are potentially viable solutions to carbon dioxide separation and capture challenges from industrial greenhouse gas streams. Furthermore, nanofluid characterization methods (e.g., interfacial tension, emulsion stability, nanoparticle size analysis) are topical and applicable to many research and industry fields. The goal of this project is to formulate nanofluids of droplet sizes <100 nm followed by characterization of their thermal, chemical, and compositional stability utilizing interfacial and rheological properties.

Student’s role and learning objectives: 

The student would gain skills and experience in emulsion formulation methods and characterization techniques for nanofluids including interfacial tensiometer, particle size analysis with dynamic light scattering, gas chromatography, and rheology. These skills will enable the student to support the development of nanofluids. For mentoring, weekly meetings would be arranged with Prof. C. Koh and a Postdoctoral Fellow in the hydrate center. Including in laboratory mentoring from the postdoctoral fellow to advise the student with the formulation, design, methods, and analysis of the research project

Faculty Mentor: Carolyn Koh | Chemical and Biological Engineering

Project Abstract: 

Interfacial properties are critical in all energy applications of clathrate hydrate crystalline compounds (e.g. fuel recovery and storage, carbon sequestration, and desalination). Furthermore, the interfacial techniques (interfacial tension, wettability, emulsion stability) are important in a wide range of research areas. The goals of this project are to measure and analyze the interfacial properties of clathrate hydrates for clean energy applications. Specifically, the effects of different sizes and charges of ions on the interfacial properties of clathrate hydrate particles will be investigated.

Student’s role and learning objectives: 

Hands-on experimental research skills, interfacial tension and contact angle measurement methods, crystal growth techniques.

MENTORING PLAN – Weekly meetings with Prof. Koh, and daily interactions and co-mentorship with graduate student.

Faculty Mentor: Kevin Cash | Chemical and Biological Engineering

Project Abstract: 

This project is to determine the impact on varying nanoparticle size for our oxygen-sensitive nanosensors. These particles are typically ~100-200 nm in diameter, and in this work we want to use controlled fabrication approaches (flash nanoprecipitation) to vary the size of the sensors to monitor changes in sensor characteristics (response, lifetime, stability) as a function of size.

Student’s role and learning objectives: 

Analytical characterization (UV-Vis spectroscopy, fluorescence spectroscopy, fluorescence imaging, particle characterization) Particle fabrication (flash nanoprecipitation, solvent emulsification nanoprecipitation) Sensor development and characterization. Weekly lab group meetings, mentoring from current graduate students and other students in the lab, as needed individual meetings or subgroup meetings.

Faculty Mentor: Kevin Cash | Chemical and Biological Engineering

Project Abstract: 

This project is to help develop a 3D printed/rapid prototyped small fluorescence imaging system. Traditional small animal imaging systems are expensive (measured in the millions of dollars), precluding use in research settings without extensive needs and funding. Our lab is developing a 3D printed animal imager that can handle some use cases, but at a significantly reduced cost (hundreds to thousands of dollars). Our initial prototype is able to do three color imaging, but in our future versions we are redesigning the imaging system to enable hyper spectral (many color) imaging, spatial reconstruction, and other more advanced approaches. For the most part, different aspects of this project will be better fits for different students. No REQUIRED background, although some examples of strongly beneficial backgrounds: LED driver system and power modulation design: EE background or prior work with LED drivers and LED illumination systems. Illumination/imaging design: Physics/optics background or prior work with imaging applications. Mechanical design, 3D printing: ME background, 3D printing and design background, rapid prototyping experience Control software, integration: extensive python background (and/or willingness to self teach more complex python usage). Application work and development: cell imaging, biological application interest

Student’s role and learning objectives: 

The student will perform advanced experiments using the In Vivo Imaging System (IVIS), including applying nanosensors, conducting DNA gel electrophoresis, and testing live plants and tissues within the imaging box system. Their work will also involve developing and characterizing Short Wave Infrared (SWIR) nanosensors. Furthermore, the student will contribute to chemical imaging research to improve drug detection methods and produce detailed technical documentation of their findings to contribute to peer-reviewed publications. Students will be mentored by the PI and a graduate student through group and individual meetings as necessary.

Faculty Mentor: Suzannah Beeler | Chemical and Biological Engineering

Project Abstract: 

A currently under-explored aspect of biology lies in the surprising fact that magnetic fields can perturb living organisms. As just one example, tadpoles grown in the absence of Earth’s magnetic field demonstrate significant developmental abnormalities. These results are quite surprising, and our current understanding of biology fails to explain any role that magnetic fields should play in the realm of living matter. Biological impacts of magnetic fields have been measured in a range of areas, from microbial life to plants and animals.

In this project, we aim to better quantify the impacts of magnetic fields on microbial systems – studying growth, metabolism, and eventually species-species interactions and microbial diversity. Towards that, the student on this project will be developing the tools to monitor growth and metabolism of microbial systems under different static magnetic fields.

Student’s role and learning objectives: 

The student’s role during this SURF fellowship is to validate and develop magnetic field impacts on cellular growth and metabolism using model microbial organisms grown in the presence of static magnetic fields. By the end of this SURF fellowship, students should be able to reproducibly grow cell cultures on small scale and measure their growth curves in the presence or absence of magnetic fields. Pending student interest, they may also fabricate systems (with 3D printing and rapid prototyping approaches) to control the magnetic field.

Faculty Mentor: Ramya Kumar | Chemical and Biological Engineering

Project Abstract: 

Genome editor proteins like CRISPR/Cas9 offer previously unattainable cures for genetic diseases like cystic fibrosis, muscular dystrophy, and sickle cell disease. However, severe intracellular delivery challenges limit their accessibility and affordability. To make genome editor proteins accessible to broad populations, we need to design safe, effective, and affordable carriers from economical materials. Synthetic polymers are promising materials because of their versatility and scalability, but experimentally determining optimal polymer properties among thousands of potential combinations is time and cost prohibitive. Machine learning can accelerate this discovery process, but its success depends on the rapid generation of large, reproducible, and chemically diverse polymer libraries, something traditional batch synthesis struggles to provide. To generate these polymer libraries, we will synthesize polymers in our recently built automated high-throughput polymerization reactor. After purifying the polymers, we will characterize the polymer molecular weight (with SEC-MALS), composition (with NMR), and pka (with titrations) and form complexes of polymers with genome editor proteins. We will determine the polymer-protein complex melting temperature using differential scanning fluorimetry (DSF) and size using dynamic light scattering (DLS). After measuring the delivery performance of these polyplexes in cells, we will feed the data to a machine learning model which will iteratively suggest subsequent polymers to synthesize until we converge on the best-performing polymers.

Student’s role and learning objectives: 

Roles and Learning Objectives: The undergraduate student will work closely with a PhD student mentor to learn and then independently perform various polymer purification (dialysis and de-salting columns) and characterization (NMR, SEC-MALS, DLS, DSF) techniques. The student will also perform automated flow polymerization and assist in method optimization and development for each of these high-throughput synthesis, purification, and characterization techniques.

Mentoring Activities: The graduate student will meet as needed with the undergraduate student to provide structured guidance and explain tasks. Both students will work together to analyze data, troubleshoot issues, and plan next steps.

Faculty Mentor: Ramya Kumar | Chemical and Biological Engineering

Project Abstract: 

Polymer-nucleic acid complexes (polyplexes) have emerged as promising genetic therapeutic alternatives to viral vectors due to their low cost and versatility. While much research has focused on how the chemical properties of polymers shape their biological performance, as soon as polyplexes enter the body, proteins are adsorbed to the surface. The protein corona reshapes the biological identity of polyplexes, modulates cellular uptake, and dictates in vivo fate (biodistribution, immune responses, and circulation time). This project will utilize a creative suite of analytical techniques to characterize the protein corona formed around polyplexes formed from amphoteric polymers. By creating a library of amphoteric polymers incorporating cationic, anionic, and moderately hydrophobic monomers, rational engineering of protein corona composition can be done. Polyplex will be characterized via AF4, proteomics, and light scattering. Initial studies will use human plasma from blood samples as the model biofluid although this approach can be extended to other biofluids (lung fluids, mucus, fetal bovine serum, etc.) as needed.

Additional Resources: https://www.sciencedirect.com/science/article/pii/S0168365920302558 https://www.sciencedirect.com/science/article/pii/S0169409X18301832?pes=vor https://www.sciencedirect.com/science/article/pii/S1742706118303362?via%3Dihub

Student’s role and learning objectives: 

Qualifications: None required. Some chemistry, molecular biology, and coding may be helpful.

Hours: 8-12 hours per week.

Skills gained: polymer synthesis, air-free technique, Schlenk line, semi-batch polymerization, characterization using nuclear magnetic resonance spectroscopy, dynamic light scattering, static light scattering, size exclusion chromatography, asymmetric flow field flow fractionation. The student will gain unique insights into applying to and succeeding in graduate school as an independent researcher.

Mentoring: The student will work closely with a graduate student to learn basic laboratory skills and safety. Then, the student will be allowed to work more independently using the training they have received.

Faculty Mentor: Ramya Kumar | Chemical and Biological Engineering

Project Abstract: 

While advances in genetic therapies have enabled the treatment of previously incurable disorders, efficacious in-vivo delivery remains a major challenge. A successful delivery vehicle must overcome multiple, sometimes contradictory, challenges to reach an afflicted cell. Existing delivery vehicles such as viral vectors are expensive, cargo-size limited, and can induce immunogenic response, prompting the exploration of safer, more affordable nonviral alternatives like polymers. By varying monomer composition, chain length, and sequence, polymers provide a near limitless design space to investigate as gene therapeutic delivery vehicles. The ability to synthesize polymers in a high-throughput manner to control sequence, length, and composition would represent a major advancement to efficiently explore this design space. Photo-catalyzed polymerization provides a platform to synthesize polymers with control over composition, chain length, and sequence, but challenges remain to implement this workflow successfully in our lab. The overarching goal of this project will be to design a highly efficient workflow for photo-catalyzed polymerization in collaboration with a PhD student. Specifically, the student will be tasked with optimizing the purification of the polymer after synthesis.

Student’s role and learning objectives: 

Student’s role and learning objectives: 
Roles and Learning Objectives:
The undergraduate for this project will learn how to synthesize, purify, and characterize polymers in a wet lab under the direction of PhD students. Learned analytical techniques will include nuclear magnetic resonance (NMR) and size exclusion chromatography (SEC). No prior lab or research experience is needed, but the right student will be excited about the project, work well in a team, and think critically about solving problems. This student will gain direct experience working in a wet lab, characterizing data, and designing experiments. This project is a great opportunity to gain experience for those interested in academic research or working within industry.
Mentoring Activities:
This student will work closely with PhD students, the faculty mentor, and other lab members to train, learn, and be successful during this project. They will coordinate regularly with their PhD student mentor on the direction of their work and will later gain more autonomy in directing their experiments. The student will present twice at a lab group meeting and will later present a poster in the Fall.

Faculty Mentor: Jenny Ketterling | Chemical and Biological Engineering

Project Abstract: 

This research aims to elucidate the metabolic synergies between bacteria, fungi, and plants through integrated metabolomics and computational modeling. Plant-microbe interactions are known to bolster crop resilience and growth, the specific flux of metabolites between hosts and symbionts remains poorly quantified at a systems level. Plants will be grown in hydroponic systems and inoculated with specific microbial strains under controlled environmental conditions. Root exudates and intracellular extracts will be analyzed using Gas Chromatography-Mass Spectrometry (GC-MS) to identify key primary and secondary metabolites. This experimental data will be used to constrain and refine Genome-Scale Metabolic Models (GEMs). By integrating metabolite concentrations into a stoichiometric framework, we can predict metabolic flux using Flux Balance Analysis (FBA).

Student’s role and learning objectives: 

The student will be involved in cultivating the plant-microbe pairs and ensuring sterile inoculation and tissue culture techniques. They will harvest root exudates and biomass at specific time intervals. They will also help prepare samples for GC-MS analysis, including extraction and derivatization of metabolites. They will be involved in the computational analysis of the data and how it integrates into a Genome-Scale Metabolic Model.

Faculty Mentor: Mike McGuirk | Chemistry

Project Abstract: 

Porous materials have an array of applications, from energy storage to sensing. The properties these materials display are intrinsically linked to the interatomic connectivity that links molecules together in space. This project is part of an investigation into alternative modes of bonding that can assembly and stabilize low-density, porous materials.

Student’s role and learning objectives: 

The student will be involved in molecular synthesis and characterization, as well as material assembly and characterization. The student will gain skills in synthesis, a suite of analytical techniques, experimental design, and computational tools.

Faculty Mentor: Mike McGuirk | Chemistry

Project Abstract: 

Cooperatively flexible metal–organic frameworks are a subclass of porous adsorbents that exhibit sorption induced phase changes. In certain cases these phase changes result in desirable adsorption–desorption profiles that can enable low energy storage and delivery of gaseous payloads. However, the structural and chemical origins of these phase changes are poorly understood. In this project we seek to advance the understanding of these phase changes, towards their deliberate design for target applications, such as the storage and delivery of hydrogen gas to be used as a fuel.

Student’s role and learning objectives: 

The student will be responsible for the synthesis and characterization of the flexible metal–organic frameworks. They will be educated in wet chemistry synthetic techniques, as well as structural and chemical characterization tools, such as PXRD, NMR, FT-IR, and gas sorption. Additionally, students will learn experimental design and scientific presentation skills. The student will be paired with a senior Ph.D. student or a post-doctoral researcher.

Faculty Mentor: Mike McGuirk | Chemistry

Project Abstract: 

Aminopolymers are an accepted technology for the capture of carbon dioxide from the atmosphere and point sources. However, they exhibit poor stability to cycling and are sourced from petrochemicals. This project will focus on both extending the lifetime of these polymers and developing alternative means for synthesizing them.

Student’s role and learning objectives: 

The student will work with a Ph.D. student from materials science. They will be directly involved in the synthesis, testing, and characterization of the aminopolymers. The student will learn material synthesis, various spectroscopic characterization techniques, and testing of performance. Additionally, presentation and writing skills will be taught.

Faculty Mentor: Brian Trewyn | Chemistry

Project Abstract: 

Micro and nano plastics (MNPs) are an emerging area of interest for the scientific community, however our understanding of them is still limited. MNPs have been found and identified within nearly every type of water system, terrestrial systems, plants, biota, and the human body. Additionally, they have been found to be able to absorb and transfer other harmful chemicals such as pesticides and polyfluoroalkyls (PFAs). With MNPs variability in size, shape, chemical and mechanical properties and with such little understanding of how they are moving through the environment, modeling has been heavily utilized to try to start understanding the impact, fate, transport and risk these materials pose. This research would focus on the current damage done by MNP and help evaluate future solutions and prevention of MNP formation. Field sampling will be conducted along side Denver Public Health and Environment in the South Platte River in Denver . Then samples will be analyzed and compared to other urban river systems around the US and the Caribbean to help create a full picture of how MNPs are moving within urban river systems. Students will also conduct agricultural soil sampling for MNPs. Agricultural soils have not been sampled for MNPs as frequently as aquatic systems have. Although the project is focusing on urban fresh water system, understanding how agricultural soils might be contributing MNPs to fresh water systems will be important for understanding MNP movement.

