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Published
Papers

All Papers

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A Machine Learning Approach to Two-Stage Adaptive Robust Optimization

with Cheol Woo Kim, submitted to European Journal of Operation Research, 2023

A Stochastic Benders Decomposition Scheme for Large-Scale Data-Driven Network Design

with R. Cory-Wright, J. Pauphilet, P. Petridis, submitted to INFORMS Journal of Computing, 2023

A data-driven approach to multistage stochastic linear optimization

with Shimrit Shtern, Bradley Sturt, Management Science 69(1):51-74, 2023

A new perspective on low-rank optimization, 2023

with Ryan Cory-Wright, Jean Pauphilet, Mathematical Programming, 2023

Accelerating Vaccine Innovation for Emerging Infectious Diseases via Parallel Discovery

with Joseph Barberio, Jacob Becraft, Zied Ben Chaouch, Tasuku Kitada, Michael L Li, Andrew W Lo, Kevin Shi, Qingyang Xu, 2023

Adaptive Linear Algebra

with Thodoris Koukouvinos, 2023

Compressed Sensing: A Discrete Optimization Approach

with Nicholas Johnson, 2023

Data-Driven COVID-19 Vaccine Development for Janssen

with Michael Lingzhi Li, Xinggang Liu, Jennings Xu, Najat Khan, INFORMS Journal on Applied Analytics, 2023

Distributionally Robust Causal Inference with Observational Data

with Kosuke Imai, Michael Lingzhi Li, submitted to Journal of Machine Learning Research, 2023

Dynamic Optimization with Side Information

with C. McCord and B. Sturt, European Journal of Operations Research, 2023

Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach

with L. Boussioux, submitted to Journal of Machine Learning Research, 2023

Explainable vs. interpretable artificial intelligence frameworks in oncology

with Georgios Antonios Margonis, Transl Cancer Res 2023;12(2):217, 2023

Finding neurons in a Haystack - Case studies with sparse probing

with Wes Gurnee, Neel Nanda, Matthew Pauly, Katherine Harvey, Dmitrii Troitskii, 2023

Forecasting COVID-19 and analyzing the effect of government interventions

with Michael Lingzhi Li, Hamza Tazi Bouardi, Omar Skali Lami, Thomas A Trikalinos, Nikolaos Trichakis, Operations Research 71(1):184-201, 2023

Global Flood Prediction: a Multimodal Machine Learning Approach

with Cynthia Zeng, arXiv preprint arXiv:2301.12548, 2023

Holistic Deep Learning

with L. Boussioux, K. Villalobos Carballo, M. Li, A. Paskov and I. Paskov, submitted to Machine Learning, 2023

Holistic Prescriptive Analytics for Continuous and Constrained Optimization Problems

with Omar Skali Lami, INFORMS Journal on Optimization 5(2):155-171, 2023

Improving Stability in Decision Tree Models

with Vassilis Digalakis Jr, submitted to Operations Research, 2023

Interpretable Matrix Completion: A Discrete Optimization Approach

with Michael Lingzhi Li, INFORMS Journal on Computing, 2023

Learning sparse nonlinear dynamics via mixed-integer optimization

with Wes Gurnee, Nonlinear Dyn, 2023

Multistage Stochastic Optimization via Kernels

with KV Carballo, arXiv preprint arXiv:2303.06515, 2023

Optimal Low-Rank Matrix Completion - Semidefinite Relaxations and Eigenvector Disjunctions

with Ryan Cory-Wright, Sean Lo, Jean Pauphilet, 2023

Patient Outcome Predictions Improve Operations at a Large Hospital Network

with Liangyuan Na, Kimberly Villalobos Carballo, Jean Pauphilet, Ali Haddad-Sisakht, Daniel Kombert, Melissa Boisjoli-Langlois, Andrew Castiglione, Maram Khalifa, Pooja Hebbal, Barry Stein, 2023

Policy Analytics in Public School Operations

with Arthur Delarue, Operations Research 71(1):289-313, 2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

with Sherratt, K., Gruson, H., Grah, R et al, 2023