About Me
I'm a Biomedical Engineering student in the Honors Program at the University of Minnesota, with an interest in the intersection of computational biology and machine learning. Currently at the Hackel Lab, I'm building StructVar-Bench, a pathogenicity prediction tool examining missense mutations; it combines structural biology, graph neural networks, and large-scale bioinformatics.
Previously, I designed a hybrid brain-computer interface that earned 1st place at the Twin Cities and Minnesota State Regional Science Fair and qualified for ISEF. Outside the lab, I've contributed 300+ volunteer hours, consulted for Thermo Fisher Scientific, and worked as a copy editor sharpening journalistic writing.
I'm passionate about developing innovations where biology and engineering intersect that can make a real difference in people's lives.
Experience
Hackel Lab
Student Researcher · University of MinnesotaBuilding StructVar-Bench: a multi-modal structural dataset and benchmark for missense variant pathogenicity prediction. Integrating ClinVar, UniProt, and AlphaFold data across ~89,000 variants and training graph neural network models with PyTorch Geometric.
Minnetonka Research Program
Student Researcher · Independent ResearchDeveloped a hybrid brain-computer interface combining EEG mental commands with eye-tracking for adaptive assistive robotic control. 1st Place in Biomedical Engineering at the Twin Cities Regional Science Fair; ISEF qualifier.
Thermo Fisher Scientific
Student Consultant · VANTAGE ProgramDelivered a cybersecurity strategy report for Thermo Fisher's biomedical product line. Conducted primary and secondary research on consumer preferences concerning cybersecurity in biomedical products.
Peak Performance Twin Cities
Student Consultant · VANTAGE ProgramProvided data-driven hiring recommendations through quantitative and qualitative analysis. Collaborated with team members to present findings and deliver well-rounded strategic solutions.
Research
StructVar-Bench
Hackel Lab · University of Minnesota
A multi-modal structural dataset and benchmark for missense variant pathogenicity prediction. Built a dataset of ~89,000 missense variants by integrating ClinVar clinical labels, UniProt annotations, and AlphaFold protein structures. Modeled energetic impact of mutations via large-scale FoldX ΔΔG calculations. Engineered graph representations of local protein environments and trained GNN models achieving strong predictive performance.
Hybrid Brain-Computer Interface
Minnetonka Research Program · Independent Research
Hybrid brain-computer interface with continuous learning and eye-tracking for adaptive mental command control. Developed a hybrid model integrating EEG-based mental command training with eye-tracking to improve accuracy and adaptability in assistive robotic control. Competed at Twin Cities and Minnesota State Science & Engineering Fairs.