Student’s role and learning objectives: 

The undergraduate student will play an active, hands-on role in all phases of this research, from sample collection and laboratory analysis to data processing and interpretation. Specifically, the student will assist with microplastic isolation through digestion, density separation and filtration in order to perform polymer identification using established analytical methods. Through this work, the student will develop core technical skills in laboratory safety, sample handling, and analytical chemistry, as well as broader competencies in data analysis, scientific writing, and critical evaluation of results. Mentoring will be provided through structured, weekly check-ins, direct laboratory supervision during new techniques, and guided discussions linking experimental results to current microplastics literature. The student will also receive support in developing a research poster or written report, with an emphasis on communicating scientific findings clearly and responsibly, preparing them for future research or graduate study in environmental science and engineering.

Faculty Mentor: Samantha Johnson | Chemistry

Project Abstract: 

Recent research has shown that reactions in micro- and nanodroplets can be accelerated relative to bulk solutions. In some cases, new products are being formed. The droplet environment in which this occurs, however, is poorly understood. We use a combination of computational methods (density functional theory and molecular dynamics) to simulate these droplets in order to paint a molecular picture of processes occurring in these droplets. One system of interest is mixed acetonitrile/water droplets. Because of their unique attractive interaction, these droplets often take on interesting structures. As such, we want to understand how protons transfer in these clusters. This summer, students will use cluster models in order to elucidate how protons move around these small clusters and change their structure. This will help us train models which we can use for larger cluster systems, as well as give a deeper understanding of proton transfer in mixed solvent systems.

Student’s role and learning objectives: 

The student will carry out calculations on these clusters. Codes have been developed within the group which will help the student, but if there is interest in coding, that can be accommodated. No coding experience, however, is necessary. The student will also perform analysis on their data, working to interpret their calculations for use by a broader audience.

The student will learn the basics of working with a supercomputing cluster, density functional theory, and apply concepts from physical chemistry, like understanding intermolecular interactions. The student will also practice science communication in the form of presentations and a report at the end of the project.

The student will be mentored directly by the PI Sam Johnson, as well as by other members of the group. This mentorship will take place through one-on-one meetings, group meetings, and subgroup meetings. The student will also be invited to attend group social/cultural activities to build inclusivity and group cohesion.

Faculty Mentor: Daniel Knaus | Chemistry

Project Abstract: 

The project will focus on the synthesis and characterization of polymers for ionic and small molecule transport for application in electrochemical devices, water purification, and gas transport. Block copolymers that contain matrix components that impart good mechanical properties as well as a component that is conducive to small molecule or ionic transport will be prepared and characterized. Polymers of different composition will be studied.

Student’s role and learning objectives: 

The student will develop polymer and organic chemistry synthesis and characterization as well as writing skills. The student will work with graduate students and independently on the preparation of new monomers and the formation of block copolymers and functionalized polymers. The new polymers will be characterized through chemical techniques and through mechanical property evaluation.

The student will participate in group meetings as well as weekly one-on-one meetings with the professor. Overall goals will be determined at the beginning of the project and biweekly milestones will be set as discussion points for addressing progress during meetings. The student will be mentored on literature searching and individual laboratory techniques during the summer.

The goals are to investigate the synthesis of modified polymers and copolymers for use in ion transport applications. Students must have completed organic chemistry 1 and 2 by the start of summer and prior research experience is a plus.

Faculty Mentor: Shubham Vyas | Chemistry

Project Abstract: 

The decommission of munitions, specifi cally nitramine explosives like RDX (1,3,5-trinitro-1,3,5-triazine), through open burning and detonation presents signifi cant environmental challenges and health risks to surrounding communities. The use of photocatalysts off ers a promising alternative for the controlled degradation of energetic materials. This project aims to investigate the photocatalytic effi ciency of substituted deazafl avin compounds in the degradation of nitramine surrogates and the synthetic effi ciency of deazafl avin production itself.
By utilizing surrogate compounds, we can safely model the reaction kinetics of nitramine compounds to provide insights into catalytic performance. This research will involve a rigorous comparison of experimentally derived physical properties against computational predictions of deazafl avins. The student will employ cyclic voltammetry to determine reduction potentials and conduct controlled photocatalysis experiments. The student will quantify and characterize the resulting breakdown products primarily through proton NMR and EPR spectroscopy. This work will establish a fundamental framework for developing scalable, light-driven remediation strategies for hazardous munitions waste.

Student’s role and learning objectives: 

-Develop laboratory skills in multi-step organic synthesis, focusing on improving reaction yields and purifi cation effi ciency.
-Characterize photocatalytic performance using techniques such as cyclic voltammetry, NMR, and EPR to characterize photocatalytic performance.
-Gain experience in setting up and monitoring light-driven reactions, including the management of photoreactor conditions and surrogate safety protocols.

The student will work with a lead graduate student and the faculty mentor collaboratively. The student will meet once a week for a 1-on-1 meeting with the faculty mentor, and as needed (multiple times) with the lead graduate student every week to discuss the research plan and results. The student will also have opportunities to present the findings in the group meeting and as available at the conferences.

Faculty Mentor: Shubham Vyas | Chemistry

Project Abstract: 

Per- and polyfl uoroalkyl substances (PFASs) are oleophobic and hydrophobic substances that are used in a variety of applications due to their extreme stability towards chemical oxidation, reduction and thermolysis. Due to the widespread use of PFASs for several decades, PFASs have found their way into water sources, soils, food items and aquatic life. As a result, nearly every American has PFASs in their blood. Due to their link to various cancers and chronic medical conditions, the United States Environmental Protection Agency (USEPA) has designated PFASs as hazardous waste. One promising focus into degradation of PFAS is reduction via the aqueous electron.

This research project will focus on the mechanisms of PFAS degradation via the aqueous electron, specifically studying ways to enhance the efficiency of PFAS degradation using various photochemical sensitizers for aqueous electron generation. The student will be using a high-powered laser coupled with analytical techniques including FT-IR and UV-Vis to elucidate and improve PFAS reduction mechanisms.

Student’s role and learning objectives: 

Learn to operate a high-powered laser for laser flash photolysis experiments.
Learn to prepare samples in an anoxic environment.
Utilize analytical techniques such as FT-IR and UV-Vis.
Read and use literature to guide experimental design.

Mentoring Activity: The student will meet with the PI once a week or as needed to learn scientifi c concepts needed to execute the proposed research activities, and also to get feedback on ongoing research activities along with professional development advice. The student will also meet a graduate student mentor on a weekly basis to get technical/hands-on experience to help achieve the learning objectives outlined above. The student will also present their fi ndings at least 1-2 times during the summer to the research group. At the end of the summer semester, the student will summarize the research fi ndings in a technical research report.

Faculty Mentor: Shubham Vyas | Chemistry

Project Abstract: 

Per- and polyfl uoroalkyl substances (PFASs) are one of the most recalcitrant manmade environmental contaminants, found in the blood of nearly all Americans. Due to their link to various cancers and chronic medical conditions, the United States Environmental Protection Agency (USEPA) has designated PFASs as hazardous waste. The specifi c role of metal ion complexes in PFAS degradation is unclear despite their ubiquity in the environment as a possible degradation pathway. Characterizing how PFAS molecules partition onto metal ions is essential for predicting their environmental fate and transport, as well as for designing more eff ective remediation techniques.
This project will investigate the binding affi nity of various PFAS molecules to metal ions. This research will utilize a multidimensional spectroscopic approach, gathering data from Infrared (IR), Ultraviolet-Visible (UV-Vis), Nuclear Magnetic Resonance (NMR), and Electron Paramagnetic Resonance (EPR), spectroscopy. Mass spectrometry will be used to validate titrant concentrations. To handle this information, the student will apply data science techniques to deconvolute multivariate data and estimate uncertainty through bootstrapping methods. Results from this study will provide a foundation for understanding catalytic breakdown pathways and improving environmental remediation strategies.

Student’s role and learning objectives: 

-Use advanced instrumentation, including NMR, IR, and UV-Vis spectroscopy, to characterize chemical interactions.
-Utilize multivariate data deconvolution techniques and statistical bootstrapping methods to interpret complex spectroscopic signals and quantify uncertainty.
-Evaluate the physical and chemical principles of PFAS partitioning and its practical application in designing filtration and remediation technologies.
-Perform comprehensive literature searches to contextualize findings within the current state of PFAS research and develop technical writing and scientific presentation skills for undergraduate symposia.

Mentoring Activity: The student will meet with the PI once a week or as needed to learn scientific concepts needed to execute the proposed research activities, and also to get feedback on ongoing research activities along with professional development advice. The student will also meet a graduate student mentor on a weekly basis to get technical/hands-on experience to help achieve the learning objectives outlined above. The student will also present their findings at least 1-2 times during the summer to the research group. At the end of the summer semester, the student will summarize the research findings in a technical research report.

Faculty Mentor: Annalise Maughan | Chemistry

Project Abstract: 

All-solid-state batteries promise safer operation and higher energy densities by enabling lithium metal anodes. However, lithium anodes remain a distinct challenge, due to the uneven deposition of lithium metal that results in dendrites. This project will investigate how particle size distribution influences packing density, microstructure, and electrochemical behavior in the sulfide solid electrolyte Li6PS5Cl. Through microstructural and electrochemical characterization, this project will determine how the distribution of particle sizes impacts the resistance of the solid-state electrolyte to lithium dendrite growth.

Student’s role and learning objectives: 

The student will prepare samples with varying distributions of particle sizes, including unimodal, bimodal, and trimodal particle size distributions. Students will measure relative density, ionic conductivity via electrochemical impedance spectroscopy, and dendrite resistance through critical current density cycling. This work will provide hands-on experience in powder processing, microstructural characterization, and electrochemical testing while contributing to improved solid-state battery performance.

Faculty Mentor: Ryan Richards | Chemistry

Project Abstract: 

High entropy metal oxides are an exciting new class of materials. However, their synthesis in the form of nanostructured materials remains challenging. This project will focus on the synthesis of HEOs and utilizing supercritical solvents and templates to control structure.

Student’s role and learning objectives: 

– inorganic synthesis and templating
– characterization of crystalline solids
– science communication
– presentations skills
– organization and planning

Faculty Mentor: Matthew Posewitz | Chemistry

Project Abstract: 

Research is focused on characterizing photosynthetic properties in organisms adapted to seawater, acid mine drainage and other extreme environments. Research will characterize electron transport mechanisms, survival strategies and genetic engineering to improve strain performance.

Student’s role and learning objectives: 

Students will work daily with to conduct experiments focused on biomass productivities, strain engineering and cell characterization.

Faculty Mentor: Lori Tunstall| Civil and Environmental Engineering

Project Abstract: 

In an effort to reduce the carbon emissions from both the concrete industry and landfills, this project explores adding biochar from municipal solid waste (MSW) into concrete. Biochar is a stable, solid form of carbon and the addition of biochar to concrete creates a long-term carbon storage solution. This project focuses on measuring and adjusting the physical and chemical parameters of MSW biochar to optimize integration with concrete. The student will connect these material characteristics with concrete mixing and strength testing to see which key material characteristics influence final concrete performance. This work is supported by a grant focused on scaling university research into the market, combining lab-based measurements with hands-on concrete batching and testing. The integration of biochar into concrete is gaining traction in the construction industry and the work done on this project will improve our understanding of how to effectively store carbon from landfill waste in the built environment.

Student’s role and learning objectives: 

This project supports ongoing research on the addition of biochar into concrete, so the student will have fellow researchers available for collaboration and extra mentoring. They will build skills in safely and thoroughly analyzing biochar characteristics through hands-on wet chemistry and concrete sample preparation. Common work involves: biochar particle size analysis, leachability studies, concrete batching, compression testing, and data analysis. Communication of data is a key learning objective emphasized by weekly update presentations, data visualization practice, and short technical report writing.

Faculty Mentor: Yangming Shi | Civil and Environmental Engineering

Project Abstract: 

This project aims to investigate how to effectively “upskill” current automotive technicians, helping them smoothly transition into the future EV service industry. By exploring innovative training models and Virtual Reality technology, we hope to identify the best practices for lowering the barrier to entry and improving training efficiency for these workers.

Student’s role and learning objectives: 

Student’s Role:
The undergraduate student will have a multifaceted role encompassing technical development, data analysis, and project coordination. Specifically, their responsibilities will include:

1. VR System Development: Assisting in the design, prototyping, and programming of interactive Virtual Reality (VR) training modules aimed at simulating electric vehicle (EV) maintenance and safety procedures.

2. Video Data Annotation: Reviewing and coding video recordings of auto repair workflows, mechanic behaviors, or existing training sessions. This data will be used to identify current skill gaps and inform the design of our training tools.

3. Workshop Organization: Supporting the logistical planning, material preparation, and execution of interactive workshops involving key stakeholders (e.g., automotive workers, educators, and industry partners).

Student Learning Objectives:
By the end of the summer project, the student will be expected to achieve the following learning objectives:

1. Technical Proficiency: Gain practical, hands-on experience in VR software development (e.g., using Unity or Unreal Engine) and user-centered design for educational technology.

2. Analytical Skills: Master observational research methods and video data annotation techniques to extract meaningful insights from real-world human behavior.

3. Professional & Communication Skills: Develop strong event management, teamwork, and communication skills by coordinating and participating in stakeholder workshops.

4. Domain Knowledge: Acquire a deeper understanding of human-computer interaction (HCI), workforce development, and the socio-technical challenges of the emerging EV industry.

Mentoring Activities:
To ensure a productive and enriching experience, I will provide structured mentorship tailored to the student’s needs, including:

1. Regular Meetings: We will hold weekly meetings to review progress, troubleshoot technical hurdles, set actionable goals, and discuss broader research concepts.

2. Targeted Technical Guidance: I will provide hands-on tutorials, initial code reviews, and specific resources for VR development and video annotation software to ensure the student feels supported in their technical tasks.

3. Professional Development: The student will be integrated into our broader research group, attending regular lab meetings. I will guide them on professional communication during the stakeholder workshops and provide opportunities to present their work, offering constructive feedback to build their confidence as a researcher.

Faculty Mentor: Christopher Bellona | Civil and Environmental Engineering

Project Abstract: 

Title: Sorption-Based Technologies for the Sustainable Remediation of Per- and Polyfluoroalkyl Substances in Surface Water Resources

Lead Principal Investigator: Christopher Bellona, Ph.D.

