We are excited to announce that Kai Jungle successfully defended his PhD on April 11! Under the supervision of Prof. Maximilian Schiffer, Kai conducted pioneering research at the intersection of machine learning and operations research, with a particular emphasis on future urban mobility systems such as autonomous mobility-on-demand and smart cities. His work is also driven by a strong passion for Explainable AI, aiming to make complex algorithmic decisions more transparent and trustworthy. We are thrilled that Kai will continue his academic journey as a postdoctoral researcher in our group, further advancing research in intelligent mobility and machine learning.
Jungel, K., Parmentier, A., Schiffer, M., & Vidal, T. (2023). Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control. INFORMS Journal on Computing (accepted).
Baty, L., Jungel, K., Klein, P. S., Parmentier, A., & Schiffer, M. (2024). Combinatorial Optimization-Enriched Machine Learning to Solve the Dynamic Vehicle Routing Problem with Time Windows. Transportation Science, 58(4), 708–725.
Jungel, K., Paccagnan, D., Parmentier, A., & Schiffer, M. (2024). WardropNet: Traffic Flow Predictions via Equilibrium-Augmented Learning. ICLR 2025 (accepted).
