Publications

My research combines methodological work in large-scale discrete optimization and machine learning with firsthand implementation of predictive and prescriptive analytics, especially in healthcare and sustainability settings.
My research is organized into four broad research directions (click to filter):




  1. Adaptive Optimization for Prediction with Missing Data [Preprint]
    Submitted, 2024, with Dimitris Bertsimas and Arthur Delarue
  2. Occupancy Prediction with Patient Data: Evaluating Time-Series, Patient-Level Aggregation, and Deep Set Models [Preprint]
    Submitted, 2023, with Song-Hee Kim and William Overman
  3. Patient Outcome Predictions Improve Operations at a Large Hospital Network [Preprint]
    Submitted, 2023, with Dimitris Bertsimas, Irra Na, Kimberley Villalobos Carballo, and Hartford HealthCare
  4. Optimal Low-Rank Matrix Completion: Semidefinite Relaxations and Eigenvector Disjunctions [Preprint]
    Submitted, 2023, with Dimitris Bertsimas, Ryan Cory-Wright, and Sean Lo
  5. Sparse PCA with Multiple Components [Preprint][Code]
    Submitted, 2022, with Ryan Cory-Wright
  6. A Stochastic Benders Decomposition Scheme for Large-Scale Stochastic Network Design [Preprint]
    INFORMS Journal on Computing, 2024 (accepted), with Dimitris Bertsimas, Ryan Cory-Wright, and Periklis Petridis
  7. Optimizing the Path Towards Plastic-Free Oceans [Preprint] [Media]
    Operations Research, 2024 (accepted), with Dick den Hertog, Baizhi Song, and Bruno Sainte-Rose and Yannick Pham (The Ocean Cleanup)
  8. Optimization Automates Nurse Scheduling at Hartford Hospital Emergency Department
    INFORMS Journal on Applied Analytics, 2024, with Dimitris Bertsimas, Irra Na, and Hartford HealthCare
  9. The Best Decisions Are Not the Best Advice: Making Adherence-Aware Recommendations [Preprint]
    Management Science, 2024, with Julien Grand-Clément
  10. Simple Imputation Rules for Prediction with Missing Data: Theoretical Guarantees vs. Empirical Performance [Preprint]
    Transactions on Machine Learning Research, 2024, with Dimitris Bertsimas and Arthur Delarue
  11. Hospital-wide Inpatient Flow Optimization [Preprint]
    Management Science, 2023, with Dimitris Bertsimas
  12. Robust Combination Testing: Methods and Application to COVID-19 Detection [Preprint] [Media]
    Management Science, 2023, with Sanjay Jain, Jonas Jonasson, and Kamalini Ramdas
  13. Minkowski Centers via Robust Optimization: Computation and Applications [Preprint]
    Operations Research, 2023, with Dick den Hertog and Mohamed Yahya Soali
  14. A New Perspective on Low-Rank Optimization [Preprint] [Code]
    Mathematical Programming, 2023, with Dimitris Bertsimas and Ryan Cory-Wright
  15. Robust Convex Optimization: A New Perspective That Unifies And Extends [Preprint]
    Mathematical Programming, 2022, with Dimitris Bertsimas, Dick den Hertog, and Jianzhe Zhen
  16. Robust and Heterogeneous Odds Ratio: Estimating Price Sensitivity for Unbought Items [Preprint]
    Manufacturing & Service Operations Management, 2022
  17. Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality [Preprint] [Code]
    Journal of Machine Learning Research, 2022, with Dimitris Bertsimas and Ryan Cory-Wright
  18. Mixed-Projection Conic Optimization: A New Paradigm for Modeling Rank Constraints [Preprint] [Code]
    Operations Research, 2021, with Dimitris Bertsimas and Ryan Cory-Wright
  19. Probabilistic Guarantees in Robust Optimization [Preprint]
    SIAM Journal on Optimization, 2021, with Dimitris Bertsimas and Dick den Hertog
  20. Sparse Classification: A Scalable Discrete Optimization Perspective [Preprint] [Code]
    Machine Learning, 2021, with Dimitris Bertsimas and Bart Van Parys
  21. A Unified Approach to Mixed-Integer Optimization Problems with Logical Constraints [Preprint] [ICS Newsletter]
    SIAM Journal on Optimization, 2021, with Dimitris Bertsimas and Ryan Cory-Wright
  22. Predicting Inpatient Flow at a Major Hospital Using Interpretable Analytics [Preprint]
    Manufacturing & Service Operations Management, 2021, with Dimitris Bertsimas, Jennifer Stevens, and Manu Tandon
  23. From Predictions to Prescriptions: A Data-Driven Response to COVID-19 [Website] [Preprint] [Code] [NYT]
    Health Care Management Science, 2021, with Dimitris Bertsimas et al.
  24. Direct Optimization across Computer Generated Reaction Networks Balances Materials Use and Feasibility of Synthesis Plans for Molecule Libraries
    Journal of Chemical Information and Modeling, 2021, with Hanyu Gao, Thomas Struble, Connor Coley, and Klavs F. Jensen
  25. Sparse Regression: Scalable Algorithms and Empirical Performance [Preprint] [Code]
    Statistical Science, 2020, with Dimitris Bertsimas and Bart Van Parys
  26. Certifiably Optimal Sparse Inverse Covariance Estimation [Preprint]
    Mathematical Programming, 2020, with Dimitris Bertsimas and Jourdain Lamperski
  27. A Tractable Numerical Strategy for Robust MILP and Application to Energy Management
    IEEE 55th Conference on Decision and Control (CDC), 2016, with Laurent El Ghaoui, Damien Faille, and Diego Kiener

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