1. Introduction
Per- and polyfluoroalkyl substances (PFASs) are highly persistent fluorinated aliphatic compounds that resist environmental degradation and tend to bioaccumulate in living organisms. Among these compounds, perfluoroalkyl acids (PFAAs), including perfluoroalkyl carboxylates (PFCAs) and perfluoroalkyl sulfonates (PFSAs), have attracted significant attention due to their widespread occurrence in aquatic environments and potential adverse health effects. Aqueous film-forming foam (AFFF) used in firefighting activities has been identified as a major source of PFAS contamination in water systems. While the removal of PFAS from groundwater is fairly well understood, there is a need to evaluate adsorbents for the removal of PFAS from surface water.
Due to the strong carbon–fluorine bonds in PFAS molecules, conventional water treatment processes are often ineffective for their removal. Sorption-based technologies, particularly granular activated carbon (GAC), anion exchange resin (AER), and Fluorosorb (FS), have emerged as promising approaches for PFAS remediation. GAC has been widely implemented in water treatment facilities because of its relatively low cost and operational simplicity. Anion exchange resins generally exhibit higher removal efficiencies for a broad range of PFAS compounds. Surface-modified organo-clay adsorbents such as FS have been developed to enhance PFAS affinity through hydrophobic and electrostatic interactions.

2. Objective
The overall objective of this study is to evaluate and compare the performance of sorption-based treatment technologies for the removal of selected PFAAs from surface water impacted by AFFF. This study contributes to improving treatment strategies for PFAS-contaminated surface water and the results of this study will provide insights into the effectiveness of emerging sorbent materials and help identify optimal treatment strategies for PFAS removal from AFFF-impacted surface water systems. The specific objectives of this study are to:
2.1. Evaluate and compare the removal efficiency of three sorbent materials, granular activated carbon, anion exchange resin, and Fluorosorb, for selected PFAAs in contaminated surface water.
2.2. Characterize the breakthrough behavior of different PFAAs during column operation and determine the relative adsorption capacity of each sorbent.
2.3. Assess competitive adsorption effects between PFAS and naturally occurring organic constituents present in surface water.
2.4. Examine the operational stability of the sorbent media, including media exhaustion and the need for replacement during continuous treatment.
2.5. Compare the overall performance and potential advantages of different sorbent configurations, including individual sorbents and potential hybrid systems, for improving PFAS removal efficiency and treatment sustainability.

3. Methodology
Rapid small-scale column tests (RSSCTs) will be employed to simulate full-scale adsorption processes under controlled laboratory conditions. RSSCT methodology allows for accelerated evaluation of sorbent performance while maintaining representative mass transfer and adsorption behavior comparable to full-scale treatment systems. Surface water samples collected from AFFF-impacted sites will be used as influent to represent realistic water quality conditions. Prior to experimentation, key water quality parameters, including pH, alkalinity, conductivity, and major ions, will be characterized to assess their potential influence on adsorption performance.
3.1. Column Experiments
Laboratory-scale columns will be packed separately with GAC, AER, and FS sorbents. The columns will be operated under continuous flow conditions designed according to RSSCT scaling relationships to simulate full-scale empty bed contact times (EBCTs). AFFF-impacted surface water will be continuously fed through the columns, and operational parameters such as flow rate and pressure will be carefully controlled to ensure consistent experimental conditions across all sorbents.
3.2. Sample Collection and Chemical Analysis
Influent and effluent samples will be collected periodically throughout the experiments to monitor treatment performance over time. PFAS concentrations, including selected PFAAs, will be quantified using LC-MS/MS.
3.3. Breakthrough and Performance Evaluation
Breakthrough curves will be generated for each sorbent by plotting normalized PFAS concentrations (C/Co) as a function of bed volumes treated. These curves will be used to
determine metrics for adsorbent comparison including media usage rates and annual adsorbent costs for a hypothetical treatment scenario.

4. References
Cheng, L., & Knappe, D. R. U. Removal of per- and polyfluoroalkyl substances by anion exchange resins: Scale-up of rapid small-scale column test data. Water Research, (2024), 249, 120956
Murray, C., Vatankhah, H., Marshall, R., Liu, C., Bellona, C. PFAS treatment with granular activated carbon and ion exchange resin: Comparing chain length, empty bed contact time, and cost, Water Process Engineering (2021), 44, 102342.
Zeng, C., Atkinson, A., Sharma, N., Ashani, H., Hjelmstad, A., Venkatesh, K., & Westerhoff, P. Removing per- and polyfluoroalkyl substances from groundwaters using activated carbon and ion exchange resin packed columns. AWWA Water Science, (2020), 2(1), e1172.
Zhang, C., Yan, H., Li, F., Hu, X., & Zhou, Q. Sorption of short- and long-chain per- and polyfluoroalkyl substances by carbonaceous materials and ion exchange resins. Chemosphere, (2019), 221, 206–214.

Student’s role and learning objectives: 

She will gain hands-on experience with sorption-based technologies through rapid small-scale column tests (RSSCTs) and will develop knowledge of water treatment and PFAS removal using various adsorbents. She will be conducting RSSCTs and preparing samples for PFAS analysis.

I am guiding her in preparing the columns for RSSCTs, preparing samples for PFAS analysis, setting up the RSSCT experiments, and collecting samples during the RSSCT process.

Faculty Mentor: Tom Williams | Computer Science

Project Abstract: 

When designing robots’ nonverbal behaviors, many researchers have turned to arts-based insights, such as Disney’s Animation Principles. Yet while these principles bear key insights into the design of like-life characters, their application to robot design is inherently limited, in part because animation is not constrained by real-world physics, and in part because animation principles focus on low level animation mechanics and not high-level design considerations for physically embodied, interactive characters. In contrast, little attention has been paid to art forms like puppetry, despite their long history of exploring morphological, behavior, and interaction design of physically embodied, interactive characters. As such, in this project we have thus far leveraged puppetry texts and practicing puppeteers’ expert knowledge knowledge to derive a set of puppetry principles with key insights for robot design. As we show, these insights go beyond — and uniquely complement — the prior insights provided by theater, dance, and animation.

The student joining this project for summer 2026 will help us to design, run, and analyze experiments to help test puppetry principles.

For example: when designing effective human-robot interactions, researchers have utilized gestures, head nodding, and turn taking cues. Although these movements are vital for these interactions, there is minimal research on the effects of robot stillness. In this work, we explore how a robot’s level of movement affects how people perceive its attentiveness, naturalness, and perceived thought when in human-robot conversations. We hypothesize that when a robot is listening its movement will create an illusion of life, while stillness will communicate attentiveness and the most effective model will utilize both. As such, we aim to run and analyze the results of an experiment to test this hypothesis.

Student’s role and learning objectives: 

Undergraduates will gain experience with study design, execution, analysis, and writing, with mentorship from Dr. Williams and from MIRRORLab graduate students. In addition to research activities, students will attend and present at weekly lab meetings, and attend a weekly seminar series featuring speakers from around the world.

Faculty Mentor: Tom Williams | Computer Science

Project Abstract: 

While cognitive science has typically focused on animal cognition and especially human cognition, researchers have recently begun to explore, the ways that mycelial networks might also be understood as cognitive systems. Mycelial networks represent a salient candidate system for investigating a neural cognition, and as such, a cognitive account of mycelial networks would stand to both expand the bounds of cognitive science and would present practical opportunities to advance key industries such as forestry and agriculture. Two key theories of mycelial network cognition have been proposed, with some scientists arguing that mycelial network cognition is purely chemical, and others arguing that mycelial network cognition has an electrochemical basis just as in plants and animals. To argue in favor of the electrochemical theory of mycelial network, cognition, scientists have produced evidence of complex electrophysiological dynamics at both the macro scale, that is at the cellular level, and at the microscale, that is at the something something level. At the micro scale further evidence has shown that these electrophysiological dynamics represent action potential like behavior. However, it is unclear whether macroscale electrophysiological dynamics are also of biological significance. In this project, we are investigating how macroscale electrophysiological dynamics can be modeled in order to better understand mycelial network cognition.

The student joining this project for summer 2026 will help us to create and analyze computational models of mycelial cognition and/or to design art installations that help to visualize those models, depending on student interest and expertise.

Student’s role and learning objectives: 

Undergraduates will gain experience with computational cognitive modeling, presenting, and writing, with mentorship from Dr. Williams and from MIRRORLab graduate students. In addition to research activities, students will attend and present at weekly lab meetings, and attend a weekly seminar series featuring speakers from around the world.

Faculty Mentor: Kaveh Fathian | Computer Science

Project Abstract: 

This project aims to build a modular “sensor backpack” platform that can be mounted on mobile robots to enable real-time 3D mapping of complex environments. The backpack will integrate multiple sensing modalities (e.g., cameras, laser scanners, IMUs), onboard high-performance computing, power management, and battery systems into a lightweight, self-contained unit. The system must be:
– Lightweight and compact, minimizing mass and volume
– Modular and robot-agnostic, enabling mounting on different robotic platforms
– Energy-efficient, maximizing mission duration
– Expandable, allowing for future sensing, autonomy, and remote operation capabilities
Students will design, prototype, manufacture, and assemble both the mechanical and electrical subsystems. The final deliverable will be a fully functional sensor platform that supports high-quality 3D mapping and serves as a reusable research infrastructure for robotics projects.

Student’s role and learning objectives: 

Students participating in this project will gain hands-on experience in robotics systems integration, hardware development, and interdisciplinary collaboration. By the end of the project, students will be able to:
– Design an integrated robotic sensing platform that balances mechanical constraints, power consumption, and sensing requirements
– Apply mechanical design principles to minimize mass and volume while maintaining structural integrity and serviceability
– Design and implement power management and battery telemetry systems, including mission-time estimation and safety considerations
– Integrate multi-sensor hardware systems (e.g., cameras, LiDAR) into a cohesive and modular architecture
– Document system architecture and create professional-grade build and operation documentation to ensure reproducibility and future scalability
– Collaborate across disciplines (ME/EE/CS) to translate high-level system requirements into integrated hardware solutions
– Lead and mentor high school summer interns, developing the ability to teach technical concepts and supervise hands-on work

Faculty Mentor: Micah Corah | Computer Science

Project Abstract: 

Camera-equipped with drones have become common in areas like sports, filming, travel, and search and rescue. This project focuses on developing autonomy for drone filming and questions of how operators should interact with these systems. For example, an operator might provide verbal or written instructions. The goal of this project will be to investigate how to enable aerial robots to observe and film single or multiple moving actors via natural language instruction from a human operator. Current autonomy systems for Unmanned Aerial Vehicle (UAV) lack domain-specific knowledge for cinematography or require a user to manually specify
parameters to capture the desired shots. The student working on this project should expect to have the opportunity to work with both real and simulated drone systems. For this purpose, NAPPLab has access to human-safe Crazyflie micro-drones, a motion capture system, and indoor flight area that will be available for research and experimentation. The exact parameters of this project may vary, and students may emphasize either technical aspects of system development or aesthetic and artistic aspects of drone filming.

Student’s role and learning objectives: 

The student engaging in this project will be provided with an aerial or ground robot and appropriate camera and compute resources to complete this task.

Through the course of this project the student may:
* Familiarize theirself with open source robotics tools such as based on the Robot Operating System ecosystem and demonstrate their operation on said mobile robot
* Learn about common models for camera intrinsics and extrinsics and apply that knowledge to camera calibration and reasoning about camera views in planning and control
* Study methods for navigation and control for mobile and aerial robots, especially methods for active perception and apply this knowledge for the purpose of this project
* Demonstrate function of the complete robot system in a laboratory environment via a cinematography task of the students’ design

Faculty Mentor: Micah Corah | Computer Science

Project Abstract: 

The ability to explore and map an unknown environment is an important prerequisite for many tasks in robotics. From mapping a house to long term operations at an industrial site or warehouse, exploring the environment can be an important first step before continued operation. For the purpose of exploration and inspection, the student may expect to learn about classical methods such as frontier exploration and more advanced concepts such as information gain, environment semantics, or methods for 3D mapping and reconstruction. Through the course of this project, the student will gain experience with common software, hardware, and sensors in robotics domains while also applying methods for path planning, mapping, and control. We also plan to emphasize integration and application on a physical robot, likely a Clearpath Jackal, which will be equipped with sensors such as multi-plane lidar, RGBD camera(s), and a PTZ camera. Depending on the preferences of the student and availability of time, a secondary task will be to implement methods to search the environment for a specified object or navigate to a goal specified by natural language.

Student’s role and learning objectives: 

The student engaging in this project will be provided with an mobile robot equipped with sensors such as a depth camera, lidar sensor, and PTZ camera and compute resources to complete this task.

Through the course of this project the student will:
* Study methods for navigation and control for mobile robots, especially methods for active perception and exploration, and apply this knowledge to their own implementation for this search and exploration task
* Work directly with a physical sensor-equipped robot gain experience with tools, controllers, and packages related to that robot’s operation and address challenges related to operating a real robot in a physical environment
* Demonstrate and evaluate navigation, search, and exploration methods in a laboratory environment
* Evaluate the implementation in physical and/or simulation experiments both in terms of direct measures of performance such as cells observed, time to completion, or frequency in which the robot gets stuck or fails at a task

Faculty Mentor: Estelle Smith | Computer Science

Project Abstract: 

The research lab of Professor C. Estelle Smith (https://estellesmithphd.com/) in the Department of Computer Science is seeking an excellent undergraduate researcher for a 2026 SURF project focused on analyzing public discussions of spiritual struggle and support on Reddit. Many people facing mental or physical health challenges seek care not only in clinical settings, but also in online communities where they can anonymously share experiences and receive support. On Reddit, communities related to depression, anxiety, suicide, PTSD, cancer, and other life challenges often include spiritual language and spiritual support practices (e.g., prayer, meaning-making, coping, hope, and compassionate presence). This SURF project will frame two central questions. First, when and how do support threads help the original poster experience positive change, such as emotional movement (e.g., despair to hope), increased self-agency, or reframing harmful beliefs? Second, which early linguistic and emotional cues in the initial post and early replies are associated with (a) engagement trajectories (e.g., depth and volume of discussion) and (b) later signals that the original poster returns and shows improvement. Across both questions, we will examine patterns in how support unfolds over time, including the kinds of replies that appear early in a thread and how the original poster’s language changes in follow-up posts or comments.

As a key member of an interdisciplinary research group, you will be embedded in the only Human-Computer Interaction (HCI) research lab at Mines. Throughout your work, you will be mentored by a research team consisting of Prof. Estelle Smith and her PhD students Shadi Nourriz and Jesan Ahammed Ovi, all in CS@Mines. This is an exciting opportunity to work on data science for good and contribute to a new research area in digital spiritual care. Learn more about spiritual care by reading this preprint of an accepted CHI 2026 conference paper: https://arxiv.org/abs/2601.14435.

