I am a PhD student in Bioinformatics and Computational Biology at Worcester Polytechnic Institute, where my research combines mathematical modeling, machine learning, and computational biology to study neuroinflammation following ischemic stroke. My work focuses on developing data-driven and mechanistic models of biological systems, particularly under conditions where experimental data are sparse, noisy, or irregularly sampled. I am especially interested in how prior biological knowledge can be incorporated into statistical and dynamical models to improve inference, prediction, and interpretability.

My background is in mathematics and biology, with experience spanning computational modeling, statistical analysis, and public health survey research. Before beginning doctoral work, I worked on large-scale health and demographic survey projects involving data processing, imputation, and reproducible statistical workflows in R and Python. These experiences shaped my interest in uncertainty quantification, missing data methods, and the challenges of extracting reliable scientific insight from imperfect real-world datasets.

My current research explores the intersection of Gaussian Process modeling, sparse system identification, and multiscale biological modeling. In particular, I investigate how probabilistic surrogate models can be used to improve equation discovery methods such as SINDy in settings with extremely limited biological observations. I also study computational models of post-stroke neuroinflammation, with interests in aging, white matter and gray matter differences, blood-brain barrier dynamics, and translational gaps between rodent and human stroke studies.

Beyond research, I am interested in scientific communication and quantitative education. I have worked extensively as a tutor and enjoy translating technical ideas into accessible explanations for broader audiences. My broader research interests include dynamical systems, uncertainty quantification, scientific machine learning, interpretable AI, mathematical biology, Bayesian modeling, and computational neuroscience.