Undergraduate Student Research

STAR Fellowship

Zachary Adams
Class of 2025
Majors: Mathematical Sciences, Physics
Advisor: Burt Tilley - Professor, Mathematical Sciences
Title: Optimal Defect Layer Position in Layered Electromagnetic Energy Absorbers

Abstract: Beamed energy applications require the use of heat exchangers to collect the thermal energy produced from the absorption of electromagnetic radiation. To explore the effects of wave-geometry interactions on heat transfer in resonant systems, we consider a 7-layer susceptor composed of alternating high and low-permittivity lossless dielectric layers, and one layer with a temperature-dependent loss factor. It is irradiated from one side by a plane electromagnetic wave normal to the susceptor and grounded on the other. We consider the system at a thin-domain limit, such that constant temperature is maintained across its width. We show that with an asymmetrically placed defect layer, resonant states produce higher temperatures at lower incident powers and exhibit greater efficiency of energy transfer to the defect layer. We additionally demonstrate transient behavior of the system to show that low-reflection states are attainable in finite time. We additionally show that these states possess high efficiency. Finally, we show that these behaviors can be replicated in an asymmetric 7-layer system composed of titanium dioxide, air and silicon dioxide.

Peter Cancilla
Class of 2026
Major: Computer Science
Advisor: Harmony Zhan - Assistant Professor, Computer Science
Title: State Transfer in Continuous Quantum Walks

Abstract: Quantum walks are fundamental tools in quantum computation. One desired phenomenon of quantum walks is the ability to transmit information from one site to another with high fidelity. In this project, we study perfect state transfer (PST), where such transfer occurs with certainly, and pretty good state transfer (PGST), where the transfer probability gets arbitrarily close to 1.
For continuous quantum walks, it is shown by Godsil that perfect state transfer is rare; on the other hand, there is a polynomial time algorithm, due to Coutinho and Godsil, that decides if a graph admits perfect state transfer. We implemented this algorithm in a python-based programming language known as SageMath and built a database for graphs up to 8 vertices and trees up to 14 vertices that admit PST, as well as those with periodic vertices/cospectral vertices/parallel vertices, which are necessary prerequisites for PST to occur.
Within discrete quantum walks, PST and PGST are much less understood. So far, the study is focused on regular graphs, and very few examples have been found. We extend the theory of PST and PGST to irregular graphs, utilizing a connection between these properties and the spectrum of the normalized adjacency matrix. In particular, we write code in SageMath that determines if a general graph admits PST and construct new infinite families of irregular graphs with this phenomenon.
A goal for our research going forward is to construct a database of graphs that admit discrete PST. Ideally, we will also construct a database for graphs admitting PGST, however doing this will prove to be more of a challenge as the characterization of PGST involves more complicated number theoretic constraints.

Olivia Cava
Class of 2026
Major: Data Science
Advisor: Randy Paffenroth - Associate Professor, Mathematical Sciences
Title: Dynamical Systems Approach for Neural Imaging Data

Abstract: This research is centered around the neural responses of Caenorhabditis elegans to various stimuli. ASH neurons were injected with a protein derived from jellyfish that caused them to illuminate upon activation. The normalized intensity of this luminescence was captured as neural imaging data. The primary objective of this research was to employ and evaluate denoising autoencoders for their effectiveness in classification, noise reduction, and feature importance from this complex and unique dataset. Preliminary findings show the successful classification of stimuli and uncovering of important features all through the use of denoising autoencoders with a dynamical systems approach.

Olivia Dube
Class of 2025
Major: Chemistry
Advisor: Ronald Grimm - Associate Professor, Chemistry & Biochemistry
Title: Air-Free, Molten-Salt Etching of Ti3AlC2 “Yields” Cl-Terminated Ti3C2Clx MXene

Abstract: MXenes are a new class of 2-dimensional conductive layered nanomaterials, about five atoms thick, that hold promising applications in clean energy storage, water purification, electromagnetic shielding, and more. Traditionally, MXene synthesis uses hydrofluoric acid to etch atomic layers out of MAX phase precursor, resulting in mixed –F, –O, and –OH surface terminations. Novel synthetic methods utilize molten-salts in a Lewis-acid reaction yield MXenes with alternative, desirable surface terminations such as chlorine, which creates highly conductive, reactive, and hydrophobic MXenes. To limit oxidative destruction and preserve chlorine terminations, an air-free molten-salt reaction was conducted by sealing copper(II) chloride and MAX powder in a vacuum-sealed tube and cooking at high temperatures with vapor transport. Terahertz spectroscopy was used to confirm the preservation of the delaminated MXene structure. X-ray photoelectron spectroscopy was used to characterize the atoms at the surface of the material. This displayed proof of synthesis and preservation of Cl-terminations through observation of the Ti, C, and Cl regions. The immediate implications of this research are development of air-free delamination procedures of hydrophobic and oxidatively unstable Cl-terminated MXenes the enable preservation of the nanomaterial.

