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), Statistics, ECE, and CS. I am affiliated with WCM Institute of AI for Digital Health. 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 causality, efficient learning and reasoning, and their applications in precision health. I develop statistically principled, computationally efficient, and interpretable methods for learning from limited and imperfect data and for making decisions under real-world constraints. I actively collaborate with NewYork-Presbyterian (NYP) and Weill Cornell Medicine (WCM) on applications including heart failure care, clinical AI, and single-cell genomics.
My work spans causal discovery and inference, adaptive experimentation and reinforcement learning, and generative models, with an emphasis on methods that perform well under realistic finite-sample and resource constraints. Work organized by area is Research by Area.
A copy of my CV can be found here.
My research focuses on causality, efficient learning and reasoning, and their applications in precision health. I develop statistically principled, computationally efficient, and interpretable methods for learning from limited and imperfect data and for making decisions under real-world constraints. I actively collaborate with NewYork-Presbyterian (NYP) and Weill Cornell Medicine (WCM) on applications including heart failure care, clinical AI, and single-cell genomics.
My work spans causal discovery and inference, adaptive experimentation and reinforcement learning, and generative models, with an emphasis on methods that perform well under realistic finite-sample and resource constraints. Work organized by area is Research by Area.
A copy of my CV can be found here.
Funding and Awards
- My research is supported by
- NewYork-Presbyterian
- AWS Credit Grants – Cornell’s Center for Data Science for Enterprise and Society 9/2024-9/2025
- AWS Promotional Credits 11/2025-10/2027
- Cornell Center for Advanced Technology; 8/1/2026-7/31/2027
- My work has been recognized by the following awards:
- NVIDIA Academic Grant Program Award 1/1/2026-6/20/2026
- 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
Group
- Postdoctoral Fellow:
- Mingzhou Liu
- PhD Advisees:
- Diyang Li (CS)
- Wenxin Chen (CS, coadvised with Fei Wang)
- Jiamin Xu (ORIE)
- Shixing Yu (ECE)
- Current and Past Student Mentoring:
- Xueqing Liu (PhD Duke-NUS Medical School 2025 -> Postdoc at Harvard, Fall 2025)
- Mengxiao Gao (Undergrad Tsinghua 2025 -> Cornell ORIE PhD program, Fall 2025)
- Flavia Jiang (Undergrad Cornell 2025 -> UChicago Data Science PhD program, Fall 2025)
- Ruiyang Lin (Undergrad USTC 2025 -> WashU in St. Louis Data Science PhD program, Fall 2025)
- Austin Zhao (Undergrad Cornell, 2026)
- Shashank Ramachandran (MEng ORIE, Cornell Tech 2026)
- Jacqueline Maasch (PhD CS 2026, Minor advisor)
Methodological Work
Chronology Papers in each section below are listed chronologically. For an organization by research theme, see Research by Area.
- MixCIT: A Kernel Based Local-Polynomial Debiased Test for Conditional Independence on Mixed-Type Data with Mengxiao Gao, Promit Ghosal (arXiv)
- Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces with Shixing Yu, Promit Ghosal (arXiv)
- Integrating Causal DAGs in Deep RL: Activating Minimal Markovian States with Multi-Order Exposure with Jiamin Xu, Jacqueline Maasch (arXiv)
- Deep Doubly Debiased Longitudinal Effect Estimation with ICE G-Computation with Wenxin Chen, Weishen Pan, Fei Wang (arXiv) (KDD 2026)
- Smooth Multi-Policy Causal Effect Estimation in Longitudinal Settings with Wenxin Chen, Weishen Pan, Fei Wang (arXiv) (ICML 2026)
- Fast Non-Episodic Finite-Horizon RL with K-Step Lookahead Thresholding with Jiamin Xu (arXiv) (ICML 2026)
- Clustering by Denoising: Latent Plug-and-Play Diffusion for Single-Cell Data with Dominik Meier, Shixing Yu, Sagnik Nandy, Promit Ghosal (arXiv) (ICLR 2026)
- From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples? with Sujai Hiremath, Dominik Janzing, Philipp Faller, Patrick Blobaum, Elke Kirschbaum, Shiva Prasad Kasiviswanathan (arXiv) (AISTATS 2026)
