James Kotary

I am interested in integrating constrained optimization and machine learning methods to enable new functionality in AI. Lately, my research has focused on learning fast approximate solvers for hard optimization problems, and enabling constrained optimization models as components in neural networks to learn data-driven optimal decision making. I am a PhD candidate at Syracuse University, where my advisor is the great Ferdinando Fioretto.

Recent Publications:

• James Kotary, My H Dinh, Ferdinando Fioretto. Backpropagation of Unrolled Solvers with Folded Optimization. IJCAI 2023.
• James Kotary, Vincenzo Di Vito, Ferdinando Fioretto. Differentiable Model Selection for Ensemble Learning. IJCAI 2023.
• James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Ziwei Zhu. End-to-End Learning for Fair Ranking Systems. WWW 2022.
• James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck. Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method. AAAI 2022.
• James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck. Learning Hard Optimization Problems: A Data Generation Perspective. NIPS 2021.
• James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Bryan Wilder. End-to-End Constrained Optimization Learning: A Survey. IJCAI 2021 (Survey Track).

Contact:

jkotary@syr.edu