I am a research scientist at Meta. I am broadly interested in developing algorithmic and data-driven tools for decision-making, especially for systems that adapt to human preferences/responses. Before Meta, I completed my Ph.D. at Stanford, focusing on contextual bandits. During this time, I also interned at as an applied scientist at Amazon, working on offline RL. Before that, I completed my bachelor's and master's at Chennai Mathematical Institute.

Links to my: Google scholar, LinkedIn.

Selected Pre-Prints and Publications: (Complete List)

• Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective [arXiv] 

Sanath Kumar Krishnamurthy*, Adrienne Propp*, Susan Athey.

AISTATS 2024.

[First approach to incorporate bias-variance trade-off into model selection for contextual bandits! Demonstrated the feasibility of costless model selection in contextual bandits under fairly benign conditions! Developed a novel misspecification test to evaluate prediction error bounds! The misspecification test enabled assumption-free guarantees in  [NeurIPS 2023].]

• Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization [arXiv] 

Sanath Kumar Krishnamurthy, Ruohan Zhan, Susan Athey, Emma Brunskill.

NeurIPS 2023.

[New form of uncertainty for contextual decision making! First general-purpose regression-based algorithm for simple regret minimization! Also, the first general-purpose algorithm that flexibly trades off simple and cumulative regret guarantees in contextual bandits!]

• Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles [arXiv]

Aldo Gael Carranza*,  Sanath Kumar Krishnamurthy*, Susan Athey.

AISTATS 2023.

[Demonstrate the theoretical and empirical benefits of using heterogeneous treatment effect estimation in contextual bandits! Uncovered favorable properties of using R-learner for downstream decision-making tasks!]

• Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning [arXiv]

Susan Athey*, Undral Byambadalai*, Vitor Hadad*, Sanath Kumar Krishnamurthy*, Weiwen Leung*, Joseph Jay Williams*

[Took contextual bandits to the real world (charitable giving)!]

• Finding Fair and Efficient Allocations [arXiv]

Siddharth Barman*, Sanath Kumar Krishnamurthy*, Rohit Vaish*

EC, 2018.

[Developed the first pseudo-polynomial time algorithm for computing fair (EF1) and Pareto optimal (PO) allocations of indivisible goods among agents with additive valuations! This is technically fascinating because even checking if an allocation is PO is co-NP complete. Our techniques to search over such allocations were used in several subsequent papers.]