Pegah Emdad
Class of 2026
Majors: Data Science, Bioinformatics & Computational Biology
Advisor: Fabricio Murai - Associate Professor, Data Science
Title: Diagnosing Bias: Predictive AI Models for Identifying Biased Health Information in Medical Curriculum

Abstract: There have been growing concerns around high-stake applications that rely on models trained with biased data, which consequently produce biased predictions, often harming the most vulnerable. In particular, biased medical data could cause health-related applications and recommender systems to create outputs that jeopardize patient care and widen disparities in health outcomes. A recent framework titled Fairness via AI posits that, instead of attempting to correct model biases, researchers must focus on their root causes by using AI to debias data. Inspired by this framework, we tackle bias detection in medical curricula using NLP models, including LLMs, and evaluate them on a gold standard dataset containing 4,105 excerpts annotated by medical experts for bias from a large corpus. We build on previous work by coauthors which augments the set of negative samples with non-annotated text containing social identifier terms. However, some of these terms, especially those related to race and ethnicity, can carry different meanings (e.g., “white matter of spinal cord”). To address this issue, we propose the use of Word Sense Disambiguation models to refine dataset quality by removing irrelevant sentences. We then evaluate fine-tuned variations of BERT models as well as GPT models with zero- and few-shot prompting. We found LLMs, considered SOTA on many NLP tasks, unsuitable for bias detection, while fine-tuned BERT models generally perform well across all evaluated metrics.

Esther Mao
Class of 2026
Majors: Society, Technology, and Policy, Data Science
Advisor: Robert Krueger - Professor, Social Science & Policy Studies
Title: The Importance of Process When Acquiring Data for Machine Learning:
An Examination of Public Perceptions of Governance in Ecuador

Abstract: Researchers in international development, especially those using AI tools, are often agnostic about where their data comes from. This project is a step toward demonstrating the value of data collected through processes found in social science.
The Poverty Stoplight (PSL) method, first developed by Fundación Paraguaya, utilizes an individualized, inductive methodology to create targeted anti-poverty solutions. The survey consists of various indicators, which represent different dimensions of poverty, and are based on input from respondents in each community. Participants rank each indicator as red, yellow, or green, based on whether they feel “very poor”, “poor”, or “not poor” in each category. The dataset includes indicators related to poverty and democracy and trust in institutions.
This research project sought to understand what can be gained from an individualized approach in studying dimensions of democracy using AI tools. By utilizing various machine learning methods on the PSL data from Ecuador, we built a model by isolating indicators tied to democratic norms and perceptions. We then compared our model’s results with other Latin American democracy studies which did not utilize an individualized survey methodology. Our findings showed that when trained on PSL data the ML model demonstrated improved accuracy.

Corbin Narita
Class of 2025
Majors: Mechanical Engineering, Physics
Advisor: William McCarthy - Assistant Professor, Physics
Title: Streamlining the Framework for SPECT Image Reconstruction

Abstract: This research project focused on developing and translating UMass Chan Medical School’s OSEM SPECT reconstruction algorithm from the C language to the python language. This project aimed to create a more accessible, efficient, and well
documented reconstruction algorithm. Throughout the 2024 STAR fellowship, I created detailed documentation for existing C code, enhancing my understanding and ability to optimize the code. I began writing new python code to closely replicate the input and output of the C code. Concluding the fellowship, I have written over 1,000 lines of python code, replicating the functionality of over 3,000 lines of C code. My research is the beginning of a larger project to fully develop the algorithm in python and deploy it for research purposes at UMass Chan Medical School.

Ryan Nguyen
Class of 2025
Major: Computer Science
Advisor: Neil Heffernan - Professor, Computer Science
Title: Creating a Conversational AI Tutor (CAIT)

Abstract: CAIT (Conversational Artificial Intelligence Tutor), is an intelligent tutoring system aiming to leverage generative AI to give a tailored learning experience to struggling students. Our research investigates three primary questions: (1) The usefulness of AI-generated supports (hints, explanations, scaffolding, etc.) for students, (2) The effectiveness of AI tutors compared to human tutors, and (3) What teachers think of using AI tutors in current form. Initial findings suggest that while not replacing teachers, AI tutors can provide effective support when teachers cannot, becoming a useful assistive tool.

