I am an assistant professor in the School of Operations Research and Information Engineering and Cornell Tech at Cornell University. I am a field member of ORIE, Center for Applied Mathematics (CAM) and Statistics. Prior to Cornell Tech, I was a postdoctoral fellow at the Statistical Reinforcement Lab at Harvard University. I received my Ph.D. in Operations Research in 2022 from Carnegie Mellon University. I received my B.A.s in Mathematics and Economics from Smith College in 2017.
My research focuses on the design of adaptive and online algorithms for personalized treatment, such as micro-randomized trials and N-of-1 trials, in constrained settings. I am also interested in robust and efficient statistical inference for data collected in both adaptive and nonadaptive settings, robust and scalable causal discovery methods, and improving fairness in healthcare treatment outcomes. More broadly, I aim to bridge the gap between research and practice in healthcare. A copy of my CV can be found here.
AWS Credit Grants – Cornell’s Center for Data Science for Enterprise and Society
My work has been recognized by the following awards:
Finalist, 2023 INFORMS DMDA Workshop Best Paper Competition -- Theoretical Track
Winner, 2021 INFORMS Pierskalla Best Paper Award
Winner, 2021 CHOW Best Student Paper in the Category of Operations Research and Management Science
Finalist, 2019 INFORMS IBM Service Science Best Student Paper Award
Tata Consultancy Services Fellowship, Tepper School of Business, CMU, 2020
Ann Kirsten Pokora Prize, Department of Mathematics, Smith College, 2017
My research focuses on the design of adaptive and online algorithms for personalized treatment, such as micro-randomized trials and N-of-1 trials, in constrained settings. I am also interested in robust and efficient statistical inference for data collected in both adaptive and nonadaptive settings, robust and scalable causal discovery methods, and improving fairness in healthcare treatment outcomes. More broadly, I aim to bridge the gap between research and practice in healthcare. A copy of my CV can be found here.
Funding and Awards
My research is supported byGroup
PhD Advisees:Preprints and Publications
- Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits with Brian Cho, Dominik Meier, Nathan Kallus (arXiv)
- LoSAM: Local Search in Additive Noise Models with Unmeasured Confounders, a Top-Down Global Discovery Approach with Sujai Hiremath, Promit Ghosal (arXiv)
- CSPI-MT: Calibrated Safe Policy Improvement with Multiple Testing for Threshold Policies with Brian Cho, Ana-Roxana Pop, Sam Corbett-Davies, Israel Nir, Ariel Evnine, Nathan Kallus (arXiv)
- Local Causal Discovery for Structural Evidence of Direct Discrimination with Jacqueline Maasch, Violet Chen, Agni Orfanoudaki, Nil-Jana Akpinar, Fei Wang (arXiv)
- Online Uniform Sampling: Randomized Learning-Augmented Approximation Algorithms with Application to Digital Health with Xueqing Liu, Esmaeil Keyvanshokooh, and Susan A. Murphy (arXiv)
- Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Models with Sujai Hiremath, Jaqueline Maasch, Mengxiao Gao, Promit Ghosal (arXiv) (NeurIPS 2024)
- Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams with Brian Cho and Nathan Kallus (arXiv)(ICML 2024)
- Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters with Brian Cho, Yaroslav Mukhin, and Ivana Malenica (arXiv)(ICML 2024)
- Finalist, 2023 INFORMS DMDA Workshop Best Paper Competition -- Theoretical Track
- Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs with Jacqueline Maasch, Weishen Pan, Shantanu Gupta, Volodymyr Kuleshov, Fei Wang (arXiv) (UAI 2024) (NeurIPS Causal Representation Learning Workshop 2023)
- Improving Treatment Responses through Limited Nudges: A Data-Driven Learning and Optimization Approach with Esmaeil Keyvanshokooh, Yongyi Guo, Xueqing Liu, Anna L. Trella, and Susan A. Murphy (Journal version)
- Contextual Bandits with Budgeted Information Reveal with Esmaeil Keyvanshokooh, Xueqing Liu, and Susan A. Murphy (arXiv) (AISTATS 2024)
- Anytime-Valid Inference in N-of-1 Trials with Ivana Malenica, Yongyi Guo, and Stefan Konigorski (arXiv) (Machine Learning for Health symposium 2023, ML4H 2023)
- Awarding Additional MELD Points to the Shortest Waitlist Candidates Improves Sex Disparity in Access to Liver Transplant in the United States with Sarah Bernards, Eric Lee, Ngai Leung, Mustafa Akan, Huan Zhao, Monika Sarkar, Sridhar Tayur, Neil Mehta (American Journal of Transplant 2022)
- Toward a Liquid Biopsy: Greedy Approximation Algorithms for Active Sequential Hypothesis Testing with Su Jia, Andrew Li, and Sridhar Tayur (Journal version) (Management Science 2024)
- Greedy Approximation Algorithms for Active Sequential Hypothesis Testing with Su Jia, and Andrew Li (NeurIPS 2021)
- Winner, 2021 INFORMS Pierskalla Best Paper Award
- Causal Inference with Selectively Deconfounded Data with Andrew Li, Zachary Lipton, and Sridhar Tayur (AISTATS 2021) (Journal version) (NeurIPS CausalML Workshop 2019)
- Machine Learning Algorithms in Predicting Hospital Readmissions in Sickle Cell Disease with Arisha Patel, Andrew Li, Jeremy Weiss, Seyed Mehdi Nouraie, Sridhar Tayur, and Enrico M Novelli (British Journal of Haematology, 2020) (Extended abstract at ASH Annual Meeting & Exposition 2019)
- Personalized Treatment for Opioid Use Disorder with Alan Scheller-Wolf and Sridhar Tayur (Journal version)
- CHOW best paper in the category of operations research/management science, 2021
- Finalist, 2019 INFORMS IBM Service Science Best Student Paper Award
- Data Visualization of Agent-Based Modeling of Virus Spread with Dominique Thiebaut
INFOCOMP, 2017 (INFOCOMP 2017;PDF) (Source code)