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I am a Chicagoland-based, PhD-trained mathematician transitioning into industry data science, with a strong foundation in pure mathematics and hands-on experience in machine learning research. My doctoral training in analysis developed the persistence, abstraction, and structured problem-solving skills required to tackle complex, ambiguous problems—skills that translate directly to modern data science and machine learning workflows.

While my dissertation research focuses on geometric function theory, my recent work centers on applied machine learning. From June 2024 to September 2025, I served as a research aide at Argonne National Laboratory, collaborating with a computational mathematician on a novel bilevel optimization problem in continual machine learning. This project combines theoretical analysis with empirical experimentation (regresssion, classification, and graph classification). You can find the the codebase on GitHub and the paper on arXiv. For an overview of the project and impressive experimental results see here.

Through my work at Argonne, I have developed strong practical skills in Python and modern ML frameworks, including PyTorch, JAX, and Equinox, alongside experience designing reproducible experiments and evaluating model performance. My background in functional analysis and proof-based reasoning has been essential for building the theoretical framework supporting our models, allowing me to bridge rigorous mathematics with applied machine learning. I am seeking data science and ML engineer roles where deep analytical thinking, statistical rigor, and research-driven modeling are valued.