Alec Norton
Class of 2026
Majors: Robotics Engineering, Computer Science
Advisor: Erin Solovey - Associate Professor, Computer Science
Title: Validating Neural Circuit Policies for fNIRS Brain Signal Classification

Abstract: As brain-computer interfaces (BCI) advance and become widespread, the demand for low-energy accurate algorithms increases. Presently, high-energy deep learning (DL) is successfully applied to brain signal classification but faces difficulty with noisy data or when generalizing to different users. Is there a low-energy model that can achieve competitive accuracy, noise robustness, and generalization? An interesting candidate is Neural Circuit Policies (NCP): a novel low-cost DL model inspired by the architecture of the C. elegans nematode’s nervous system. NCP consumes less energy and is far smaller than standard DL models but has been shown to be remarkably capable at time-series applications. To validate this for BCI, we constructed a NCP and Convolutional Neural Network (CNN) model for comparison. Using brain signal data collected by fNIRS from Tufts University, we performed a 10-fold cross validation to determine each model’s accuracy on the shuffled dataset and then perturbed the data with noise and recorded the declining accuracy of both models. Finally, to determine generalization, each model was trained on a subsection of the entire dataset and then tested on the entire whole to determine the accuracy for unseen subjects. NCP achieved a 96% accuracy rate and showed competitive noise robustness and generalization with the CNN model. Therefore, NCP seems to be a low-cost competitive alternative to standard DL models, reducing the energy requirement for BCI.

Conner Olsen
Class of 2026
Majors: Computer Science, Math
Advisor: Daniel Reichman - Assistant Professor, Computer Science
Title: Building a Dataset of NP-Hardness Reduction Proofs for Generative AI Applications

Abstract: The study of using techniques such as Generative AI and automated theorem provers in constructing reductions has great potential to benefit both the theoretical understanding of reductions and the development of automated tools in formalizing mathematics. There exist short yet unintuitive proofs of NP-hardness reductions, suggesting that automated discovery of such proofs may be feasible. Many of these proofs, while conceptually complex, can be expressed concisely in formal language or code. This conciseness, combined with the structured nature of reduction proofs, indicates that with appropriate heuristics and search strategies, AI systems could potentially generate these proofs within reasonable computational bounds. By constructing a dataset of solved NP-Hardness reductions, we have provided the means for the application of Generative AI into the field. Such datasets are hard to come by in other domains of mathematics and differ from datasets that are currently used to evaluate the mathematical capabilities of large language models (LLMs). Reductions have significant applicability in STEM undergraduate education. A core tenet of problem-solving is the ability to recognize and establish connections between equivalent approaches or problems. Finally, this work could allow for the existence of AI models that can construct relations, which could automate the discovery of new algorithms.

Brenna Pfisterer
Class of 2025
Major: Psychological Science
Advisor: Erin Ottmar - Associate Professor, Social Science & Policy Studies
Title: Revealing Variations in Math Strategies and Perceptual Structures

Abstract: Creativity and strategic thinking are foundational for effective problem-solving, particularly in mathematics, where the ability to navigate between divergent and convergent thinking can influence solution and learning outcomes. In mathematics, creativity extends beyond finding correct answers; it encompasses exploring multiple pathways, uncovering novel approaches, and identifying connections that may otherwise be overlooked. This STAR research outlines a study design that investigates the variations in mathematical problem-solving strategies and perceptual structures among undergraduate students using Graspable Math. GM is a digital tool designed to help students learn and interact with mathematical concepts in a hands-on way. On this platform, the steps and behaviors of participants can be tracked as they solve problems. The proposed study designed over the course of the summer aims to classify and visualize diverse solving strategies, with a focus on understanding how problem types, structured similarly but with different goals and instructions, influence the use of divergent versus convergent problem-solving approaches. The mathematical problems were created with distinct instructions and three variations of solution states. I propose that approximately 100 undergraduate students recruited through the WPI SONA pool participate in this 45 minute online study. This study will use a 2 x 3 factorial design and multilevel modeling. Additionally, the use of multi-level modeling will allow for examination of variance due to problem variations vs variance due to the individual students. The proposed research is to be continued as an MQP for the 2024-2025 academic year.

Srisaranya Pujari
Class of 2026
Majors: Physics, Data Science
Advisor: Raisa Trubko - Assistant Professor, Physics
Title: Pulsed Quantum Diamond Magnetometry

Abstract: With the emergence of quantum sensors such as the Quantum Diamond Microscope (QDM), we can now image magnetic fields. The QDM uses Nitrogen-Vacancy centers within a diamond to study geological samples, biological samples, and novel materials. Of the different schemes for the QDM, a pulsed measurement protocol offers many advantages. Pulsing the laser leads to faster data acquisition times and higher contrast in our measurements. We also find reduced heating of our samples, which helps us prevent sample burning. In this project, we built a pulsed QDM. We demonstrate this with Rabi Oscillations and a pulsed Optically Detected Magnetic Resonance (ODMR) spectrum for five different Nitrogen-Vacancy Diamonds.

