I’m a PhD student in Statistics at Carnegie Mellon University where I am lucky to be advised by Aaditya Ramdas.
Before coming to CMU, I studied math and statistics at the University of Waterloo in Canada.
I am broadly interested in statistical theory and methodology under weak assumptions, often in adaptive and sequential settings. For example, I focus on
- anytime-valid sequential inference,
- nonparametric methods,
- causal inference,
- reinforcement learning, and
- differential privacy.