Preprints and Publications
- From Restless to Contextual: A Thresholding Bandit Approach to Improve Finite-Horizon Performance with Jiamin Xu, Ivan Nazarov, Aditya Rastogi, Africa Perianez (arXiv)
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LoSAM: Local Search in Additive Noise Models with Mixed Mechasnisms and General Noise for Global Causal Discovery with Sujai Hiremath, Promit Ghosal (arXiv)
- Online Uniform Sampling: Randomized Learning-Augmented Approximation Algorithms with Application to Digital Health with Xueqing Liu, Esmaeil Keyvanshokooh, and Susan A. Murphy (arXiv)
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Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits with Brian Cho, Dominik Meier, Nathan Kallus (arXiv) (AISTATS 2025)
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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) (KDD 2025)
- Local Causal Discovery for Structural Evidence of Direct Discrimination with Jacqueline Maasch, Violet Chen, Agni Orfanoudaki, Nil-Jana Akpinar, Fei Wang (arXiv) (AAAI 2025)
- 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) (Forthcoming, Management Science)
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)
Optimizing Wearable Devices in Personalized Opioid Use Disorder Treatments Under Budget Constraint with Yanhan (Savannah) Tang, 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)