Ronak Wani
Class of 2026
Major: Computer Science
Advisor: Matthew Ahrens - Assistant Teaching Professor, Computer Science
Title: Grounded Theory-Driven Software Solutions for Advising

Abstract: This research explores challenges in academic advising. These issues contribute to student stress. To address these challenges, we propose AI-integrated solutions for detailed academic planning. These innovations aim to improve advising quality, support, and enhance student success in higher education.

Tianxing Weng
Class of 2026
Major: Physics
Advisor: Kun-Ta Wu - Associate Professor, Physics
Title: Active cavity flow with a Hybrid Lattice Boltzmann Method

Abstract: The lid-driven cavity flow system as a benchmark problem in fluid mechanics, is well-known to develop turbulence at high Reynolds number in response to the external stimuli, yet active fluid being internally driven, exhibits turbulence even at very small Reynolds number. In our study, we numerically investigate the competition between external and internal driving and the so-induced transition from disordered states to ordered states via a hybrid lattice Boltzmann method in collaboration with experiments. A transition in flow patterns and the velocity-velocity correlation length is observed in numerical simulations in consistency with experimental observations.

LaPre Fellowship

Grace Baumgartner
Class of 2025
Majors: Chemistry, Mathematics
Advisor: Ronald Grimm - Associate Professor, Chemistry & Biochemistry
Title: Cationic Pollutants Adsorb Reversibly to 1DL Surfaces

Abstract: To effectively remove industrial pollutants from waterways, new materials are needed. Titania-based one-dimensional lepidocrocite (1DL) is a promising candidate for this application due to its inexpensive synthesis and its photocatalytic ability. Furthermore, due to its anionic terminations, 1DL readily adsorbs cations from solution, changing its interlayer spacing. Crystal violet and methylene blue, two visible-light dyes and proxies for cationic organic pollutants, are adsorbed by as-synthesized 1DL, forming a complex with emergent electronic behavior. Now, it is shown that this adsorption can be reversed by the addition of LiCl salt, freeing the dye from solution and rinsing the material for future reprocessing. This demonstrates how 1DL’s varying affinities for different cations can be leveraged to optimize its performance as an adsorbent. Special thanks to David LaPré for funding this summer.

Trevor Bush
Class of 2025
Majors: Biotechnology, Biochemistry
Advisor: Pamela Weathers - Professor, Biology & Biotechnology
Title: Validating Methodology for MMP3 and Collagen
Detection and Quantification in Dermal Fibroblasts

Abstract: Fibrosis is pathological healing process through non-regenerative mechanisms and leads to scar formation. Approximately 30% of deaths are attributed to fibrosis. Here methods for detecting and quantifying pro and anti-fibrotic markers in vitro, such as collagen and MMP3 proteins respectively, were tested for their validity in determining the efficacy of the therapeutics dihydroartemisinin, previously shown to upregulate MMP3 and downregulate a-SMA. Human dermal fibroblasts (HDF) were pre-treated with two doses of ±TGF-b (10 ng/mL), seeded into 24 well plates and treated with DMSO control (0.1% v:v) or dihydroartemisinin (DHA, 50 μM). Media was collected and cells were fixed after 4 days and 10 days with either a one or two dose DHA treatment. An ELISA was used to quantify MMP3 in the media. Sirius Red/Fast Green FCF dyes were used to determine collagen and total protein concentrations in media and fixed cells. Immunocytochemistry (ICC) was used to observe a-SMA and collagen I protein expression and cell nuclei were localized with Hoechst. By the 10th day of 1 dose of DHA, a-SMA decreased, and collagen fibrils were undetectable in cells compared to DMSO controls. MMP3 significantly increased in the media of both 1 and 2 dose DHA treated cells after days. Although collagen in cells decreased with 2 doses of DHA after 10 d, relative to total protein there was no change. Unfortunately, the Sirius Red/Fast Green FCF gave false positive results indicating it was unreliable. Together results showed that most methods were valid, but that collagen assay of the media requires improvement.

