Math & ML for Safer Technology and Better Medicine

Thanks for your interest in my work! I am a PhD student in the Department of Mechanical and Aersopace Engineering at UC San Diego. I do research in control theory, a branch of applied math that is used throughout engineering and is closely related to reinforcement learning. My goal is to leverage control theory and reinforcment learning to advance medicine, biology, and technology.

I am grateful to be advised by Dr. Sylvia Herbert and Dr. Boris Kramer. I am also grateful to the various programs that have supported my work: the NIH Intramural Research Training Award, the NSF Graduate Research Fellowship Program, the NIH-UCSD Interfaces Training Grant, and the ARCS Foundation Scholarship.

Prior education and training

I recieved my bachelor’s degree in biomedical engineering from Johns Hopkins University with a focus in computational biology. Afterwards, I did research in computational biology at the National Institutes of Health for two years, and I subsequently obtained my master’s degree in biological engineering from MIT. While at MIT, I did research at the intersection of biology and control theory, leading to my decision to do a PhD in the latter.

Why control theory?

We live in a world increasingly reliant on “black-box” algorithms. They are already being used in self-driving cars and robotics, and they are progressively becoming more central to bioscience and clinical decision-making. While these technologies can be highly performant, they often fail in unpredictable and perplexing ways (ever seen a chat-bot hallucinate?).

I want to help make our algorithms safer. Doing so involves a lot of math and computer science, but also a lot of domain knowledge, so I have to (really get to) speak many languages to do my work. I also love my research because on a day-to-day level, it involves solving interesting puzzles and close collaboration with many amazing colleagues.