***Required Qualifications:***
Strong interest in Data Science, Human-Computer Interaction, Social Computing
Strong interest in language-focused analytical methods (e.g., emotion, tone, help-seeking, empowerment/agency language) and how it relates to engagement in online discussions.
Currently enrolled as an undergraduate student, preferably in Data Science; Computer Science; Applied Mathematics and Statistics; Engineering, Design and Society; or related areas. 
Excellent project management skills, attention to detail, and interpersonal sensitivity.
Self-motivated and able to work independently, as well as to work collaboratively in a team environment.
Prior experience using APIs, big data, and/or databases
Prior training in statistics and/or data science coursework

***Preferred Qualifications:*** 
Prior experience as a Reddit user
Prior training in natural language processing and/or machine learning coursework
Prior exposure to research ethics and CITI training

Student’s role and learning objectives: 

***Roles:***
Conduct literature review to study and learn advanced data science techniques.
Gain mastery of the public Reddit API and learn best practices for collecting, cleaning, and structuring thread-level data (original post, comments, timestamps, and follow-up posts when available).
Collaborate closely with the research team to maintain documentation, ensure careful interpretation of findings, and communicate results clearly in research write-ups and presentations.
Actively participate in team meetings and maintain professionalism and integrity.

***Learning objectives:*** 
Data Collection and Analysis: Learn how to collect, process, and analyze behavioral trace data on Reddit using statistics and software tools to extract meaningful insights and trends.
Project Collaboration: Work collaboratively with a multidisciplinary project team to develop strategies, conduct research, and deliver final project outcomes, gaining experience in teamwork and project management. 
Research Ethics: Gain an understanding of research ethics and best practices in data collection, analysis, and reporting, ensuring compliance with ethical guidelines and standards, including CITI training.
Contribute to writing up research results in scientific publications.

***Mentoring Activities:***
Our lab will regularly be meeting in-person on campus this summer. The student will have weekly 1:1 mentoring sessions with PhD students and/or Prof. Estelle Smith. The student will also be invited to participate in lab meetings and social activities during the summer. Upon successful submission of papers resulting from this research, students may possibly have the opportunity to attend conferences and present papers or posters, especially if they are able to make contributions meriting first authorship. Our goal is to provide an excellent opportunity to learn about research in Spiritual Care and Human-Computer Interaction. We will provide career guidance, mentorship, support, and networking opportunities, with the potential to help initiate a successful career in this direction.

Faculty Mentor: Sihua Shao | Electrical Engineering

Project Abstract: 

Urban stormwater filtration systems are widely used to prevent debris and pollutants from entering waterways, but maintaining these systems is challenging because there is currently no easy way to know when filtration bags are full and need replacement. This project aims to develop a low-power, smart sensing system that can automatically monitor the fullness of stormwater filtration bags and report this information remotely.

The waterproof system uses a small ultrasonic sensor mounted above the filtration bag to measure how much material has accumulated inside. A compact electronics module processes this information and wirelessly uploads the data to a cloud server, allowing maintenance teams to check the status of the system on demand rather than relying on fixed inspection schedules. This approach can reduce unnecessary site visits, improve maintenance efficiency, and help prevent overflow or system failure during heavy rainfall events.

Through this project, an undergraduate student will contribute to the development, testing, and evaluation of a real-world sensing system that integrates hardware, software, and wireless communication. The work supports sustainable infrastructure management and builds practical engineering solutions for environmental monitoring and smart city applications.

Student’s role and learning objectives: 

Student Role

The undergraduate student will play an active role in the design, implementation, and evaluation of the stormwater filtration monitoring system. Under faculty mentorship, the student will:
– Program a low-power microcontroller to collect data from an ultrasonic sensor and manage system operation.
– Interface the sensing unit with cellular communication modules and implement data transmission to a cloud platform.
– Compare the performance and power consumption trade-offs of two low-power cellular technologies (LTE-M and NB-IoT).
– Measure system power consumption under different duty-cycling strategies and data upload intervals.
– Conduct laboratory testing of the prototype under controlled temperature conditions.
– Assist with system assembly, integration, and preparation for field testing.
The student will document experimental results, analyze system performance, and participate in regular project meetings to discuss progress and design decisions.

Student Learning Objectives

By the end of the project, the student will:
– Gain hands-on experience with embedded systems, including sensors, microcontrollers, and low-power design.
– Develop practical skills in wireless communication and data transmission for Internet-of-Things (IoT) applications.
– Learn how engineering trade-offs (power, performance, reliability) influence system design in real-world deployments.
– Build confidence in experimental testing, data analysis, and prototype evaluation.
– Strengthen technical communication skills through written documentation and oral progress updates.
– Experience mentored research in an applied engineering context, preparing them for graduate study or industry careers.

Mentoring Activities

The faculty mentor will provide structured guidance through weekly meetings, design reviews, and milestone-based feedback. The student will receive training in laboratory safety, experimental methods, and professional research practices. Emphasis will be placed on independent problem-solving, ethical research conduct, and reflection on the broader societal impact of engineering solutions for environmental sustainability.

Faculty Mentor: Mike Wakin | Electrical Engineering

Project Abstract: 

As part of a NASA project, our group is working with Prof. Yamuna Phal to develop a mid-infrared microscope with space flight capability. This microscope uses a technique called computational imaging, where the system can be configured to take various types of numerical measurements, which must then be processed by a computer to create an image. The SURF student will help our team find the optimal set of measurement configurations for creating the best images.

Our group is also actively engaged in K-12 outreach through the STEM Kits program. STEM Kits are themed outreach kits we have developed in coordination with Scouting Colorado. STEM Kits have three themes: Sensing Circuits; Machines Lend a Hand; and Earth, Energy, and Environment. The kits are distributed for free to local schools and youth organizations, and they are also presented through outreach events organized by Mines students and Scouting staff. The SURF student will help our team with outreach events and continuous improvement of the STEM Kits program.

Student’s role and learning objectives: 

For the microscopy system, the SURF student will learn about the capabilities of the imaging system, implement various measurement patterns in software, and use numerical tests to evaluate the effectiveness of the patterns. Such tests may involve optimization algorithms for image reconstruction, sparse signal processing, and/or AI-based pattern generation. The student will also have the opportunity to interact with other students in the lab who are doing hardware configuration. Participation will help the student develop their knowledge and skills in signal processing and computational imaging.

For the STEM Kits outreach, the SURF student’s activities may include delivering outreach lessons at Scouting summer camps, refining online instructions and videos, and brainstorming new activities that can be performed with the kit components. Participation will help the student refine their oral and written communication skills, teaching/outreach skills, and project planning skills.

Preferred qualifications include basic experience with Python (or Matlab) programming, an introductory signal processing course (such as EENG310), prior experience with outreach/teaching/tutoring, and/or a willingness to learn.

Faculty Mentor: Yamuna Phal | Electrical Engineering

Project Abstract: 

This project aims to develop a mid-infrared (mid-IR) imaging platform to analyze chiral molecular interactions with high spatial resolution. These interactions play a crucial role in understanding biomolecular mechanisms, such as protein folding, which are relevant to neurodegenerative diseases and drug design. The goals are scientifically compelling as they bridge cutting-edge spectroscopy techniques with practical biomedical applications, pushing the boundaries of imaging technology in the mid-IR spectrum.

Student’s role and learning objectives: 

This project integrates concepts from electrical engineering, biomedical imaging, chemistry, and materials science. It involves designing optical systems (engineering), studying molecular interactions (chemistry/biochemistry), and employing advanced computational techniques for data analysis (computer science). The interdisciplinary approach allows for tackling complex challenges that cannot be solved within a single field.

Possible skills/techniques gained by the student are as follows —
• Hands-on experience with optical system design and alignment.
• Knowledge of mid-IR spectroscopy and imaging principles.
• Data acquisition and processing skills.
• Programming skills for automation and data analysis.
• Scientific communication and teamwork in a collaborative research environment.

The student(s) will receive one-on-one mentorship from graduate students and weekly check-ins with the principal investigator (Phal). They will attend lab meetings to discuss their progress and present their findings. Training will be provided for all necessary techniques, and feedback will be given regularly to support their academic and professional growth. Clear milestones will be set to ensure timely progress, with flexibility to adapt as needed.

Faculty Mentor: Zane Jobe| Geology and Geological Engineering

Project Abstract: 

The larger project focuses on integrating analog (old) and digital (new) data from outcrop in the Guadalupe Mountains, Texas to improve predictions of rock properties and geological features for energy applications. Through the years, we have generated extensive legacy datasets, including graphic logs, interpreted photographs, GPS coordinates, drone imagery, LiDAR point clouds, and associated geological interpretations.

The undergraduate student will organize and catalog these legacy files, create a comprehensive file management system, and develop GIS databases that spatially reference all project data. This work is essential for making the data accessible for ongoing research and applicability in energy-resource development, carbon storage, and geothermal systems. The student will gain valuable experience in geospatial data management, GIS software applications, Python programming for data automation, and digital data organization—skills highly relevant to careers in energy geoscience, where managing large, multi-modal datasets is fundamental to understanding subsurface geology and resource development.

Student’s role and learning objectives: 

The student will work directly with the principal investigator to inventory, organize, and catalog all legacy files associated with the Guadalupe Mountains project. Specific activities will include creating a logical file directory structure, developing metadata standards, georeferencing field data, and establishing data management protocols for future field work. The student will learn QGIS for geospatial analysis and use Python to help automate file organization and data processing tasks. Mentoring activities will include weekly meetings to discuss progress and challenges, hands-on training in GIS software and data management best practices, guidance on creating effective metadata and documentation, and opportunities to learn about the broader project goals and their applications to energy geoscience. The student will develop professional skills in data management that are directly applicable to careers in almost any sector, where scientists and engineers routinely work with complex, multi-source datasets.

Faculty Mentor: Piret Plink-Bjorklund | Geology and Geological Engineering

Project Abstract: 

River floods are among the most common and destructive natural hazards, with significant societal impacts. However, their role in shaping landscapes and contributing to the sedimentary record remains poorly understood. Traditionally, rivers are viewed as relatively stable systems where moderate floods drive most changes. We propose that while this holds true for some rivers, in others, extreme floods play a defining role—reshaping channels and triggering cascading flood effects.
This project takes a fresh approach to studying river floods by analyzing global river discharge patterns. By leveraging the untapped information in river discharge time series – in hydrograph shapes, we will move beyond traditional statistical indices to gain new insights.

Student’s role and learning objectives: 

The undergraduate researcher will assist in global river discharge data analyses in close collaboration with faculty. We will together develop a research plan and decide on expected outcomes. The student will learn how to set up and conduct research projects, including scientific questions and testable hypothesis, how to plan and conduct work that ensures results, and how to disseminate the results by conference presentations (Mines and well as national conferences) or publications.
The student will learn about statistical methods for time-series analyses and their geoscience applications. The student will also learn about rivers globally, and how distinct discharge patterns shape landscapes and influence flood hazards.
We prefer students familiar with using Python, R, or MATLAB for data analysis, and with experience with applied statistics or machine learning (MATH324, CSCI303, or equivalent). These skills are, however, not required.

Faculty Mentor: Yvette Kuiper | Geology and Geological Engineering

Project Abstract: 

I will need someone to help two graduate students map parts of the Loveland Pass and Mount Logan 7.5’ quadrangles. The undergrad will have an opportunity to do their own project within the larger project. The focus of that project can be somewhat flexible and depends on student interests. We will be mapping Proterozoic to Paleogene bedrock, ductile and brittle structures, and surficial deposits (e.g., glacial, landslides). The student must be fit to hike mountainous terrain at elevations between ~9,500 and ~13,500 feet. Students with hiking and backpacking experience are preferred. Mapping and GIS experience are a plus also, but not required.

Student’s role and learning objectives: 

The student will gain invaluable mapping experience. As stated above, there is a bit of flexibility in what the undergrad student would want to focus on. Examples of past projects include surficial deposit mapping, and fault/joint mapping and structural analysis. One could also focus on specific rock types, structures, or metamorphic or igneous characteristics. Ideally the student would present at our undergraduate student research fair in February, in addition to the University-wide Undergraduate Research conference. The student would receive extensive mentoring by myself and my graduate students. Especially while in the field (typically about 4 days a week during the summer), they would work directly with a graduate student at all times, and with me when I join. In addition, I typically set up meetings in the office throughout the summer with the student(s) as needed and as the field schedule allows.

Faculty Mentor: Aaron Girard | Geophysics

Project Abstract: 

Kīlauea volcano is one of the most studied volcanoes on the planet, and is also one of the most instrumented. In May and June of 2023 the USGS, in collaboration with several universities, acquired an active- and passive-seismic continuously recorded nodal survey with the goal of building a 3D model of the subsurface structure to better understand the magma flow. Coincidentally, immediately after the active-source survey was completed, the seismic array recorded over 40,000 earthquakes and the eruption of June 7, 2023. Work has already been done to build P-wave velocity models from the active-source seismic data, picking the earthquake arrivals for all of the ~1800 receivers, and using interferometry to identify surface-wave energy. In this project a student will develop a workflow to use this data set to identify the sources of the earthquakes and make an updated velocity model of the Kīlauea caldera. This will have large impacts on the understanding of the Kīlauea magma flow processes, aid in interpreting non-seismic data sets of the area, and be valuable for any future similar studies on other volcanoes.

Student’s role and learning objectives: 

This project requires a student who is interested in combining geophysics and computer science to analyze continuous seismic data from the June 2023 Kīlauea eruption and develop a surface-wave velocity model. The student should have experience with Python programming and an interest in learning about seismology and volcanology. The student will work closely with Dr. Aaron Girard and collaborate with researchers from the USGS. Through this project, the student will gain experience in data analysis, seismic model development, and scientific communication.

During this project, the student will:
– Analyze large seismic datasets using open-source Python tools
– Identify and classify seismic wave types within continuous recordings
– Apply geophysical principles to build surface-wave velocity models
– Use GitHub for collaborative coding and project organization
– Practice presenting research results to scientific and student audiences

The student will be co-mentored by Dr. Aaron Girard and Dr. Jeffrey Shragge, with whom they will meet weekly to discuss progress and receive guidance on research development and career goals.

By the end of the project, the student is expected to:
– Make significant progress toward generating a preliminary surface-wave velocity model for the Kīlauea eruption dataset
– Demonstrate improved skills in seismic data processing and computational analysis
– Gain foundational knowledge in seismology and volcanology methods
– Communicate research findings through presentations and written reports

Depending on progress and interest, the student may have opportunities to present at scientific conferences and contribute to future publications.

