Andrew A. Li

I am an Assistant Professor of Operations Research at CMU Tepper. 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 am currently funded by an NSF Career Award.

Finally, I've gotten to work with some fantastic Ph.D. students:

• Lin An
• Tianyi Peng (joining Columbia Business School)
• Kyra Gan (Assistant Professor, Cornell Tech)
• Su Jia (Research Assistant Professor, Cornell ORIE)
  Winner, Dantzig Dissertation Award
• Melda Korkut (Applied Scientist, Amazon Machine Learning Science)

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Conference Proceedings

• Short-lived High-volume Bandits
  ICML'23
  with Su Jia, Nishant Oli, Ian Anderson, Paul Duff, and R Ravi
• Dynamic Pricing with Monotonicity Constraint under Unknown Parametric Demand Model
  NeurIPS'22
  with Su Jia and R Ravi
• Markovian Interference in Experiments
  NeurIPS'22 Oral Presentation (< 2%)
  with Vivek Farias, Tianyi Peng, and Andrew Zheng
  Winner, INFORMS Applied Probability Society Best Student Paper Prize, 2022
  Winner, INFORMS RM&P Student Paper Award, 2022
• 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

• Learning Treatment Effects in Panels with General Intervention Patterns
  Submitted
  with Vivek Farias and Tianyi Peng
  Finalist, MSOM Best Student Paper Prize, 2022
• Toward a Liquid Biopsy: Greedy Approximation Algorithms for Active Sequential Hypothesis Testing
  Major Revision in Management Science
  with Kyra Gan, Su Jia, and Sridhar Tayur
  Co-winner, Pierskalla Best Paper Award, 2021
• 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
  Management Science, 2024
  with Vivek Farias, Deeksha Sinha, and Andrew Zheng
  Finalist, INFORMS RM&P Student Paper Award, 2020
• Solving the Phantom Inventory Problem
  Manufacturing & Service Operations Management, 2024
  with Vivek Farias and Tianyi Peng
• Protein Corona Sensor Array Nanosystem for Detection of Coronary Artery Disease
  Small, 2024
  with Gha Young Lee, Claudia Corbo et al.
• 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, Omid Farokhzad 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