Interns

Research Project 1 

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Erica Li 
Department of Statistics
Oregon State University

Xinyu Li is a second-year PhD student in the Department of Statistics at Oregon State University. She received her B.S. in Mathematics and Economics from the University of California, Los Angeles. 

Her current research, conducted within RP1, focuses on using neural networks to predict molecular binding regions, particularly for antigen–antibody interactions.

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Emily Thomas 
Department of Bioinformatics and Genomics 
University of North Carolina at Charlotte
 
Emily Thomas is a master’s student in Bioinformatics at UNC Charlotte with interests in computational biology, machine learning, and immunology. Through MISM, she is studying interactions between neutralizing antibodies and the SARS-CoV-2 spike protein receptor-binding domain. She is particularly interested in understanding antibody binding, viral escape, and how computational modeling can contribute to therapeutic and vaccine development. Emily hopes to pursue a career in clinical and translational bioinformatics focused on applying computational methods to improve human health.

Research Project 2 

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Henry Uddyback 
Department of Statistics
North Carolina State University

Henry Uddyback is a PhD student in Statistics at North Carolina State University. He has worked on statistical methods that introduces uncertainty quantification to cell-cell communications in spatial transcriptomic data sets.  His research interests are spatial transcriptomics, Gaussian processes, Bayesian methods, and spatial statistical methods.

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Maggie Zhang 
Department of Statistics
Duke University

My research focuses on mathematical and computational modeling of immune dynamics. I hope to collaborate across scales—from molecular binding to cell and tissue behavior—to build integrative models that connect biophysical mechanisms with system-level immune responses.

Research Project 3 

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Montse Torres Garcia 
Department of Mathematics
University of North Carolina at Charlotte

Montse Torres Garcia is a PhD candidate in Applied Math at UNC Charlotte. Montse’s research focuses on multi-type branching processes with applications to infectious disease modeling, particularly malaria parasite population dynamics. Her mathematical background is rooted in stochastic processes and linear algebra, including spectral theory, and differential equations modeling. 

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Lan Trinh
Department of Mathematics
Tulane University 

Lan Trinh is a Ph.D. candidate in the Department of Mathematics at Tulane University. Her research lies at the intersection of mathematical modeling, statistical methodology, and biological applications. She is particularly interested in developing quantitative frameworks that integrate mathematical theory, statistical inference, and scientific data to better understand complex biological systems.