James Kotary
Welcome to my research homepage. It's a record of my published work starting with my PhD program.
I'm most interested in the integration of AI and machine learning with more traditional mathematical models of physical and decision-making processes. I graduated recently from the University of Virginia, where my advisor was the great Ferdinando Fioretto. My thesis work focused on hybrid optimization and machine learning methods aimed at decision-making in real-time and under uncertainty. Now, I'm a postdoctoral researcher at the Pacific Northwest National Laboratory, where we work on design automation of energy systems and scientific workflows.
Recent Publications:
- James Kotary, Natalie Isenberg, Draguna Vrabie. Efficient Gradient-Based Optimization for Joint
Layout Design and Control of Wind Turbines. PESGM 2026.
- James Kotary, Himanshu Sharma, Ethan King, Ferdinando Fioretto, Draguna Vrabie, Jan Drgona. Learning to Solve Constrained Bilevel Control
Co-Design Problems. L4DC 2026.
- Ethan King, James Kotary, Ferdinando Fioretto, Jan Drgona. Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming. CDC 2024.
- James Kotary, Vincenzo Di Vito, Ferdinando Fioretto, Pascal Van Hentenryck. Learning Joint Models of Prediction and Optimization. ECAI 2024.
- My H Dinh, James Kotary, Ferdinando Fioretto. End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty. UAI 2024.
- My H Dinh, James Kotary, Ferdinando Fioretto. Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages. ACM 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. JAIR.
- 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. NeurIPS 2021.
- James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Bryan Wilder. End-to-End Constrained Optimization Learning: A Survey. IJCAI 2021 (Survey Track).
Contact:
james.kotary@pnnl.gov