Jillian Crandall
Class of 2025
Majors: Biotechnology, Biochemistry
Advisor: Ronald Grimm - Associate Professor, Chemistry & Biochemistry
Title: Solving the Kek1/EGFR Binding Pocket Puzzle

Abstract: The Epidermal growth factor receptor (EGFR) is a receptor tyrosine kinase, whose activation controls cell proliferation, survival, migration, and cell fate determination¹. As such, activating mutations to EGFR is associated with many cancers, including breast, brain, lung¹. Therapeutic approaches typically involve molecules designed to inhibit the receptor². Kekkon1 is an inhibitor of Drosophila EGFR (dEGFR) and one of a family (Kek) of six transmembrane molecules in Drosophila¹³. Interestingly, within the family only Kek1 inhibits the Drosophila receptor and the extracellular and transmembrane regions have been identified as the domains required for inhibition⁴. The extracellular region consists of N-insert, seven LRRs, flanked by cysteine rich
domains and an Ig domain¹. While the importance of the LRRs to Kek1’s ability to bind the receptor have been established, key questions remain. Are the cysteine rich flanking regions involved in binding, what LRR residues within the predicted binding pocket drive specificity of the interaction, and is the N-insert required for inhibition in vivo?

Connor Doran
Class of 2026
Major: Chemistry
Advisor: Shawn Burdette - Professor, Chemistry & Biochemistry
Title: Synthesis and Characterization of a Quinoline Based Zinc Photocage Designed for Red-Shifted Absorption

Abstract: Zinc is an important metal that is utilized throughout the body for a variety of tasks. In the brain, zinc is an important neurotransmitter where an imbalance correlates to neurological disorders such as Parkinson’s and Alzheimer’s. However, due to having a full valence orbital, it is difficult to monitor zinc pathways using optical spectroscopy. Cell permeable photocages that selectively chelate to zinc ions are useful tools for the controlled release of zinc in cell assays. Optimally, these cages should be activated by low-energy light to reduce cell damage. 8-aminoquinoline (8AQ) was utilized to red-shift the activation wavelength of a novel zinc photocage. The synthesis of 8AQdeCage was optimized and partially characterized, displaying a bathochromic shift of the 𝜆max to 346 nm when compared to previous cages.

Manning/Leser Fellowship

Leah Maciel
Class of 2025
Majors: Bioinformatics & Computational Biology
Advisor: Luis Vidali - Professor, Biology & Biotechnology
Title: Bioinformatics driven analysis of Arl8 structure and myosin interactions in Physcomitrium patens

Abstract: This project focused on two proteins in the moss model organism P. patens: Arl8, a small GTPase involved in vesicle trafficking, and myosin XI, a motor protein that facilitates intracellular transport. Despite the importance of Arl8 in cellular processes, the 3D structure of P. patens Arl8 and its interactions with the cargo binding domain of MyoXI have not yet been experimentally determined. Understanding these structures and interactions in P. patens could provide insight into fundamental processes such as vesicle transport and plant growth. By predicting the 3D structure of Arl8 based on its amino acid sequence, this project aims to elucidate its structure and potential interaction sites between Arl8 and MyoXI to identify key amino acids in the interaction. A multiple sequence alignment and phylogenetic tree of Arl8 sequences across organisms were generated using Geneious to determine that Arl8 is a conserved protein; hence structural information about other Arl8s can be used to inform the P. patens structure. The predictive software tools SWISS-MODEL, Alphafold3, and Cluspro were then used to predict the structure of Arl8 and generate models of Arl8 with MyoXI. These tools were first tested using amino acid sequences of MyoXI with Rab-E14, a known interaction in P. patens. The models were then visualized using UCSF Chimera, and residues involved in the interaction were identified. A potential model of MyoXI, Arl8 and Rab-E14 was also generated using the same methods. These results yielded predictive models of Arl8 and its interactions with MyoXI and Rab-E14. Future steps include experimentally confirming these structures and the key residues in the interactions.

Neuroscience Fellowship

Vishali Baker & Amanda Shea
Class of 2025
Majors: Biomedical Engineering, Professional Writing
Advisor: Benjamin Nephew - Associate Research Professor, Biology & Biotechnology
Title: Multimodal (fNIRS and fMRI) Neuroimaging On Resting State Functional
Connectivity

Abstract: Resting-state functional connectivity (RSFC) is a measure of temporal correlation in the absence of an event or stimuli. The most common technique to analyze these networks is through functional magnetic resonance imaging (fMRI). While this method provides reliable, insightful data, it has inherent limitations. In recent studies, however, data suggests that an alternative modality known as functional near-infrared spectroscopy (fNIRS) may offer a unique opportunity to investigate brain functionality and whole brain connectivity by proxy. This study analyzes multimodal (fNIRS & fMRI) neural hemodynamics data during resting-state for future data collation and applications in whole brain RSFC research. For analysis, a sample set of seven participating healthy individuals over age 18 underwent multimodal neuroimaging utilizing both fMRI and fNIRS imaging techniques simultaneously. Both the fNIRS and fMRI data were successfully processed and analyzed to derive functional connectivity metrics during resting state to observe neural activity in the absence of an event or stimuli. The resulting metrics indicated spontaneous increase in hemoglobin in the four valid samples identified, which aligned with existing literature and expectations of resting state neural activity. The data points garnered from the functional connectivity maps will be used to compare the brain activity between fNIRS and fMRI. These results will be used to determine if the cortical data from fNIRS are indicative of deep brain fMRI data connectivity networks.

