I am interested in principled approaches to machine learning with focus on data-driven decision making, deep learning foundations, and heterogeneous data. My research integrates optimization methods (specifically convex and first-order) and statistical learning theory to design efficient algorithms/architectures that address these data-science problems.
Additional Information
How did you end up where you are today?
I obtained my PhD degree from Caltech in 2015 where I received a Charles Wilts Prize for the best departmental thesis. During postdoc, I was at UC Berkeley as a Simons Fellow. After spending few years in industry, I joined UC Riverside where I received NSF CAREER and Google Research Scholar Awards. Starting Fall 2023, I will be joining EECS department at U-M.
An interesting fact: The elegance of mathematics has always amazed me and led me to participate in International Math Olympiad.
Also, my Erdos number is 3!
Accomplishments and Awards
- 2023 Propelling Original Data Science (PODS) Grant Award: Foundations of Sequence Models for Learning, Estimation, and Control of Dynamical Systems.