Andrew A. Li

I am an Assistant Professor of Operations Research at CMU Tepper, where I currently hold the BP Junior Faculty Chair. I completed my Ph.D. at the Operations Research Center at MIT.

My research interests are in statistics, optimization, and machine learning, with applications to operations management and medicine.

I get to work with some fantastic Ph.D. students:

• Kyra Gan (joining Cornell Tech)
• Su Jia
• Melda Korkut (joining Amazon Machine Learning Science)
• Tianyi Peng

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Conference Proceedings

• Uncertainty Quantification for Low-Rank Matrix Completion with Heterogeneous and Sub-Exponential Noise
  AISTATS'22
  with Vivek Farias and Tianyi Peng
• Greedy Approximation Algorithms for Active Sequential Hypothesis Testing
  NeurIPS'21
  with Kyra Gan and Su Jia
• Learning Treatment Effects in Panels with General Intervention Patterns
  NeurIPS'21 Oral Presentation (< 1%)
  with Vivek Farias and Tianyi Peng
• Causal Inference with Selectively-Deconfounded Data
  AISTATS'21
  with Kyra Gan, Zachary Lipton, and Sridhar Tayur
• Near-Optimal Entrywise Anomaly Detection for Low-Rank Matrices with Sub-Exponential Noise
  ICML'21
  with Vivek Farias and Tianyi Peng
• Disposable Linear Bandits for Online Recommendations
  AAAI'21
  with Melda Korkut
• Optimizing Offer Sets in Sub-linear Time
  EC'20
  with Vivek Farias and Deeksha Sinha
• Optimal Recovery of Tensor Slices
  AISTATS'18
  with Vivek Farias

Completed Articles

• Toward a Liquid Biopsy: Greedy Approximation Algorithms for Active Sequential Hypothesis Testing
  Submitted
  with Kyra Gan, Su Jia, and Sridhar Tayur
  Co-winner, Pierskalla Best Paper Award, 2021
• Learning Treatment Effects in Panels with General Intervention Patterns
  Submitted
  with Vivek Farias and Tianyi Peng
• Solving the Phantom Inventory Problem
  Submitted
  with Vivek Farias and Tianyi Peng
• Markdown Pricing Under Unknown Demand
  Major Revision in Management Science
  with Su Jia and R Ravi
• Causal Inference with Selectively-Deconfounded Data
  Major Revision in Management Science
  with Kyra Gan, Zachary Lipton, and Sridhar Tayur
• Optimizing Offer Sets in Sub-linear Time
  Major Revision in Management Science
  with Vivek Farias and Deeksha Sinha
  Finalist, INFORMS RM&P Student Paper Award, 2020
• Learning Preferences with Side Information
  Management Science, 2021
  with Vivek Farias
  First Place, Nicholson Student Paper Competition, 2017
  Finalist, INFORMS Applied Probability Society Best Student Paper Prize, 2017
• Machine Learning Algorithms for Predicting Hospital Re-admissions in Sickle Cell Disease
  British Journal of Haematology, 2021
  with Arisha Patel, Kyra Gan, Jeremy Weiss, Seyed Nouraie, Sridhar Tayur, and Enrico Novelli
• Analysis of the Human Plasma Proteome Using Multi-Nanoparticle Protein Corona Characterization for Detection of Alzheimer’s Disease
  Advanced Healthcare Materials, 2020
  with Claudia Corbo et al.
• Optimal Resource Consumption with an Application to Cloud Infrastructure via Data-Driven Prophet Inequalities
  with Muhammad Amjad, Vivek Farias, and Devavrat Shah
• Staffing to Stabilize Blocking in Loss Models with Time-Varying Arrival Rates
  Probability in the Engineering and Informational Sciences, 2016
  with Ward Whitt and Jingtong Zhao
• Approximate Blocking Probabilities in Loss Models with Independence and Distribution Assumptions Relaxed
  Performance Evaluation, 2014
  with Ward Whitt
• Ordered Multiplicity Lists for Eigenvalues of Symmetric Matrices Whose Graph is a Linear Tree
  Discrete Mathematics, 2014
  with Charles Johnson and Andrew Walker