NSF/CAREER Student (Farny Lab)

Aleksandra Maak
Class of 2027
Majors: Computer Science & Bioinformatics
Advisor: Natalie Farny - Assistant Professor, Bioinformatics & Computational Biology
Title: Uncovering the Effect of Tetracycline Contamination on the Survival of P. putida in Soil

Abstract: Worldwide, agricultural activity is boosted with tetracyclines, a group of antibiotics used as growth promoters. Their accumulation in soil is a potential threat to microbial communities and, thus, to whole soil ecosystems. Such compromised soil poses global environmental and health risks. There are bacterial biosensors in place to measure the levels of tetracycline. Our goal is to measure the survival of cells that have biosensors for tetracycline under sterile soil conditions. Our basic method is to use Colony Forming Unit assays and 16S DNA sequencing to monitor bacterial populations and the effects of the contaminant on the soil microbiome. We expect to better understand the pattern of survival and persistence of contaminated soil microbiomes. This work will help inform the design of biosensing bacteria for soil applications.

Samuel Darer
Class of 2024
Chemistry
Advisor: Ron Grimm, Associate Professor - Chemistry & Biochemistry

Bella DeCilio
Class of 2025
Biochemistry
Advisor: Arne Gericke, Interim Dean of Undergraduate Studies

Sona Hanslia
Class of 2025
Physics
Advisor: Raisa Trubko, Assistant Professor - Physics

Ezra Yohay
Class of 2025
Physics
Advisor: Qi Wen, Associate Professor - Physics

Keelan Boyle
Class of 2025
Robotics Engineering
Environmental Sustainability
Advisor: Berk Calli, Assistant Professor - Robotics Engineering

Kylar Foley
Class of 2024
Physics
International and Global Studies
Advisor: Rob Krueger, Professor & Department Head - Social Science and Policy Studies

Max Seager
Class of 2025
Biochemistry
Advisor: Inna Nechipurenko, Assistant Professor - Biology & Biotechnology

Eva Pestschek
Class of 2023
Psychological Science
Biology & Biotechnology
Advisor: Richard Lopez, Assistant Professor - Social Science and Policy Studies

Jessica Liano
Class of 2024
Interactive Media and Game Development
Advisors: Ed Gutierrez, Assistant Professor - Humanities & Arts
Farley Chery, Associate Professor of Teaching - Interactive Media & Game Development

Michael Gatti
Class of 2024
Computer Science
Advisor: Walt Yarborough, Professor of Practice - Interactive Media & Game Development

Tom Bryon
Class of 2024
Computer Science
Robotics Engineering
Advisor: Walt Yarborough, Professor of Practice - Interactive Media & Game Development
 

Lauren Abraham

Class of 2024

 Biology & Biotechnology

Advisor: Professor Natalie Farny - Biology & Biotechnology

Abigail Boafo

Class of 2024

Society, Technology & Policy

Advisors: Professor Crystal Brown & Professor Hermine Vedogbeton - Social Science & Policy Studies

