I am an incoming Ph.D. student at UChicago Data Science Institute (DSI), and a second-year M.S. Statistics student advised by Prof. Daniel Sanz-Alonso. I am also a part of the AER Lab at Brown under the mentorship of Prof. Sebastian Musslick.

My research interests seek to answer two questions: (1) how to leverage probablistic thinking, e.g. prior knowledge and decision theory, to build more robust and versatile machine learning models (Probablistic ML)? (2) how do we allow efficient inferences on these models when faced with large-scale real world data (Approximate Inference)?

Previously, I graduated from UC Berkeley with double majors in Stat & CS. My academic journey started with working alongside Prof. Allen Yang and Prof. Aditya Guntuboyina. A boring but extended bio about my life before college is at here.

For current/prospective students having questions about M.S./Ph.D. applications in Statistics/DS, I plan to spend an hour every week doing online Q&A. Feel free to book my time here; for current UChicago undergrad/master interested in my variational inference reading list, you can contact me for potential reading program.