Faculty Mentor: Ryan Shìjié Dù | Geophysics

Project Abstract: 

Deep inside the ocean, there are large underwater waves called internal solitons. They can travel long distances, even keep their shape after bumping into other waves. Although these waves move below the surface, they also affect what happens at the top of the ocean. When an internal soliton passes by, it can change how large the surface waves are, creating streaks or patches that sailors call tidal rips, which can be dangerous for ships and offshore operations. But they also allow satellite observations (see examples compiled at internalwaveatlas.com). A new satellite called SWOT (Surface Water and Ocean Topography) measures the ocean surface with high resolution. This allows us to:
• Detect where internal solitons are
• Measure their size and shape
• Observe how they disturb surface waves
• Study how the two types of waves interact
By combining SWOT’s measurements of sea surface height and surface wave energy, we can visualize both kinds of waves at the same time and build models to understand how they influence each other.

Student’s role and learning objectives: 

We are looking for a student interested in ocean physics, mathematical modeling, and remote sensing. You’ll work with the Mines Oceanography group and receive training in:
• Foundational knowledge in physical oceanography, including the physics of internal and surface waves
• Data analysis skills and knowledge of ocean applications of satellite remote sensing
• Mathematical and numerical modeling of physical processes in fluids.
• Scientific computing: version control, documentation, and code organization
• Collaborative research: using GitHub, communicating results, and open science practices

You will meet weekly with Dr. Ryan Shìjié Dù for scientific guidance and feedback. Depending on progress and your interests, the project may lead to a conference presentation or a research publication.

Required experience: To succeed in this project on this short time scale, the student should have experience with Python programming, differential equations, and basic statistics. Previous knowledge of physical oceanography is not required.

Faculty Mentor: Guilherme Castelão | Geophysics

Project Abstract: 

Ocean wave models are essential tools for predicting coastal hazards, assessing wave energy resources, and understanding how waves interact with ocean currents and extreme storms. Before these models can run, they require carefully designed computational grids that describe how the ocean is represented in space. Many grid-generation workflows today rely on legacy MATLAB scripts, which can be difficult to maintain, hard to reproduce, and require a paid software license.

The goal of this SURF project is to develop a modern, open-source grid-generation package for the widely used wave model WaveWatch III, written in the Rust programming language. The software will generate structured computational grids and export them in formats compatible with WaveWatch III, providing a transparent and reproducible alternative to existing workflows. Rust is a fast-growing systems programming language that combines high performance with strong safety guarantees, making it increasingly popular in both industry and scientific computing. Its emphasis on memory safety, reliability, and maintainability makes it well suited for building robust scientific software that can be used and extended by a broad community.

By modernizing how wave model grids are created, this project will support applications spanning renewable wave energy, wave–current interaction studies, and simulations of extreme weather. The resulting tool will be relevant to users in academia, national laboratories, and industry, and will lower the barrier to setting up reliable wave model simulations.

Student’s role and learning objectives: 

We’re seeking a student with a strong interest in software development and computer science who is excited to apply computing skills to a real-world geoscience problem. As part of this project, you will work closely with researchers from the National Laboratory of the Rockies and the Mines Oceanography research group, and receive training in scientific software design and computational modeling workflows. By the end of the project, the student will have:

– Developed or strengthened coding skills in the Rust programming language
– Gained experience in best practices of scientific computing and software development, including version control, packaging, unit testing, and documentation
– Developed knowledge of physical oceanography and how numerical models represent the ocean
– Practiced collaborative software development, open science, and project management through GitHub

The student will be primarily mentored by Dr. Guilherme Castelão, with co-mentorship from Dr. Bia Villas Bôas. Mentorship will include weekly meetings and regular feedback on project progress. Depending on the student’s progress and interest, there is potential for submitting the resulting software to the Journal of Open Source Software (JOSS), contributing the tool to the WaveWatch III user community, and presenting results at scientific conferences.

To succeed in this project on this short time scale, the student should have prior experience with programming and a strong interest in software development. Some familiarity with Rust is desirable, but not required. We welcome students who are motivated to learn a new language and build expertise in Rust, a skill that is increasingly valuable for careers in software engineering, scientific computing, and high-performance computing. Prior knowledge of physical oceanography or wave modeling is not required.

Faculty Mentor: Bia Villas Boas| Geophysics

Project Abstract: 

Ocean wave models such as WaveWatch III are widely used to support coastal hazard prediction, renewable energy planning, and research on extreme weather. A key challenge in using these models is efficiently analyzing large volumes of output and evaluating model performance against real-world observations. These tasks are essential for building confidence in model predictions but often require time-consuming, manual workflows.

The goal of this SURF project is to develop an open-source Python-based toolbox that automates visualization and validation of WaveWatch III output using buoy observations. The toolbox will integrate model data with measurements from major buoy networks such as the National Data Buoy Center (NDBC) and the Coastal Data Information Program (CDIP), and provide standardized plots, performance metrics, and summary statistics. In addition, the toolbox will enable rapid generation of climatologies of key wave quantities, allowing users to explore long-term patterns and variability.

This project sits at the intersection of data science and Earth system science. The student will work with large environmental datasets, apply statistical and visualization techniques, and use the resulting tools to investigate how wave model performance varies across regions, seasons, and conditions. The toolbox will support applications in both academic research and industry, including wave energy, model development, and extreme weather studies.

Student’s role and learning objectives: 

Faculty Mentor: Brandon Dugan | Geophysics

Project Abstract: 

IODP3-NSF Expedition 501 collected multiple sediment and fluid samples from an offshore freshened groundwater system with the US Atlantic continental shelf south of Massachusetts. As part of the standard research for the expedition, bulk density and formation factor measurements were made assuming that pore water within the sediments had standard seawater salinity. Direct salinity measurements, however, document that salinity varied from approximately 32 practical salinity units (PSU) to as less than 1 PSU. This project will use the measured salinity data to update the bulk density and formation factor measurements so they are representative of the fluids that are hosted in these sediments.

Student’s role and learning objectives: 

Student will learn the basics of how offshore freshened groundwater systems work and the importance of knowing bulk density and formation factor. They will learn how the samples for bulk density, water analyses, and formation factor were collected and analyzed, and they will learn the basic calculations for integrating measured pore water salinity into bulk density and formation factor analyses. The student will then develop a simple Python-based code to update the bulk density and formation factor measurements. All produced data will be submitted as a Data Report to the Integrated Ocean Drilling Programme to provide a permanent, open-access archive of the work done so other researchers can use it. Student and mentor will meet weekly to set short-term goals and to do training and professional development related to reviewing data and metadata, developing and testing code, and writing of research results.

Faculty Mentor: Kate Allstadt | Geophysics

Project Abstract: 

This project is designed for students interested in learning how earthquakes and their effects are monitored and estimated in near-real-time. The student would work with a team of geophysicists at the U.S. Geological Survey’s (USGS) National Earthquake Information Center on the CSM campus. The team develops the near-real-time information products (including ShakeMap, PAGER, and Ground Failure) as part of the USGS’s response to national and international earthquakes. The project will focus on improving tools that rapidly estimate potential human impacts caused by earthquakes, with a focus on transportation network disruptions due to seismically triggered landslides. Students will be involved in updating and integrating a road and rail obstruction algorithm for inclusion in the operational codes for automated earthquake impact assessments.

Student’s role and learning objectives: 

The student will work on refactoring outdated Python codes for estimating road obstructions from earthquake-triggered landslides and optimizing them for inclusion in the seismic monitoring operational framework. The student will work with the latest geospatial roadway databases, learn collaborative coding techniques in Python and with Amazon Web Services tools, and develop a deeper understanding of geospatial analysis as part of a real-world application for improving disaster response.

Faculty Mentor: David Wald | Geophysics

Project Abstract: 

The US Geological Survey (USGS) ShakeMap system models in near-real-time the ground shaking from earthquakes all over the globe. ShakeMap uses several inputs to improve the modeling; peak ground motion values derived from seismic sensor time series data, Did You Feel It? (DYFI) human-generated macro-seismic intensities, and models of earthquake rupture in three dimensions. Much of the data ingestion required is automated, but there are many instances of missing valuable data for impactful (deadly and damaging) earthquakes. In this project a student will use existing Python code to develop a workflow to examine ShakeMap events (individually and in aggregate) for data that could be used to improve the estimates of shaking and downstream impacts on people and structures.

Student’s role and learning objectives: 

This project requires a student who is interested in the intersection of geophysics and computing. The student will be required to know some of the Python programming language and be willing to learn more. The student will work closely with members of the USGS Shakemap team to run and modify existing code. Through this project, the student will gain experience in data analysis and visualization, and real-world programming for scientific applications.

During this project the student will work on some or all of the following tasks:

• Use Python code to search ShakeMap products on the web for missing rupture data or ground motion recordings

• Compare ShakeMaps from the USGS with those from regional networks in the US, quantify the differences in input data

• Create a global catalog of “strong” ground motion stations, to be used in real-time operations as an indicator of potentially missing data

By the end of the project, the student is expected to:

• Make significant progress toward generating tools that can be used by the ShakeMap team during post-earthquake response

• Demonstrate improved skills in Python programming and data analysis

• Communicate findings via a presentation to the ShakeMap team and faculty advisors.

Faculty Mentor: Kate Allstadt, Matthew Siegfried | Geophysics

Project Abstract: 

Some glacier processes like calving and basal sliding can generate seismic signals that are recorded on global seismic monitoring networks. Even though patterns in seismic activity can tell us important information about glacier processes, glacier earthquakes are not currently included in operational global seismic monitoring. Largely, this is because their seismic signals are different than earthquakes, and there is no comprehensive catalog of glacier earthquakes that could be used to optimize seismic monitoring routines for their distinct characteristics. This project will focus on compiling a curated catalog of confirmed glacier earthquakes of a variety of types from around the world for addition to the “Exotic Seismic Events Catalog” (https://ds.iris.edu/ds/products/esec/) that can then be used to develop classification algorithms using newer machine learning approaches that are optimized for detecting glacier earthquakes and other exotic seismic signals in routine seismic monitoring.

Student’s role and learning objectives: 

The student will work with faculty mentors to design the catalog by adding new attributes specific to glacier earthquakes and will search the scientific literature for confirmed and published glacier seismic events for which the seismic data are openly available. They will then learn how to analyze the glacier seismic signals and process them for addition to the catalog and to conduct analyses to summarize the signal characteristics of different types of seismic events. The student will learn about glacier seismology and seismic monitoring and will gain substantial experience with Python coding and signal processing of seismic data. The student will work with seismic monitoring experts at the U.S. Geological Survey on campus and glacier experts in the Mines Geophysics Department.

Faculty Mentor: Greg Jackson | Mechanical Engineering

Project Abstract: 

The student will work with a graduate on exploring tubular fluidized bed reactor for converting biogas or upgrading natural gas with simulated concentrated solar fluxes for syngas production as part of a solar fuels. The student will learn how to use high-temperature flow rig controls, mass spectrometry for exhaust gas analysis, and thermal and pressure measurements for reactor characterization. The students will also explore the technoeconomic feasibility of scaled-up reactor designs using internally developed models for potential applications in the Southwest US and other places with high solar resources.

Student’s role and learning objectives: 

Faculty Mentor: Frances Zhu | Mechanical Engineering

Project Abstract: 

To understand how rovers move on the Moon, aerospace engineers typically conduct rigorous testing on a similar rover in an analogous terrain testbed. Over the course of the summer, we will test the mobility limits of our in-house rover in our in-house lunar analogue.

Student’s role and learning objectives: 

Faculty Mentor: Neal Sullivan | Mechanical Engineering

Project Abstract: 

In this project, the undergraduate student will work with a team of faculty, staff and graduate students to characterize the performance of unique electrolyzers for applications in “green” steel production. Some estimate that modern steel production accounts for perhaps 5% of global CO2 generation. Utilizing primarily on coal-based fuels, iron ore is “reduced” from iron oxide to iron in large-scale blast furnaces. Blast-furnace exhaust gases are one of the primary sources of these CO2 emissions. Mines is working with local developer Utility Global to harness these waste gas streams to convert H2O into H2 within their unique “eXERO” electrolyzers. Once formed, the renewably derived H2 can be utilized in other plant operations, reducing carbon footprint, and bringing a measure of sustainability to steel manufacturing.
Over the course of the summer, the student will work with a team of researchers in the Colorado Fuel Cell Center to establish operation of a new eXERO electrochemical-performance test stand. Once operational, the student will use the stand to characterize eXERO performance and H2 production across a broad operational space, and share results with fellow researchers at Mines and Utility Global. The student can expect a “hands-on”, laboratory-focused experience, though no experience is necessary, or expected.

Student’s role and learning objectives: 

Faculty Mentor: Neal Sullivan | Mechanical Engineering

Project Abstract: 

The research team at the Colorado Fuel Cell Center is working with Connecticut-based company Precision Combustion, Inc. (PCI) to develop advanced electrolyzers to convert lunar-sourced H2O into hydrogen. NASA seeks technologies to enable a sustained human presence on the moon. Lunar-based resources are scarce, but there is evidence of ice pockets within moon craters. This H2O-resource could be converted into H2 and O2 by harnessing solar power and using the electricity to drive an H2O electrolyzer, effectively splitting H2O into hydrogen and oxygen. As far out as this may seem, the technology has already been demonstrated…on Mars:

https://www.youtube.com/watch?v=UkQHCSZQvv0

In this project, the student will work within a team of faculty, research engineers, and graduate students to characterize performance of PCI electrolyzers for this unique application. A hands-on, laboratory-based experience can be expected.

Student’s role and learning objectives: 

Faculty Mentor: Alexandra Newman | Mechanical Engineering

Project Abstract: 

Standings in the major professional athletics leagues are published daily in newspapers and updated in real time on sports websites. These outlets typically report when teams are eliminated from the playoffs (i.e., have fallen so far behind that they cannot qualify even if they win all of their remaining games) and provide so-called “magic numbers” that represent how close the leading teams are to the opposite state, that of clinching a playoff spot. Fan interest in the standings helps drive traffic to commercial websites, determines which games to televise nationally, and influences what merchandise to offer and when to offer it. Teams and their managers have interest in this information because they can start selling tickets for home playoff games as soon as they have clinched a spot. Accurate assessment of teams’ playoff chances can also help coaches plan playing time for injured or rookie athletes. Sports media primarily rely on heuristics to make such assessments, which can lead them to announce eliminations or clinches late or even erroneously.

The Remote Interactive Optimization (RIOT) Sports website, riotsports.net, is powered by a collection of integer-programming optimization models that consider the teams’ schedules and the leagues’ tie-breaking rules to yield more accurate and informative magic numbers for Major League Baseball, the National Basketball Association, and, more recently, the Women’s National Basketball Association, and the Korean Baseball League. Using these models, RIOT can announce when a team has clinched or been eliminated from a playoff spot days before it is reported in popular media outlets. These models are run nightly on a behind-the-scenes server to generate up-to-date information regarding enhanced and traditional magic numbers. The site, uses publicly available data to compute traditional magic numbers (i.e., playoff elimination, playoff clinch, first place elimination and first place clinch), which are updated every day based on current standings during the regular season.