Sydney Gardner

Class of 2023

Interactive Media & Game Development

Advisor: Professor Farley Chery - Interactive Media & Game Development

Thomas Kneeland

Class of 2024

 Computer Science/Music

Advisor: Professor Ben Young - Director of Jazz History Database

Daniel Larabee

Class of 2023

Bioinformatics & Computational Biology

Advisor: Professor Scarlet Shell - Biology & Biotechnology

Cole Parks

Class of 2024

Robotics Engineering/Computer Science

Advisor: Professor Carlo Pinciroli - Robotics Engineering 

Allison Rozear

Class of 2024

Major:  Human and Machine Communication 

Advisor: Professor Yunus Doğan Telliel - Humanities and Arts

Rachel Swanson

Class of 2023

Chemistry and Chemical Engineering 

Advisor: Professor Patricia Zhang Musacchio - Chemistry and Biochemistry

Camille Williams

Class of 2025

Major: Mathematics / Physics

Advisor: Professor Vadim Yakovlev - Mathematics

Clare Boothe Luce Research Scholars

Alexandra Auteri

Class of 2020

Mathematical Sciences

Mentor & Research Advisor: Sarah Olson​

Alexis Buzzell

Class of 2020

Physics

Mentor & Research Advisor: Lyubov Titova

Olivia Gulezian

Class of 2020

Mathematical Sciences

Mentor & Research Advisor: Suzanne Weekes​​

Fareya Ikram

Class of 2020

Computer Science

Mentor: Suzanne Weekes

Research Advisor: Gillian Smith

Leah Mitchell

Class of 2020

Mathematical Sciences

Mentor: Suzanne Weekes

Research Advisor: Andrea Arnold

​Erin Morissette

Class of 2019

Physics

Mentor: Lyubov Titova

Research Advisors: Ron Grimm & Lyubov Titova

MaryAnn VanValkenburg

Class of 2019

Computer Science

Mentor: Suzanne Weekes

Research Advisor: Dan Dougherty

Bryannah Voydatch

Class of 2019

Physics

Mentor & Research Advisor: Lyubov Titova

Karitta (Kit) Christina Grand Zellerbach

Class of 2019

Computer Science

Mentor & Research Advisor: Carolina Ruiz

Olivia Atkins
Class of 2023
Biology & Biotechnology
Advisor: Scarlett Shell, Assistant Professor of Biology & Biotechnology

Eugena Choi
Class of 2023
Environmental and Sustainability Studies & Environmental Engineering
Advisor: William San Martin, Assistant Teaching Professor of Humanities & Arts

Elizabeth Koptsev
Class of 2022
Psychological Science
Advisor: Angela Rodriguez, Assistant Professor of Social Science & Policy Studies 

Brock Jolicoeur
Class of 2022
Physics
Advisor: David Medich, Associate Professor of Physics

Michelle Pan
Class of 2022
Biology & Biotechnology
Advisor: Inna Nechipurenko, Assistant Professor of Biology & Biotechnology

Mohammed Mohammed 
Class of 2022
Chemistry & International Studies
Advisor: Crystal Brown, Assistant Professor of Social Science & Policy Studies

Victoria Mirecki
Class of 2022
Advisor: Gillian Smith, Associate Professor of Computer Science

Katie Housekeeper
Class of 2023
Advisor: Elke Rundensteiner, Professor of Computer Science

Sarah Tarantino
Class of 2021
Biology & Biotechnology
Advisor: Jagan Srinivasan, Associate Professor of Biology & Biotechnology

Nicholas Tourtillott
Class of 2022
Bioinformatics & Computational Biology
Advisor: Liz Ryder, Professor of Biology & Biotechnology

Jocelyn Mendes
Class of 2021
Chemistry
Advisor: Ronald Grimm, Associate Professor of Chemistry & Biochemistry

Zhifei Ma
Class of 2022
Mathematical Science & Computer Science
Advisor: Min Wu, Assistant Professor of Mathematical Sciences

Brady Jeong
Class of 2022
Physics
Advisor: Doug Petkie, Professor & Department Head of Physics

Benjamin Lunden
Class of 2022
Physics
Advisor: Izabela Stroe, Associate Professor of Teaching

Jialin Song
Class of 2021
Robotics Engineering & Computer Science
Advisor: Loris Fichera, Associate Professor of Robotics Engineering

Alisionna Iannacchione
Class of 2021
Psychology
Advisor: Erin Ottmar, Associate Professor of Social Science & Policy Studies

Constantina Gatsonis
Class of 2021
Psychology
Advisor: Angela Rodriguez, Assistant Professor of Social Science & Policy Studies

Mariko Endo
Class of 2022
IMGD
Advisor: Jennifer DeWinter, Professor of Arts, Communications, & Humanities

Tyler Marcus
Class of 2022
IMGD
Advisor: Jennifer DeWinter, Professor of Arts, Communications, & Humanities

Olivia Hunker
Class of 2020
Chemistry
Advisor: Arne Gericke, Professor and Department Head of Chemistry & Biochemistry

Nicole Jutras 
Class of 2021
Psychology and Computer Science
Advisor: Jeanine Skorinko, Professor of Psychology

Daniel McDonough
Class of 2020
Computer Science and Bioinformatics & Computational Biology
Advisor: Amity Manning, assistant professor of biology & biotechnology

Julia Noel 
Class of 2021
Chemistry & Society, Technology & Policy
Advisor: Anita Mattson, Associate Professor of Chemistry & Biochemistry

Dung Pham 
Class of 2020
Physics and Electrical & Computer Engineering
Advisor: L. Ramdas Ram-Mohan, professor of physics

Annalise Robidoux
Class of 2020
Biology & Biotechnology and Chemistry & Biochemistry
Advisor: Jagan Srinivasan, Associate Professor of Biology & Biotechnology

