About me

I’m an associate professor at the Technical University of Denmark (DTU) in the Machine Learning for Smart Mobility (MLSM) group, where my research is primarily focused on machine learning models for understanding and optimizing urban mobility and human behaviour. Previously, I was a H.C. Ørsted / Marie-Skłodowska Curie Actions (COFUND) postdoctoral fellow, also at DTU, working on spatio-temporal models of mobility demand with emphasis on modelling uncertainty and the effect of special events. I have published more than 60 research articles in leading conferences and journals in both transportation and machine learning. I’m ranked in the worldwide Top 2% most cited scientist list (Elsevier–Stanford 2025) in the subfields “Logistics and Transportation” and “Artificial Intelligence and Image Processing”. I serve as Area Editor for “Transportation Research Part C: Emerging Technologies” and for “Artificial Intelligence for Transportation” (Elsevier).

My research interests include:

  • Machine Learning
  • Reinforcement Learning
  • Bayesian Modelling
  • Intelligent Transportation Systems
  • Urban Mobility
  • Behaviour Modelling

E-mail: rodr [at] dtu.dk – Google Scholar – GitHub

  1. Lassen, O., Agriesti, S., Rodrigues, F., Pereira, F., 2026. Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM. International Joint Conference on Artificial Intelligence (IJCAI), AI and Social Good Track.
  2. Rodrigues, F., 2025. Diffusion-aware Censored Gaussian Processes for Demand Modelling. International Joint Conference on Artificial Intelligence (IJCAI). [PDF] [Code]
  3. Schmidt, C., Gammelli, D., Harrison, J., Pavone, M., Rodrigues, F., 2025. Offline Hierarchical Reinforcement Learning via Inverse Optimization. International Conference on Learning Representations (ICLR). [PDF] [Code]
  4. Gammelli, D., Harrison, J., Yang, K., Pavone, M., Rodrigues, F., Pereira, F., 2023. Graph Reinforcement Learning for Network Control via Bi-Level Optimization. International Conference on Machine Learning (ICML). [PDF]
  5. Gammelli, D., Yang, K., Harrison, J., Rodrigues, F., Pereira, F., Pavone, M., 2022. Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand. 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). [PDF] [Code]
  6. Schmidt, C., Tygesen, M., Rodrigues, F., 2026. A Large-Scale Analysis on the Use of Arrival Time Prediction for Automated Shuttle Services in the Real World. IEEE Transactions on Intelligent Transportation Systems.
  7. Tresca, L., Schmidt, C., Harrison, J., Rodrigues, F., Zardini, G., Gammelli, D., Pavone, M., 2026. Robo-taxi fleet coordination at scale via reinforcement learning. IEEE Transactions on Control of Network Systems.
  8. Li, X., Alharbi, M., Gammelli, D., Harrison, J., Rodrigues, F., Schiffer, M., Pavone, M., Frazzoli, E., Zhao, J., Zardini, G., 2026. Reproducibility in the control of autonomous mobility-on-demand systems. IEEE Transactions on Robotics.
  9. Li, X., Schmidt, C., Gammelli, D., Rodrigues, F., 2025. Learning Joint Rebalancing and Dynamic Pricing Policies for Autonomous Mobility-on-Demand. IEEE Transactions on Intelligent Transportation Systems.
  10. Meskar, M., Krueger, R., Rodrigues, F., Aslani, S., Modarres, M., 2025. Combining choice and response time data to analyse the ride-acceptance behavior of ride-sourcing drivers. Transportation Research Part C: Emerging Technologies. [PDF]