Student’s role and learning objectives: 

Faculty Mentor: Katie Knaus | Mechanical Engineering

Project Abstract: 

The goal of this project is to measure muscle fatigue, the reduction in muscle force/power in response to contractile activity and assess its effects on joint strength and on functional movements. Specifically, this project will use isokinetic dynamometry and surface electromyography (EMG) to assess baseline joint strength and muscle activity in maximum voluntary isometric contraction (MIVC) in ankle plantarflexion/dorsiflexion. Isokinetic dynamometry will be used for a muscle exercise protocol involving a prescribed succession of concentric or eccentric contractions to induce unilateral plantarflexor fatigue. Following the exercise protocol, MVIC testing will be repeated to quantify fatigue effects then participants will repeat functional movement assessments to determine changes in performance
To understand human performance, we want to understand an individual’s capacity for functional movement at the onset of an activity and how that capacity might change while sustaining activity over an extended period. This knowledge of fundamental muscle function has important and interesting in implications in many areas of human movement and biomechanics research and applications in multiple populations, including military service members and athletes.

Student’s role and learning objectives: 

Faculty Mentor: Valerio Mascolino | Mechanical Engineering

Project Abstract: 

Advanced nuclear reactor paradigms, such as small and micro modular reactors, often lack the space or penetrations required for traditional in-core detectors that are necessary for monitoring and control. This project investigates a machine learning-based alternative developed at Argonne National Laboratory. The method uses only ex-core detectors and a Green’s function formulation to reconstruct the in-core neutron flux with high fidelity, a traditionally “ill-posed” problem. This method relies on a mathematical approximation of the transport of neutrons within the reactor known as “monoenergetic diffusion”. The approximation is realized in the model via effective nuclear data for the reactor core regions and materials. The proposed SURF project aims at investigating the impact of the variability of the effective nuclear data as a function of a system’s parameter (e.g., temperature) on the accuracy of the methodology. To do so, an open-source Monte Carlo particle transport software, OpenMC, will be used to both generate the effective nuclear data as a function of relevant parameters and to produce synthetic ex-core detector data for the validation.

The project will be started as a SURF (Summer 2026) and continued as a MURF (Fall 2026 and Spring 2027). The student roles are divided by semester below. The aim of the SURF (i.e., the summer portion of the project) is to establish a computational model that is suitable to generate the required data and study its impact on the machine learning algorithm.

Student’s role and learning objectives: 

Faculty Mentor: Joy Gockel | Mechanical Engineering

Project Abstract: 

This project will investigate the manufacturing challenges of functionally graded materials that blend different metal compositions or microstructural features in a single component. While these spatially varying parts are highly desirable for aerospace and industrial applications, creating predictable structures across different shapes and sizes remains a significant hurdle. This project specifically will involve the directed energy deposition (DED) additive manufacturing (AM) process where different manufacturing parameters will be used to make, characterize and test material with spatially varying properties. This research is critical for developing the next generation of manufacturing systems capable of producing reliable, high-performance components with complex geometries and tailored material properties.

Student’s role and learning objectives: 

Faculty Mentor: Samantha Webster | Mechanical Engineering

Project Abstract: 

Metal additive manufacturing (AM) is a group of new technologies that have become popular in the aerospace, biomedical, and repair industries due to their flexible manufacturing capabilities. Laser powder blown directed energy deposition (DED-LB) is one of these manufacturing techniques where small metal powders are delivered through a nozzle onto a piece of metal and are melted by a laser that travels through the center of the nozzle. The nozzle and laser move together to deposit material where “lines” of metal can be drawn on a surface. Three dimensional parts can be made by layering these lines on top of each other and next to each other. A recent technique to understand the fundamental mechanisms in the DED-LB process is high-speed in situ X-ray imaging, which is performed at a synchrotron facility such as the Advanced Photon Source at Argonne National Labs. A synchrotron provides high powered X-rays which can be used to visualize phenomena inside the melt pool during the DED-LB process. However, a custom experimental setup needs to be designed to fit within an X-ray hutch at the beamline in order to perform these type of experiments. An instrument in the lab currently has the potential to be used at the beamline, but requires significant modification. The experimental setup will also be used for lab-scale experiments using high-speed imaging.

Student’s role and learning objectives: 

Faculty Mentor: George Kontoudis | Mechanical Engineering

Project Abstract: 

Aerial manipulation enables drones to physically interact with their environment, expanding their capabilities beyond passive sensing and surveillance. However, reliable grasping from a flying platform remains a major challenge due to aerodynamic downwash, payload instability, and limited contact time. This project aims to experimentally validate a hybrid aerial gripper mounted on a quadrotor platform. The gripper combines three complementary mechanisms: a pinching device to reduce propeller downwash, a compliant multi-finger structure for adaptive grasping, and a net-based enclosure to secure objects during transport. The research will focus on system integration and experimental validation within an indoor drone arena equipped with motion capture. The student will implement autonomous grasp-and-transport routines using ROS2-based control and evaluate performance across multiple object geometries and weights. Experimental metrics will include grasp success rate, stability during transport, positional accuracy, and robustness to disturbances.

Student’s role and learning objectives: 

Faculty Mentor: George Kontoudis | Mechanical Engineering

Project Abstract: 

Body-powered prostheses are widely used due to their mechanical robustness, low cost, and intuitive control. However, they are limited in dexterity and adaptability compared to muscle-machine interfaces. This project aims to develop and experimentally evaluate a hybrid muscle-machine interface that integrates forcemyography (FMG) and electromyography (EMG) sensing with a traditional body-powered prosthesis to enhance dexterous manipulation while maintaining robust grasping capabilities. The research will focus on designing a signal acquisition and control interface that augments cable-driven prosthetic actuation with muscle-machine interfaces. The hybrid system will enable improved grip modulation and functional versatility without fully replacing the mechanical control paradigm. The student will prototype the sensing and signal-processing pipeline, integrate it with an existing body-powered prosthetic device, and conduct controlled experimental evaluations. The goal is to demonstrate manipulation capabilities of a hybrid solution that combines mechanical body-powered actuation with muscle-machine interfaces.

Student’s role and learning objectives: 

Faculty Mentor: Paulo Tabares | Mechanical Engineering

Project Abstract: 

Many buildings utilize cool outdoor air to naturally maintain occupant comfort with minimal energy consumption. However, this natural cooling capacity may be diminished due to rising global temperatures and increased air pollution. This project continues to quantify these effects: the impact of higher outdoor air temperatures coupled with increased outdoor air pollution using publicly available data

Student’s role and learning objectives: 

Faculty Mentor: Leslie Lamberson | Mechanical Engineering

Project Abstract: 

Protective materials—such as those used in helmets, body armor, and spacecraft shielding—must withstand extreme forces while remaining lightweight and effective. To design better protective materials, scientists test how they behave under rapid and intense impacts, similar to those experienced in high-speed collisions or explosions.
One of the most common ways to study these materials is by using a Kolsky bar, a specialized device that generates and measures high-speed forces in a controlled environment. However, traditional Kolsky bars are large and expensive, limiting their use in certain applications. This research project focuses on developing a miniaturized version of the Kolsky bar, making it more accessible for small-scale experiments while still providing accurate results.
A smaller, portable Kolsky bar could allow researchers to test advanced materials more efficiently and in a wider range of conditions. This work will involve designing, building, and testing the new system, with the goal of improving our ability to create stronger and safer protective materials for the future.

Student’s role and learning objectives: 

Faculty Mentor: Valerio Mascolino | Mechanical Engineering

Project Abstract: 

The modeling of advanced nuclear reactor designs, as envisioned to be licensed and built in the coming decades by the Department of Energy (DOE), will require advanced modeling and simulation tools fundamentally different from the software used for current commercial light water reactors, such as pressurized and boiling water reactors. Among the advanced methodologies capable of accurately predicting the distribution of particles within a nuclear reactor (e.g., to simulate accident transients), hybrid methods have garnered attention in recent years. These methods combine the benefits of existing state-of-the-art software by combining deterministic and Monte Carlo (statistical) approaches. Mines is involved in the development of one such hybrid approach, based on the Transient Fission Matrix (TFM) methodology. Only a handful of research groups in the world have worked on this method, and there currently isn’t a widely available state-of-the-art neutron transport software that implements it. This SURF project, intended for a student interested in learning about particle transport in nuclear reactors and software and algorithm development, focuses on implementing TFM capabilities into the open-source Monte Carlo software, OpenMC. OpenMC is one of the most widely used software for the modeling and simulation of nuclear systems; it is heavily supported by the DOE, and maintained by collaborators at Argonne National Laboratory. This process will expose the selected student to national laboratory scientists and their research.

The project is intended to be started as a SURF (Summer 2026) and continued as a MURF (Fall 2026). In the summer, the student will familiarize themselves with the OpenMC software, draft the software modification requirements, and implement preliminary modifications to achieve the project goals. The following Fall, the student will finalize the generation of time-independent TFM coefficients, as well as setting the stage for the implementation of their time-dependent counterpart.

Student’s role and learning objectives: 

Faculty Mentor: Valerio Mascolino | Mechanical Engineering

Project Abstract: 

Evaluating the neutron flux in the core of a nuclear reactor is of paramount importance for its safety and operation. Advanced reactor concepts are shifting towards extremely compact reactor cores, for which in-core detectors are either not feasible or undesirable. This is a shift in paradigm with respect to current highly instrumented commercial power reactors, for which a large amount of data from decades of operational know-how are available. This challenge generates the need for methodologies that allow operators and engineers to accurately evaluate the flux distribution inside of the reactor without having to rely on in-core instrumentation, taking advantage of ex-core detectors only. However, the reconstruction of the neutron flux from a handful of ex-core detectors is an ill-posed inverse problem that is very difficult to solve, especially with traditional computer-intensive transport methods. Novel hybrid methods for solving the neutron transport problem have been developed that combine the benefits of deterministic and statistical approaches. Among these, the fission matrix method is a response function-based approach that can obtain solutions with extremely high accuracy while substantially reducing computing time. The significant speedup achieved using this method can enable the application of iterative inference techniques, accelerated by machine learning and artificial intelligence. By generating a database of response functions for a large number of fuel compositions and properties, these entries can then be generated in real-time in an inference algorithm to match ex-core detector arrays output and ensure that a unique solution can be found. Additionally, such methodology could be used with an inverse approach to optimize the positioning of ex-core detectors to maximize the identification of isotopic signatures and diverted nuclear material in a reactor. This safeguard application could allow the identification of non-proliferation threats from ill-intentioned international actors, by deploying a non-intrusive detection system during inspections that is capable of reconstructing with high accuracy the particle and isotopic distribution within the core. This SURF project aims at developing a preliminary inference-based methodology using the fission matrix methodology that is being extensively developed at Mines.

The project is intended to be started as a SURF (Summer 2026) and continued as a MURF (Fall 2026). In the summer, the student will familiarize themselves with the OpenMC software, learn the fission matrix method, generate a test case matrix for evaluating the methodology, and initiate conceptual and algorithmic development. The following Fall, the student will finalize the preliminary implementation of the methodology into an open-source repository, generate synthetic data for testing the methodology, and perform preliminary verification and validation.

Student’s role and learning objectives: 

Faculty Mentor:  Jihye Kim | Metallurgical and Materials Engineering

Project Abstract: 

The recent expansion of clean energy technologies, particularly electric vehicles, has driven enormous growth in the lithium-ion battery (LIB) market. The sustainability of this growth critically depends on the robustness of supply chains for essential battery metals. However, the increasing demand for critical metals such as Co and Ni is projected to outpace identified reserves in the near future. Consequently, developing sustainable processes for recovering these metals from spent LIBs has become imperative.

One of the most significant challenges in this field is the selective recovery of Co and Ni. These two metals share inherently similar physicochemical properties, including vapor pressure, reduction potentials (-0.277 V for Co and -0.250 V for Ni vs. SHE), and solubility, causing substantial challenges for separation using conventional pyrometallurgical and hydrometallurgical methods, as well as emerging electrochemical approaches.

To overcome this bottleneck, the project aims to develop a programmable electrodeposition technology to selectively recover Co and Ni at precisely controlled and predictable ratios. Since non-equilibrium behavior is strongly influenced by ion size-induced diffusion, we leverage this effect and enhance separation efficiency through complexation reactions. Electrodeposition tests, both with and without complexing agents, will determine whether stabilizing one element via complexation can maximize deposition differences under transient conditions.

Student’s role and Learning Objectives: 

The undergraduate student will support experimental development of a programmable electrodeposition process for selective Co and Ni recovery. Their responsibilities will include preparing synthetic electrolytes, conducting electrodeposition experiments with and without complexing agents, and performing solution and solid characterization using ICP-OES, XRD, FTIR, and UV-Vis. The student will assist with data analysis to evaluate selectivity under non-equilibrium conditions and document experimental outcomes.

Learning objectives are to develop practical skills in electrochemistry and extractive metallurgy, understand the role of coordination chemistry and mass transport in electrochemical separations, gain experience with advanced characterization tools, and build foundational research skills in experimental design, data analysis, and scientific communication.

The undergraduate student will work closely with a graduate student mentor who provides day-to-day guidance on laboratory procedures and data analysis. The student will also meet with the faculty advisor every other week to discuss progress, address challenges, and define clear research goals and plans for the subsequent period.

Faculty Mentor:  Jihye Kim | Metallurgical and Materials Engineering

Project Abstract: 

Rare earth elements are critical components of clean energy technologies, electronics, and advanced materials, yet their supply is increasingly constrained. Large quantities of these valuable elements remain locked in industrial waste streams. One such material is bauxite residue, a byproduct of aluminum production that is generated worldwide and stored long term.

This project investigates sustainable methods to both extract and separate rare earth elements from bauxite residue. The work will compare conventional chemical lixiviants, such as inorganic acids, with a newer class of chemicals known as ionic liquids, which may offer improved selectivity and reduced environmental impact. Through controlled leaching and separation experiments, combined with advanced materials characterization, the project seeks to understand how rare earth elements are released from complex solid matrices and how they can be selectively separated from major impurities. The outcomes will support the development of cleaner, more efficient processes for recovering critical materials from industrial waste.

Student’s role and Learning Objectives: 

The student will lead experimental work focused on both leaching and separation of rare earth elements from bauxite residue using conventional lixiviants and ionic liquids. Responsibilities will include conducting leaching experiments, developing and testing separation processes, and performing characterization using ICP-OES, XRD, SEM-EDS, XPS, and/or XRF. The student will interpret experimental data, evaluate recovery and selectivity, and communicate results through research posters.