Megan Varney
Class of 2021
Mathematical Sciences
Advisor: Kun-Ta Wu, Assistant Professor of Physics

Hannah Kraus
Class of 2018
Mathematical Sciences
Mentor & Research Advisor: Sarah Olson
 
Caroline Johnston
Class of 2019
Mathematical Sciences
Mentor: Suzanne Weekes
Research Advisor: Andrew Trapp
 
Toni Joy
Class of 2019
Mathematical Sciences
Mentor and Research Advisor: Suzanne Weekes

Erin Morissette
Class of 2019
Physics
Mentor: Lyubov Titova
Research Advisors: Lyubov Titova and Ron Grimm
 
Sierra Palmer
Class of 2019
Robotics Engineering
Mentor: Carolina Ruiz
Research Advisor: Carlo Pinciroli
 
Aline Tomasian
Class of 2018
Physics
Mentor: Lyubov Titova
Research Advisor: Izabela Stroe
 
MaryAnn VanValkenburg
Class of 2019
Mathematical Sciences and Computer Science
Mentor & Research Advisor: Carolina Ruiz
 
Sarah Ma
Class of 2018
Mathematical Sciences
Mentor & Research Advisor: Sarah Olson

Shannon Feeley
Class of 2017
Mathematical Sciences
Mentor: Suzanne Weekes, Professor of Mathematical Sciences

Research Project: Search and Rescue Planning: When a search and rescue incident occurs, it is imperative to find survivors as quickly as possible.  The uncertainty in the survivors' location usually increases with time, and their likelihood of survival decreases with time.  This project will research the methods that are used to identify the most efficient way to maximize the likelihood of locating survivors.  

Katie Gandomi
Class of 2017
Robotics Engineering
Mentor: Carolina Ruiz, Associate Professor of Computer Science

Research Project: Autonomous Delivery with Unmanned Aerial Vehicles: As e-commerce companies like Amazon and Ebay grow, there is a demand to have products delivered from factories into the hands of customers faster than ever. With the help of autonomous quadrotor transport, packages could be at your doorstep within hours as small drones are deployed and organized into a complex network of delivery-robots.

In this research project, the mechanical, electrical and software aspects of this problem are explored as well as the artificial intelligence and machine learning behind the master control unit that organizes and deploys the robots.     

Amanda Leahy
Class of 2018
Physics
Mentor: Lyubov Titova, Assistant Professor of Physics

Research Project: Use of Gafchromic Film for Brachytherapy Source Characterization: This project will investigate the use of Yb-169 in High Dose Rate brachytherapy using Gafchromic film. The Gafchromic film will be used to measure the radiation output of Yb-169. The results will be compared to Ir-192, currently the most common isotope used in brachytherapy. 

Holly Nguyen
Class of 2018
Computer Science
Mentor: Carolina Ruiz, Associate Professor of Computer Science

Research Project: Personalized Computational Tools to Foster Better Sleep Habits in College: This research project involves the design, implementation and use of algorithms and computational tools in a mobile app to improve sleep behavior in college students. The app enables users to track their sleep schedule (as well as caffeine intake and exercise), receive graphical feedback and tailored advice based on personality and chronotype, and adopt healthier sleep behaviors.

The project covers a wide range of computational aspects (including the design and implementation of mobile apps, data mining and predictive analytics), as well as medical and psychology aspects (including healthy behaviors, personality types, behavioral change, feedback and interventions).

Aline Tomasian
Class of 2018
Physics
Mentor: Lyubov Titova, Assistant Professor of Physics

Research Project: Structural changes and the movement of proteins in the aqueous cellular environment play an essential role in biological processes. This research project will use spectroscopic techniques to uncover a complete picture of protein dynamics, focusing specifically on amyloidogenic proteins related to Alzheimer's Disease and Type II Diabetes.  

Hope Wallace
Class of 2018
Computer Science
Mentor: Kathi Fisler, Professor of Computer Science

Research Project: Predicting Exergame Enjoyment: This project aims to create a recommendation system for mobile exercise games (exergames) in order to encourage people to continue playing them and therefore lead healthier lifestyles. The first phase of this project will create a taxonomy for mobile exergames and create a questionnaire to measure exergame enjoyment.  

Natalie Wellen
Class of 2017
Mathematical Sciences
Mentor: Suzanne Weekes, Professor of Mathematical Sciences

Research Project: Systemic Risk Analysis of the OTC Market: Some of the major questions in the financial industry today are what are the next regulations going to be and how will they affect the markets? The goal of this research is to create a model of the Over the Counter Derivatives Market, and specifically to apply Central Clearing Parties to this model, a form of regulation imposed in the Dodd-Frank Act.