PNNL

James is a Postdoctoral Associate with the Data Science and Machine Intelligence (DSMI) group at PNNL. His PNNL career began in 2023, as a summer intern, before receiving his PhD at the University of Virginia in 2024. Lately, James’ research has primarily focused on integrating machine learning with classical optimization methods, toward real-time optimization and control under uncertainty. Previously, he received a MS in applied mathematics at Stony Brook University before joining the Institute for Defense Analyses as an operations research analyst. His main interests include optimization for operations research, design and control, machine learning for optimization, computational physics, and scientific machine learning.

• Doctor of Philosophy, Computer Science, University of Virginia
• Master of Science, Operations Research, Stony Brook University 
• Bachelor of Science, Mathematics, University of Buffalo, State University of NY

• Two-Year University Fellowship, Syracuse University
• Excellence in Teaching, Spring, Stony Brook University, Dept. of Applied Mathematics
• Summa Cum Laude, University at Buffalo
• Outstanding Senior Award, Runner-Up, University at Buffalo, Dept. of Mathematics
• Excellence in Undergraduate Research, University at Buffalo

2024

Ethan King, James Kotary, Ferdinando Fioretto, Jan Drgona. ”Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming.” In the IEEE Conference on Decision and Control (CDC), 2024.

James Kotary, Vincenzo Di Vito, Ferdinando Fioretto, Pascal Van Hentenryck. ”Learning Joint Models of Prediction and Optimization.” In the European Conference on Artificial Intelligence (ECAI), 2024.

My H Dinh, James Kotary, Ferdinando Fioretto. ”End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty.” In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2024.

My H Dinh, James Kotary, Ferdinando Fioretto. ”Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages.” In Proceedings of the ACM Conference on Fairness, Accountability and Transparency (FAccT), 2024.

Jayanta Mandi, James Kotary, Senne Berden, Maxime Mulamba, Victor Bucarey, Tias Guns, Ferdinando Fioretto. ”Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities.” In the Journal of Artificial Intelligence Research (JAIR), 2024.

2023

James Kotary, My H Dinh, Ferdinando Fioretto. ”Backpropagation of Unrolled Solvers with Folded Optimization”. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2023.

James Kotary, Vincenzo Di Vito, Ferdinando Fioretto. ”Differentiable Model Selection for Ensemble Learning”. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2023.

2022

James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Ziwei Zhu. ”End-to-end Learning for Fair Ranking Systems”. In Proceedings of the ACM Web Conference (WWW), 2022.

James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck. ”Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method”. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2022.

2021

James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck. ”Learning Hard Optimization Problems: A Data Generation Perspective”. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NeurIPS), 2021.

James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Bryan Wilder. ”End-to-End Constrained Optimization Learning: A Survey”. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI: Survey Track), 2021.

2012

Boris Brimkov, Jae-Hun Jung, James Kotary, Xinwei Liu, Jing Zheng. ”A spectral and radial basis function hybrid method for visualizing vascular flows”. In Computational Modelling of Objects Represented in Images (CompIMAGE), 2012.