Learning objectives are to develop hands-on experience in hydrometallurgical extraction and separation of critical minerals, learn how to use advanced characterization techniques, and strengthen scientific research and communication skills.

The undergraduate student will work closely with a graduate student mentor who provides day-to-day guidance on laboratory procedures and data analysis. The student will also meet with the faculty advisor every other week to discuss progress, address challenges, and define clear research goals and plans for the subsequent period.

Faculty Mentor:  Geoff Brennecka | Metallurgical and Materials Engineering

Project Abstract: 

Aerosol deposition is a technique to achieve nearly 100% dense depositions from a powder feedstock–even hard ceramics–at room temperature by spraying them at high velocity at a substrate and essentially ‘splatting’ the powders into a solid film. Our group has a project funded by the Office of Naval Research to use such aerosol-deposited films to isolate effects of densification and grain growth during thermal annealing for a better fundamental understanding of microstructure development in piezoelectric ceramics for sonar applications. The broader project includes both experimental sample fabrication and testing as well as multi-scale computational simulations.

Student’s role and Learning Objectives: 

The SURF student will work on processing ceramic powders by solid state synthesis and aerosol deposition and will measure and characterize both the starting powders and resulting films using optical and electron microscopy and x-ray diffraction. In addition to depositing and characterizing films, the SURF student will also assist in upgrading our aerosol deposition chamber with additional capabilities including a motion-controlled x-y stage. Work will initially be in close collaboration with the graduate student whose thesis is funded by this project, but over time the SURF student will become more independent and self-directed (with guidance from both the faculty mentor and graduate student collaborator). The SURF student will achieve competence in powder processing of ceramics and will gain important experience in microscopy and diffraction techniques. The SURF student will also learn about integrated computational and experimental research projects.

Faculty Mentor:  Eve Mozur | Metallurgical and Materials Engineering

Project Abstract: 

Many renewable energy feedstocks are intermittent – the wind doesn’t always blow and the sun doesn’t shine at night. When implementing these technologies, we therefore need energy storage materials. Currently, we rely on lithium-ion batteries that have limited capacity. The goal of this project is to develop methods to increase the capacity of lithium- and sodium-ion battery materials through rational substitution. Known high-capacity battery materials suffer from poor reversibility due to volume changes and high energy barriers. We will use manganese oxides as a case study.

Student’s role and Learning Objectives: 

Students will gain skills in: preparation of materials, structural characterization of materials (e.g. diffraction), presenting data (e.g. graphs and figures), electrochemistry.

The student will become a member of the Mozur Research Group, and therefore will be mentored by Dr. Mozur directly as well as graduate students working on similar projects. Mentoring activities will include meeting as a research group, meeting one-on-one with Dr. Mozur, and training in laboratory skills. We aim to have clear expectations for all members of the research groups, including setting reasonable and achievable research goals that we are able to modify according to the progress of projects.

Faculty Mentor:  Xiaolei Guo | Metallurgical and Materials Engineering

Project Abstract: 

Coastal infrastructure faces escalating risks from climate-driven sea-level rise and more frequent flooding, challenging the longevity of conventional building materials. Ensuring the durability of concrete under these harsh, cyclic exposure conditions is essential for resilient, sustainable design. The goal of this project is to evaluate the corrosion resistance of both historical and modern concrete mixtures, including those formulated with ordinary Portland cement and emerging next-generation binders, under laboratory-simulated coastal environments that replicate rising seas and recurrent inundation.

Student’s role and Learning Objectives: 

Students will gain hands-on experience in concrete mix design, accelerated corrosion testing, data analysis, and scientific communication as they design, execute, and troubleshoot experiments in a collaborative research setting. They will also develop professional competencies in project management, teamwork, and networking through regular mentorship, presentations, and engagement with real-world infrastructure resilience challenges.

The PI will begin with a comprehensive orientation covering lab safety, project goals, and key techniques. The PI will then meet one-on-one bi-weekly to set milestones, troubleshoot experiments, and review data. As their skills grow, the PI will integrate them into group meetings, assign increasing responsibility, provide regular written and oral feedback, and coach them on presenting results at seminars or conferences.

Faculty Mentor:  Xiaolei Guo | Metallurgical and Materials Engineering

Project Abstract: 

High-entropy alloys are a new class of metals made by combining several elements in nearly equal amounts. Unlike traditional alloys, which are based on one main element, these materials can exhibit exceptional strength, stability, and resistance to extreme environments. This project focuses on a high-entropy alloy composed of niobium, tantalum, titanium, and vanadium (NbTaTiV), a material that shows promise for use in demanding applications such as energy systems, aerospace components, and harsh chemical environments.

The goal of this study is to understand how the microstructure of the NbTaTiV alloy influences its resistance to corrosion. The “microstructure” of a metal refers to the arrangement of its grains, phases, and defects at very small length scales. These features strongly affect how a material interacts with its environment, including how and where corrosion begins. In this project, the alloy’s microstructure will be carefully characterized using advanced microscopy and analytical tools, and its corrosion behavior will be evaluated through controlled laboratory experiments.

By linking microstructural features to corrosion performance, this work aims to provide fundamental insights into why NbTaTiV alloys resist degradation and how their properties can be optimized. The results will help guide the design of more durable, corrosion-resistant materials for future technologies and provide hands-on research experience in materials characterization and corrosion science for the student.

Student’s role and Learning Objectives: 

The undergraduate student will actively participate in the study of the microstructure and corrosion behavior of the NbTaTiV high-entropy alloy, gaining hands-on experience in materials research. The student will assist with sample preparation, including sectioning, mounting, grinding, and polishing, and will help characterize the alloy microstructure using optical and scanning electron microscopy. They will also support corrosion experiments, such as immersion and electrochemical tests, and assist with data collection, organization, and preliminary analysis.

Through these activities, the student will develop an understanding of how microstructural features influence corrosion behavior, learn fundamental corrosion concepts, and gain practical experience with common materials characterization techniques. By the end of the project, the student will be able to interpret experimental data, calculate basic corrosion metrics, and communicate results through figures and short written summaries.

Mentoring will be provided through close supervision with increasing independence over time. Initial training will emphasize laboratory safety, research ethics, and core concepts in corrosion and microstructure. The student will meet weekly with the mentor to review progress, discuss results, and receive feedback on data analysis and documentation. The student will also be guided in preparing a final written report or presentation, supporting their development as an independent and confident researcher.

Faculty Mentor:  Xiaolei Guo | Metallurgical and Materials Engineering

Project Abstract: 

This project will investigate how metals and alloys made using modern additive manufacturing techniques behave when exposed to corrosive environments. Additive manufacturing methods such as laser powder bed fusion, directed energy deposition, and friction stir additive manufacturing build metal parts layer by layer, which can create unique internal structures compared to traditionally manufactured materials. These internal features can strongly influence how corrosion starts and progresses. In this study, the student will examine different additively manufactured metals, observe their internal structure, and evaluate their resistance to corrosion through laboratory testing. The results will help improve understanding of how manufacturing methods affect material durability and guide the design of longer-lasting metal components.

Student’s role and Learning Objectives: 

The undergraduate student will play an active role in investigating the corrosion behavior of metals and alloys produced by additive manufacturing techniques. The student will assist with specimen preparation, such as sectioning, mounting, grinding, and polishing, and will help characterize microstructural features using optical and scanning electron microscopy. They will also participate in corrosion experiments, including immersion testing and electrochemical measurements, and will help collect and organize experimental data.

Through this work, the student will develop core learning outcomes, including an understanding of how different manufacturing processes influence microstructure and corrosion behavior, familiarity with fundamental corrosion mechanisms, and practical experience with common materials characterization and testing techniques. The student will gain skills in data analysis, including calculating corrosion rates and interpreting trends, and will practice communicating scientific results through figures, short written summaries, and presentations.

Mentoring will be provided through structured, hands-on guidance with increasing independence. Initial training will focus on laboratory safety, research ethics, and foundational concepts in additive manufacturing and corrosion. The mentor will meet regularly with the student to review progress, discuss results, and provide feedback on data analysis and documentation. The student will also be guided in preparing a final report or poster, supporting professional development and confidence in research communication.

Faculty Mentor:  Anna Staerz | Metallurgical and Materials Engineering

Project Abstract: 

We have designed a low-cost measurement chamber that allows us to test gas sensors and fuel cell materials in operation conditions. At the moment these measurement chambers are operated using large, expensive and bulky electrochemical measurement equipment. To lower cost, allowing for more measurements to be done simultaneously or more long-term studies a simplified measurement set-up is required. During the summer, the student will design the circuitry and integrate small chip-based impedance measurement systems with our measurement hardware. The student will follow an iterative approach benchmarking the chip-based system against lab scale measurement equipment. The student will gain experience in circuit design, electrical wiring, electrochemical measurements, data collection and evaluation.

Student’s role and Learning Objectives: 

The engineering objective of the project is to create small chip-based measurement systems optimized for the needs of gas sensors and fuel cells.
The student will work closely with the PhD students to determine design the circuitry and integrate the equipment with the hardware. The student will be asked to prepare and present a project plan at the weekly group meetings.
During the summer internship, the student will learn the principles of engineering design, taking an iterative approach with frequent benchmarking. The project will give the student the chance to work with micro-electronics, design circuits, learn 3D printing (outer housing), and some exposure to simple programming (measurement control), Depending on the background and interests of the student any of these aspects can be focused on more heavily.
I am a very hands-on mentor with an open office policy. Unless I am in a meeting students are always allowed to stop by my office to ask questions and share ideas. I currently advise five PhD students who all have expertise in various aspects of the project. We have a weekly group organizational meeting and in the summer, we will also do a weekly paper discussion. The undergraduate student will be encouraged to participate in both meetings.

Faculty Mentor:  Megan Holtz | Metallurgical and Materials Engineering

Project Abstract: 

Multiferroic oxides have been studied extensively for a wide variety of electronic applications from quantum computing to memory storage. Hexagonal ABO3 oxide materials are particularly promising for these applications because they couple ferroelectricity from A-site displacements with frustrated magnetism arising on their B-sites. Here we grow thin films of YMnO3 (YMO) (0001) via molecular beam epitaxy (MBE) for use in investigating the effects of indium doping on its ferroelectric behavior and ionic conductivity. We deposited an epitaxial Ir (111) electrode on a YSZ (111) substrate, then co-deposited yttria and manganese in oxygen plasma. We determined appropriate growth temperatures and rates by utilizing x-ray diffraction (XRD) and RHEED to characterize their phase, orientation, and roughness. We then used scanning transmission electron microscopy (STEM) to verify the layering behavior of the film. Further investigation will determine ferroelectricity and ionic conductivity of the film with varied indium doping.

Student’s role and Learning Objectives: 

Student will learn thin film deposition and characterization techniques.

Faculty Mentor:  Megan Holtz | Metallurgical and Materials Engineering

Project Abstract: 

This project will focus on the computational simulation of advanced microscopy. One of the biggest challenges in materials science is “seeing” where atoms are in 3D. We use a technique called Scanning Transmission Electron Microscopy (STEM) to better visualize crystal structure and defects at the smallest length scales. Your role will be to use computer simulations to determine the best way to image these atomic structures before we get to the lab.

Student’s role and Learning Objectives: 

This position is ideal for a student in Physics, MME, or Computer Science who is interested in the intersection of coding and physical science. No prior experience with electron microscopy is required—we will train you on the software and the underlying physics. If you enjoy problem-solving and want to help build the foundation for next-generation technology, we want to hear from you!

Faculty Mentor:  Kei Yazawa | Metallurgical and Materials Engineering

Project Abstract: 

(Al,X)N-based materials (X = Sc3+, B3+, Gd3+) have garnered much attention for microelectronics with their ferro-and piezo-electric properties. Recently, we demonstrated that films with heterovalent (donor) substitution such as X = Hf4+ stay electrically insulating and show ferroelectric switching and improvement of piezoelectric response. This result drastically extends the substitution element candidates including possible acceptor substitution such as Cu1+ and Ni2+. However, the effects of the substitution polarity (donor vs acceptor) on insulating AlN-based materials have not been fully understood. The proposed study will investigate the leakage conduction mechanisms of (Al,Cu)N and (Al,Ni)N films and compare them to donor-substitute (Al,Hf)N, to provide insight into the leakage current reduction strategy in heterovalent substituted AlN films.

Student’s role and Learning Objectives: 

The student will (1) measure leakage current at various electric fields and temperatures and (2) analyze the leakage mechanisms based on the conduction models. Throughout the project, the student will learn (1) handling a probe station and source meter unit for thin film devices characterization, and (2) energy landscapes and properties of trap states associated with heterovalent substitution.

Faculty Mentor:  Zhenzhen Yu | Metallurgical and Materials Engineering

Project Abstract: 

Laser-hybrid welding is currently being evaluated as a promising new technology for the shipbuilding industry. This project focuses on the quantitative assessment of the microscopic changes introduced when using hand-held laser tools followed by arc rewelding. The student participant will gain critical expertise in metallurgical analysis, learning how to evaluate the structural integrity of high-strength steel. This work is an essential step toward adopting more efficient manufacturing processes in maritime engineering.

Student’s role and Learning Objectives: 

Student Roles and Responsibilities: The student will be responsible for preparing metallurgical samples, including sectioning, grinding, and polishing welded specimens. They will perform primary data collection using optical microscopy, Vickers hardness mapping, and Scanning Electron Microscopy (SEM) for high-resolution imaging.

Student Learning Objectives: By the end of this program, the student will be able to: (1) Independently operate metallurgical characterization equipment; (2)
Identify and quantify microstructural phases in high-strength low-alloy (HSLA) steels. (3) Relate welding process parameters to the resulting mechanical properties of the joint.

Mentoring Activities: The faculty advisor will provide structured mentorship through bi-weekly progress reviews to discuss data interpretation and troubleshoot experimental challenges. Additionally, the student will be embedded within the Center for Welding, Joining, and Coatings Research, where they will receive daily technical peer-mentoring from senior graduate students. To provide professional context, I will facilitate quarterly technical briefings between the student and our industrial advisors, allowing the student to practice communicating their findings to professional stakeholders.

Faculty Mentor: Nicole Smith| Mining Engineering

Project Abstract: 

Critical minerals and materials (CMMs) are needed for a wide range of applications and technologies, and the US government has a stated policy of increasing domestic production and processing of CMMs. Increasing domestic CMM production could require opening new mines, expanding existing mines, and recovering resources from non-traditional sources. In each case, CMM production will have impacts for the communities near these activities, which could include positive and negative impacts across economic, social, and environmental dimensions.

This SURF project will focus on understanding public perceptions of CMM production in the areas where projects have been proposed or are being developed. The research uses surveys of community members to gather data on public perceptions and the factors behind them. The SURF project will allow a student to gain hands-on experience in social science research and analysis. The project is appropriate to students in any major, who have an interest in community dynamics and mineral development. There is the possibility of participating in fieldwork in northern Minnesota for survey data collection at the end of the summer (early August).

