Applied Mathematics | Scientific Machine Learning | Biostatistics & Causal Inference 

About

I am a Ph.D. candidate in Applied Mathematics at Kent State University, advised by Dr. Omar De La Cruz Cabrera. My research spans scientific machine learning, biostatistics, inverse problems, dynamical systems, causal inference, uncertainty quantification, and biomedical artificial intelligence.

My work develops along two complementary directions. One focuses on reliable statistical learning for biomedical and population-health applications, including Bayesian uncertainty quantification, interpretable clinical prediction, external validation, causal inference, and transportability. The other focuses on scientific machine learning for inverse problems and dynamical systems, including physics-informed neural networks, sparse and time-varying network discovery, stochastic modeling, epidemiological systems, and computational imaging.

Across these areas, I am interested in developing mathematically principled and computationally scalable methods that recover hidden structure, quantify uncertainty, improve generalizability, and support scientifically meaningful decision making.