- From Restless to Contextual: A Thresholding Bandit Approach to Improve Finite-Horizon Performance with Jiamin Xu, Ivan Nazarov, Aditya Rastogi, Africa Perianez (arXiv) (AISTATS 2026)
- MOSIC: Model-Agnostic Optimal Subgroup Identification with Multi-Constraint for Improved Reliability with Wenxin Chen, Weishen Pan, Fei Wang (arXiv)(ICML 2026)
- Targeted Maximum Likelihood Learning: An Optimization Perspective with Diyang Li (NeurIPS 2025) (a copy here)
- When Additive Noise Meets Unobserved Mediators: Bivariate Denoising Diffusion for Causal Discovery with Dominik Meier, Sujai Hiremath, Promit Ghosal (arXiv) (NeurIPS 2025)
- Optimal Adjustment Sets for Nonparametric Estimation of Weighted Controlled Direct Effect with Ruiyang Lin, Yongyi Guo (arXiv) (NeurIPS 2025)
- Online Uniform Sampling: Randomized Learning-Augmented Approximation Algorithms with Application to Digital Health with Xueqing Liu, Esmaeil Keyvanshokooh, and Susan A. Murphy (arXiv)
- LoSAM: Local Search in Additive Noise Models with Mixed Mechanisms and General Noise for Global Causal Discovery with Sujai Hiremath, Promit Ghosal (arXiv) (UAI 2025)
- 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)
- 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
Winner, 2021 INFORMS Pierskalla Best Paper Award
Canadian Healthcare Optimization Workshop (CHOW) best paper in the category of operations research/management science, 2021
Finalist, 2019 INFORMS IBM Service Science Best Student Paper Award
Review, Workshop Publication, and Applied Work
- Federated Causal Inference in Healthcare: Methods, Challenges, and Application with Haoyang Li, Jie Xu, Fei Wang, Chengxi Zang (arXiv)
- 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)
- 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)
- Data Visualization of Agent-Based Modeling of Virus Spread with Dominique Thiebaut
INFOCOMP, 2017 (INFOCOMP 2017;PDF) (Source code)
Teaching
- Fall 2023, 2024, 2025, 2026, CS 5785/ORIE 5750/ECE 5414, Applied Machine Learning
- Spring 2026, ORIE 7790: Causal Graphical Models: From foundations to modern applications
- Spring 2025, ORIE 5217: Digital N-of-1 Trials and Their Application
- Spring 2024, ORIE 7790: Selected topics in Applied Statistics - Statistical and Optimization Methods for Decision-Making in Healthcare
Selected Talks
- JSM, Fast Non-Episodic Finite-Horizon RL with K-Step Lookahead Thresholding, Boston, August 6, 2026
- Informs Healthcare, Raleigh, NC, July 28, 2026
- From Restless to Contextual: A Thresholding Bandit Approach to Improve Finite-Horizon Performance;
- Fast Non-Episodic Finite-Horizon RL with K-Step Lookahead Thresholding
- Weill Cornell Symposium, Artificial Intelligence for Basic Research in Medicine and the Life, What AI Can and Cannot Do in Clinical Decision Making: Lessons from Heart Failure and Sepsis, New York, June 2, 2026
- TCS Life Sciences Forum New York 2026, Demystifying n-of-1 and Micro-randomized Controlled Trials: Assumptions, Models, and State-of-the-Art Solutions, New York, Apr 14, 2026
- Amazon Scout RL Lunch Talk, From Restless to Contextual: A Thresholding Bandit Approach to Improve Finite-Horizon Performance, New York, Nov 21, 2025
- Informs Annual Meeting 2025, From Restless to Contextual: A Thresholding Bandit Approach to Improve Finite-Horizon Performance, Atlanta, Oct 28, 2025
- 8th Rotman Annual Research Roundtable: Data Analytics in Healthcare, Local Causal Discovery for Structural Evidence of Direct Discrimination for Liver Transplant, Toronto, May 23, 2025
- Cornell AI Seminar, Efficient Local and Global Causal Discovery: Methods Leveraging Causal Substructures for Improved Finite Sample Performance, Virtual, Feb 14, 2025
- Workshop on Individualized Decision Making, panelist on implementation challenges of individualized decisions, Berkeley, July 18-19, 2024
- Conference on Health, Inference, and Learning (CHIL), moderator, the panel on behavioral health and economics, New York, June 27-28, 2024
- 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, Jersey 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