About Me

I am a fifth-year PhD candidate in Operations Research at Tepper School of Business, Carnegie Mellon University. I am joining Cornell Tech as an Assistant Professor in ORIE starting July 2023, and I will spend one year at the Statistical Reinforcement Learning lab with Susan Murphy. My advisors are Prof. Sridhar Tayur and Prof. Andrew Li. I also work closely with Prof. Zachary Lipton and Prof. Alan Scheller-Wolf, and I am part of the ACMI lab. Prior to joining CMU, I received my BA degrees in Mathematics (with the Ann Kirsten Pokora Prize) and Economics from Smith College in May, 2017. I completed my first year of college at UCSD in June, 2014. I am originally from Hangzhou, China.

Research Interests

I am interested in efficient algorithms in precision medicine, and my work lies in the intersection of optimization, machine learning, and medicine. As the concept of precision medicine spreads, there is a growing need for developing better algorithms that
  • are sample efficient (i.e., require fewer samples to achieve the same accuracy level),
  • think beyond association (to identify the causation hidden in the data),
  • provide insights to medical practice.
  • Thus, I am particularly interested in applying optimization in causal inference to provide guidance for medical practice. My past research includes a variety of healthcare topics, ranging from opioid use disorder, and sickle cell disease, to liquid biopsy. My past theoretical work has focused on a) optimal policies for deconfounding, b) active sequential hypothesis testing, and c) constrained POMDP. A copy of my CV can be found here.

    I am passionate about tackling real-world medical problems. I am actively collaborating with the University of Pittsburgh Medical Center, and I welcome all collaboration opportunities.

    Awards

    I am honered to have received the following awards and fellowships:
  • 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
  • William Larimer Mellon PhD Fellowship, 2017-2019, 2021-2022
  • Internal CMU Nomination, 2021 Apple Scholars in AI/ML PhD fellowship
  • Publications

    Toward a Liquid Biopsy: Greedy Approximation Algorithms for Active Sequential Hypothesis Testing with Su Jia, Andrew Li, and Sridhar Tayur (SSRN)(ArXiv)

    NeurIPS 2021 (forthcoming)
    Winner, 2021 INFORMS Pierskalla Best Paper Award
    Submitted to Operations Research

    Causal Inference with Selectively Deconfounded Data with Andrew Li, Zachary Lipton, and Sridhar Tayur (DOI) (ArXiv) (SSRN)

    AISTATS 2021
    Management Science Round 1 Decision: Major Revision
    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
    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 (PDF) (Source code)

    Teaching Experience

  • Instuctor, 70460: Mathematical Models for Consulting (Undergrad), Spring 2021, rating: 4.33/5.0
  • TA, 70467: Machine Learning for Business Analytics (Undergrad), Fall 2020, 2021
  • TA, 46886: Machine Learning for Business Applications I (MSBA), Mini 4, 2020, 2021, 2022
  • TA, 46887: Machine Learning for Business Applications II (MSBA), Mini 5 2019, 2020, 2021
  • TA, 45883: Machine Learning for Business (MBA), Mini 6 2019
  • TA, 47774/47775: Advanced Stochastic Analysis and Applications (PhD), Fall 2019
  • TA, 46976: Financial Optimization (MSCF), Mini 1 2019, 2020
  • TA, 45861: Six Sigma Tools and Techniques (MBA), Mini 2 2019, 2020
  • TA, 46881: Programming in R and Python (MSBA), Mini 2 2019, 2020
  • TA, 70332: Business, Society and Ethics (Undergrad), Spring 2020, 2019
  • TA, 70364: Business Law (Undergrad), Spring 2020