Student’s role and learning objectives: 

The SURF project work will entail a mix of data analysis, project support, and potential for fieldwork to collect new data. The main project work will be statistical analysis and interpretation of survey data that has already been collected. Candidates should have a basic foundation in statistics (e.g. Math 201 or equivalent) with a preference for additional statistics training (e.g. Math 324, 335, 437, etc.), as well as interest in social science topics. Analysis work may include contributing to reports or articles. Secondary work will include project support tasks such as updating websites, preparing outreach materials, etc. Finally, the student may have the possibility to participate in fieldwork to northern Minnesota to conduct a survey, in early August. Fieldwork participation depends on successful completion of SURF work and confirmation of logistics. In the event of fieldwork, all travel and expenses would be covered by the researchers (free for the student).

Learning objectives include (1) deepen understanding of the social dynamics that influence acceptance or opposition of industrial activities, including economic, social, and environmental influences; (2) learn about the policy and practical challenges surrounding critical minerals production; (3) strengthen statistics expertise through application of statistical techniques to real world, primary data; (4) gain experience working as part of a multi-person research team; (5) potentially gain experience in direct social science data collection via in-person surveys.

The SURF student will benefit from mentorship from two faculty members: Nicole Smith and Aaron Malone. Dr. Smith and Dr. Malone are both social scientists with substantial expertise in social dynamics of the mining industry and CMMs. Mentorship will include weekly check-in meetings, support and feedback on a continual basis, and the opportunity to present your summer’s work to the research group and receive feedback.

Faculty Mentor: Wouter Van De Pontseele | Physics

Project Abstract: 

Neutrinos are the “ghost particles” of the universe, nearly massless, neutrally charged, and incredibly difficult to detect. To study them, physicists use ultra-sensitive superconducting detectors that must operate at temperatures colder than outer space. This project focuses on the critical microwave “infrastructure” required to make these detectors work.

We are targetting two cutting-edge challenges in cryogenic engineering. First, you will help design and optimize the physical 3D packaging that houses Shot-Noise Tunnel Junctions (SNTJs), specialized quantum devices used to calibrate system noise at the quantum limit. This involves using ANSYS HFSS to simulate how microwave signals behave when we integrate complex components like bias tees into tiny, chilled environments. Second, you will dive into the “digital brain” of the experiment by programming a ZCU216 FPGA platform to handle microwave multiplexing. This technology allows us to read out thousands of sensors simultaneously through a single cable, a requirement for the massive detector arrays used in next-generation neutrino and astrophysics experiments. Conducted in collaboration with NIST, this project offers a front-row seat to the engineering that makes modern “big science” possible.

Student’s role and learning objectives: 

Student Roles:

– Cryogenic Package Design: Perform 3D electromagnetic simulations in ANSYS HFSS to modify existing packaging, specifically focusing on the integration of a shot-noise tunnel junction and a bias tee to ensure signal integrity at GHz frequencies.
– Digital Readout Development: Program and deploy microwave multiplexing algorithms on the Xilinx ZCU216 (RFSoC) platform, focusing on the implementation of flux-ramp modulation for signal linearization.
– Hardware-Firmware Integration: Test the radout chain between the physical SNTJ hardware and the digital readout system to verify noise calibration accuracy.
– Technical Liaison: Actively participate in design reviews and technical check-ins with NIST collaborators to ensure designs meet national metrology standards.

Learning Objectives:

– Applied Electromagnetics: Move beyond textbook theory to model complex S-parameters and impedance matching in a 3D environment where parasitic effects dominate.
– Quantum Metrology: Understand the physics of shot-noise as a primary calibration standard and how it is used to characterize the noise temperature of HEMT amplifiers.
– High-Speed DSP for Physics: Gain proficiency in FPGA-based digital signal processing, specifically tone-tracking and the multiplexing techniques used in the Simons Observatory and neutrino testbeds.
-Professional Design Cycle: Experience the iterative process of “Simulate → Design → Collaborate,” learning how to communicate technical hardware requirements to high-level research partners.

Faculty Mentor: Veronica Policht | Physics

Project Abstract: 

In typical optical setups fed by lasers we work with Gaussian beams where the profile in the xy plane follows a Gaussian curve. Structured light is a growing field of optics that modifies the spatial profile of light by applying a phase mask in the spatial frequency domain. Some common structured light beams are so-called vortex beams which have a toroidal beam profile and carry orbital angular momentum. Typical phase masks are static – as in they are solid pieces of glass or other material designed to produce a single kind of structured light. In this project, the student researcher will help to build and program an apparatus that can produce programmable kinds of structured light through use of a spatial light modulator (SLM). The ability to dynamically program the structured light beam profile allows for a new class of spectroscopies, where we can integrate spatial degrees of freedom in addition to the dynamics (time) and spectral (wavelength/frequency) degrees of freedom in typical spectroscopies. The student researcher should have taken electricity and magnetism (E&M), be familiar with Fourier transforms, and be familiar with coding in either python or Matlab.

Student’s role and learning objectives: 

The student researcher will take on the following roles for this 7 – 10 week project:
– Research SLM-based structured light generation techniques
– Program an interface with the SLM
– Calibrate the SLM using a HeNe or similar cw-laser
– Measure beam properties at the input and output of the apparatus
– Demonstrate structured light generation
– (Potentially) Fabricate an axicon grating using grey-scale lithography

The learning objectives are:
– Ability to compare different techniques for structured light generation and determine the optimal technique for broadband spectroscopy applications
– Apply E&M knowledge to understand how to manipulate the spatial mode of light through the application of phase masks in the spatial frequency domain
– Analyze sources of beam distortion and generate calibration phase masks to compensate for them
– Simulate a phase mask to generate a vortex beam and develop a computer program to apply this phase mask to the SLM
– Become familiar with basic optical engineering and optical setup design

The faculty’s mentoring activities include:
– Guidance throughout the research activity
– Weekly update meetings to discuss progress and challenges
– Provide the student researcher with necessary resources for executing the project, including relevant background readings and equipment purchases
– Inclusion of student researcher on relevant collaborations

Faculty Mentor: Kamil Ciesielski | Physics

Project Abstract: 

The emergence of new complex electronic materials is vital for improvements in quantum information, sensing and computing. Recently, our laboratory discovered ternary materials with frustrated vanadium sublattices, i.e. networks where the magnetic spins cannot arrange in alternating directions (imagine putting the spins on the corners of a triangle). These materials have been shown to host multiple coexisting quantum effects, including superconductivity, topological properties and charge density waves. The phenomena are found to interact and compete, making the materials tunable and hence potentially useful for quantum technology. Full understanding and control of these interactions, however, is challenging because of very limited material examples known so far. In this project, we aim to discover novel compounds with frustrated vanadium nets by screening broad array of ternary phase diagrams. As our synthesis technique, we will use single crystal growth from metal flux. The obtained samples will be characterized structurally by scanning electron microscopy (SEM) and x-ray diffraction (XRD). The student’s work will also include growth of known compounds with vanadium sublattices, what were not characterized from perspective of their magnetotransport and thermodynamic properties.

The student will also engage in discussion on scientific literature both directly relevant to the laboratory activities (i.e. focusing on synthesis and new compounds discovery), as well as the articles discussing the newest findings on quantum effects in frustrated Kagome nets. The obtained results will be shared every 2-3 weeks with the research group by the short students’ presentations.

Student’s role and learning objectives: 

Learning objectives:
– Proficiency in single crystal growth from metal flux,
– Familiarity with fundamental tool of structural characterization for inorganic materials: SEM and XRD (powder and single crystal)
– Good practices in data management and scientific communication,
– Scientific literacy.

Students role:
– Co-designing experiments for the single crystal growth with the SURF mentor, sample weighing, sealing the silica ampoules with the torch, furnace operation, and centrifuging,
– Operating scanning electron microscopy, x-ray diffraction devices with mentor supervision,
– Maintaining personal laboratory logbook and leading short presentations of the findings to the research group every 2-3 weeks,
– Reading relevant scientific literature provided by the mentor and chosen independently by the student.

Faculty Mentor: Kamil Ciesielski | Physics

Project Abstract: 

The emergence of new complex electronic materials is vital for improvements in quantum information, sensing and computing. Recently, our laboratory discovered ternary materials with frustrated vanadium sublattices, i.e. networks where the magnetic spins cannot arrange in alternating directions (imagine putting the spins on the corners of a triangle). These materials have been shown to host multiple coexisting quantum effects, including superconductivity, topological properties and charge density waves. The phenomena are found to interact and compete, making the materials tunable and hence potentially useful for quantum technology. Full understanding and control of these interactions, however, is challenging because of very limited material examples known so far. In this project, we aim to discover novel compounds with frustrated vanadium nets by screening broad array of ternary phase diagrams. As our synthesis technique, we will use single crystal growth from metal flux. The obtained samples will be characterized structurally by scanning electron microscopy (SEM) and x-ray diffraction (XRD). The student’s work will also include growth of known compounds with vanadium sublattices, what were not characterized from perspective of their magnetotransport and thermodynamic properties.

The student will also engage in discussion on scientific literature both directly relevant to the laboratory activities (i.e. focusing on synthesis and new compounds discovery), as well as the articles discussing the newest findings on quantum effects in frustrated Kagome nets. The obtained results will be shared every 2-3 weeks with the research group by the short students’ presentations.

Importantly, this project will be co-led by Dr. Phil Yox from SIF at Mines, formerly Chemistry Department at Mines. Dr. Yox will be involved in leading X-ray diffraction studies and potentially contributing to synthesis efforts, depending on development of the project.

Student’s role and learning objectives: 

Learning objectives:
– Proficiency in single crystal growth from metal flux,
– Familiarity with fundamental tool of structural characterization for inorganic materials: SEM and XRD (powder and single crystal)
– Good practices in data management and scientific communication,
– Scientific literacy.

Students role:
– Co-designing experiments for the single crystal growth with the SURF mentor, sample weighing, sealing the silica ampoules with the torch, furnace operation, and centrifuging,
– Operating scanning electron microscopy, x-ray diffraction devices with mentor supervision,
– Maintaining personal laboratory logbook and leading short presentations of the findings to the research group every 2-3 weeks,
– Reading relevant scientific literature provided by the mentor and chosen independently by the student.

Faculty Mentor: Meenakshi Singh | Physics

Project Abstract: 

Predicting massive tectonic earthquakes is one of science’s greatest unsolved challenges. The main roadblock? A lack of data. Major fault lines only rupture every few decades or centuries, leaving AI models with almost nothing to train on. To solve this, we look to the ice. Glaciers experience “icequakes”—driven by the exact same stick-slip friction as crustal faults—but they happen twice a day. They are earthquakes in fast-forward, providing a massive, perfect dataset of pre-quake seismic noise. In this SURF project, we will test whether next-generation tech can do what classical computers can’t. Students will use real seismic data from Antarctica to train a Quantum Machine Learning (QML) algorithm. By leveraging the unique pattern-recognition strengths of quantum computing, we aim to detect the subtle, hidden acoustic signals that happen right before the ice snaps.

Student’s role and learning objectives: 

The student will take a hands-on role in bridging classical seismology and next-generation computing, with no prior quantum experience required. Their primary responsibility will be wrangling real-world Antarctic icequake data using Python, establishing classical machine learning baselines (like Random Forests) for slip prediction, and eventually feeding that preprocessed data into a Quantum Machine Learning (QML) simulator. By the end of the program, the student will have mastered essential data science skills for noisy time-series analysis, grasped the physical mechanics linking glacial stick-slip to tectonic faults, and gained a practical, resume-building foundation in hybrid quantum-classical algorithms.

To ensure the student transitions confidently from a learner to an independent researcher, I provide a highly structured mentorship environment. We will hold weekly one-on-one meetings to troubleshoot coding roadblocks, discuss interdisciplinary papers, and keep the project on track. Additionally, the student will be fully integrated into our lab, presenting their progress—both successes and failed experiments—at our bi-weekly group meetings. This regular cadence of presenting and receiving diverse feedback is explicitly designed to build their scientific communication skills, preparing them to confidently deliver their final SURF poster and setting the stage for future graduate school applications or national conference abstracts.

Faculty Mentor: Meenakshi Singh | Physics

Project Abstract: 

Scaling semiconductor quantum computers is currently bottlenecked by the “wiring problem.” Tuning milliKelvin qubits using heavy software ML on room-temperature GPUs requires massive, heat-leaking coaxial bundles. We need to move the “brain” inside the dilution refrigerator. This project bypasses software ML entirely by testing physical neural networks (analog hardware) to tune quantum dots. The student will characterize analog electronic components, interface them with simulated quantum dot tuning models, and benchmark their speed and energy efficiency against classical software. The ultimate goal is proving this hardware is low-power enough to sit in the cryostat. Expect hands-on bench-top electronics work and rapid prototyping.

Student’s role and learning objectives: 

SURF students are treated as junior researchers in my lab. My goal is to get you up to speed quickly and transition you to independent work.

Learning Objectives: You will walk away with highly marketable skills in FPGA programming, hardware description languages (Verilog), hardware-in-the-loop testing, and the device physics of semiconductor quantum dots.

Meetings: We will have a strictly scheduled 1-on-1 meeting every week to troubleshoot code or hardware bugs, review literature, and keep the project moving.

Group Integration: You will attend our bi-weekly lab meetings and present your progress (including your failed experiments—which will happen).

Outcomes: This pattern is designed to force regular scientific communication, ensuring you are fully prepared for your final SURF poster presentation and future grad school applications.

Faculty Mentor: Patrice Genevet | Physics

Project Abstract: 

This project experimentally investigates the implementation and control of structural nonlinearities in photonic platforms for computation. While nonlinear optical effects are traditionally achieved through intrinsic material responses (e.g., Kerr or electro-optic effects), recent advances in nanofabrication enable nonlinear behavior to emerge from device geometry, modal interactions, and engineered feedback—providing scalable and potentially lower-power pathways to optical computing.

The research will focus on the fabrication and characterization of nanophotonic structures—particularly metasurfaces—designed to exhibit geometry-induced nonlinear transfer functions. By engineering resonance detuning, and feedback topology, we will experimentally realize thresholding behavior dynamics suitable for computational primitives such as logic operations, memory elements, and analog function mapping.

Student’s role and learning objectives: 

The student will support the experimental characterization (and maybe some fabrication) of nanophotonic devices exhibiting structural nonlinearities. Responsibilities include assisting with device simulation and layout, participating in cleanroom fabrication processes, setting up and maintaining optical measurement systems (e.g., liquid crystal characterization), and collecting and analyzing nonlinear input–output data. The student will also help compare experimental results with theoretical predictions, contributing to iterative device optimization and preparation of research reports and publications.