About Me

I am an Assistant Professor of Operations Research and Information Engineering at Cornell Tech. Prior to joining Cornell, I was a postdoctoral fellow in the Department of Harvard Statistics, working with Susan Murphy. I obtained my Ph.D. degree in Operations Research from the Tepper School of Business, Carnegie Mellon University in May 2022. Prior to CMU, I received my BA degrees in Mathematics (with the Ann Kirsten Pokora Prize) and Economics from Smith College in May 2017.

Research Interests

My research interests include adaptive/online algorithm design in personalized treatment (including micro-randomized trials and N-of-1 trials) under constrained settings, robust and efficient inference and causal discovery methods. More broadly, I am interested in bridging the gap between research and practice in healthcare. A copy of my CV can be found here.



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, 2020
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    Group

    PhD Advisees:
  • Brian Cho (Cornell ORIE, coadvised with Nathan Kallus)
  • Jacqueline Maasch (Minor advisor, Cornell CS)
    • Other Student Collaborators:
  • Xueqing Liu (PhD student at Duke-NUS Medical School)
    •

    Preprints and Publications

    Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams with Brian Cho and Nathan Kallus (arXiv)

    Online Uniform Risk Times Sampling: First Approximation Algorithms, Learning Augmentation with Full Confidence Interval Integration with Xueqing Liu, Esmaeil Keyvanshokooh, and Susan A. Murphy (arXiv)

    Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs with Jacqueline Maasch, Weishen Pan, Shantanu Gupta, Volodymyr Kuleshov, Fei Wang (arXiv)
    A preliminary version is accept at NeurIPS Causal Representation Learning Workshop, December 2023

    Anytime-Valid Inference in N-of-1 Trials with Ivana Malenica, Yongyi Guo, and Stefan Konigorski (arXiv) (ML4H)

    Machine Learning for Health symposium 2023 (ML4H 2023)

    Contextual Bandits with Budgeted Information Reveal with Esmaeil Keyvanshokooh, Xueqing Liu, and Susan A. Murphy

    Accepted at AISTATS 2024 (arXiv)

    Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters with Brian Cho, Yaroslav Mukhin, and Ivana Malenica(arXiv)

    Finalist, 2023 INFORMS DMDA Workshop Best Paper Competition -- Theoretical Track

    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 (DOI)

    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 (Jounal Version)(NeurIPS 2021)

    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)

    AISTATS 2021; a preliminary version was accepted at NeurIPS CausalML Workshop, December 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 (DOI)

    British Journal of Haematology, December 2020; an extended abstract was accepted at ASH Annual Meeting & Exposition, December 2019

    Personalized Treatment for Opioid Use Disorder with Alan Scheller-Wolf and Sridhar Tayur (SSRN)

    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;PDF) (Source code)

    Selected Talks

  • Upcoming! pre-ENAR workshop on Statistical Methods for Digital Health Technologies data, Online Uniform Risk Times Sampling, Baltimore, March, 9, 2024
  • Learning on Graphs New York, A gentle introduction to causal discovery and local causal discovery, Jeresity City, March 1, 2024
  • ITA Workshop, Online Uniform Risk Times Sampling, San Diego, Feb 20, 2024
  • Cornell Center for Applied Mathematics Colloquium, Kernel Debiased Plug-in Estimation , Ithaca, Feb 9, 2024
  • IMSI workshop on Machine Learning and Artificial Intelligence for Personalized Medicine, Budgeted Information Reveal in Sequential Experiments, Chicago, April 17, 2023
  • Harvard Statistics Colloquium, Greedy Approximation Algorithms for Active Sequential Hypothesis Testing, Cambridge, October 31, 2022
  • Workshop on Quantifying Uncertainty: Stochastic, Adversarial, and Beyond, Simons Institute for the Theory of Computing, Greedy Approximation Algorithms for Active Sequential Hypothesis Testing, Berkeley, September 12, 2022

    • Teaching

  • Spring 2024, ORIE 7790: Selected topics in Applied Statistics -- Statistical and Optimization Methods for Decision-Making in Healthcare
  • Fall 2023, CS 5785/ORIE 5750/ECE 5414, Applied Machine Learning