top of page

Optimal and Scalable
Machine Learning

Machine learning is already very powerful, but there are still so many tasks outside of its reach. How do we develop machine learning and optimization algorithms for difficult, large-scale tasks? This stream of work aims to push the boundaries on algorithm tractability while providing performance guarantees.


Branch-and-Price for Prescriptive Contagion Analytics (with Jacquillat, A., Wang, K., Rame, M.). Major Revision at Operations Research.  


Slowly Varying Regression Under Sparsity (with Bertsimas, D., Diaglakis, Lami, O. S). arXiv preprint arXiv:2102.10773 (2021). Major Revision at Operations Research. 


Stochastic Cutting Planes for Data-Driven Optimization (with Bertsimas, D.).  INFORMS Journal of Computing 34(5), 2400-2409. 


Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion (with Bertsimas, D.). Journal of Machine Learning Research, 21(231), 1-43 (2020).  


Interpretable Matrix Completion: A Discrete Optimization Approach (with Bertsimas, D.). arXiv preprint arXiv:1812.06647 (2020). INFORMS Journal of Computing. 


Scalable Holistic Linear Regression (with Bertsimas, D.). Operations Research Letters (2020).  

bottom of page