Sharan Sahu

I am a first-year PhD student in Statistics and Machine Learning at Cornell University. Broadly, I am interested in statistical machine learning and using statistical/machine learning tools to improve decision-making and bridge the gap between machine learning methodology and practice by making these methods more reliable, human-compatible, statistically rigorous, and deployable. I am fortunate to be supported by a Cornell University Fellowship

Before joining Cornell University, I was an undergraduate at UC Berkeley where I studied Computer Science. I was very fortunate to be advised by Iain Carmichael and Ryan Tibshirani working on statistics and machine learning problems in computational precision medicine and statistical learning theory.

If you're an undergrad or Master's student at Cornell and are interested in collaborating, please reach out!

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Research

I am interested in theory and methods in the areas of high-dimensional statistics, stochastic optimization, reinforcement learning, deep learning, language and diffusion models, and differential privacy.

Towards Optimal Differentially Private Regret Bounds in Linear MDPs
Sharan Sahu
Privatizing LSVI-UCB++ with Bernstein bonus achieves state-of-the-art regret in linear MDPs under joint differential privacy with minimal utility loss.
[ArXiv]

Presentations and Talks

Template from here.