|
Current: I am currently an Assistant Professor at Cornell Tech and a field member of Computer Science, Operations Research and Information Engineering, Statistics and Applied Math, at Cornell University. I am interested in both theoretical and applied aspects of statistical machine learning and data science and I enjoy building statistically and computationally efficient strategies for data- and computation-driven personalized decision-making with several applications in healthcare. En route, I develop theory and methods spanning the areas of causal inference, reinforcement learning, Bayesian inference, optimization, and high-dimensional statistics.
Postdoc and Ph.D.: Prior to Cornell, I was fortunate to be FODSI postdoc fellow and advised by Prof. Susan Murphy in the Departments of Computer Science and Statistics at
Harvard, and Prof. Devavrat Shah in the Laboratory of
Information Decision and Systems (LIDS), Department of
EECS at MIT from 2021 to 2023. I finished my Ph.D. in Summer 2021 from the Department
of EECS at the UC Berkeley where I was fortunate to be advised by Prof. Martin
Wainwright and Prof. Bin Yu.
My thesis committee members included Prof. David Aldous and Prof. Peter Bartlett. I was also fortunate to work with several collaborators, including Prof. Michael Jordan at UC Berkeley, Lester Mackey at Microsoft Research (MSR), and Prof. David Madigan at the Northeastern University. At MIT, I am also associated with IDSS, and at Berkeley, I was also associated with the Berkeley Laboratory for Information and System Sciences (BLISS), and the Berkeley Artificial Intelligence Research group (BAIR).
|