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

Selected publications:

• • • Rodrigues, F.
      Diffusion-aware Censored Gaussian Processes for Demand Modelling [PDF] [Code]
      in International Joint Conference on Artificial Intelligence (IJCAI), 2025
    • Schmidt, C. and Gammelli, D. and Harrison, J. and Pavone, M. and Rodrigues, F.
      Offline Hierarchical Reinforcement Learning via Inverse Optimization [PDF] [Code]
      in International Conference on Learning Representations (ICLR), 2025
    • Hüttel, F. and Riis, C. and Rodrigues, F. and Pereira, F.
      Bayesian Active Learning For Censored Regression [PDF] [Code]
      in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2025
    • Łukawska, M. and Jensen, A. and Rodrigues, F.
      Context-aware Bayesian mixed multinomial logit model [PDF]
      Journal of Choice Modelling, 2025
    • Gammelli, D. and Harrison, J. and Yang, K. and Pavone, M. and Rodrigues, F. and Pereira, F.
      Graph Reinforcement Learning for Network Control via Bi-Level Optimization [PDF] [Code]
      in International Conference on Machine Learning (ICML), 2023
    • Rodrigues, F.
      On the importance of stationarity, strong baselines and benchmarks in transport prediction problems [PDF] [Code]
      in IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 2023
    • Gammelli, D. and Yang, K and Harrison, J and Rodrigues, F. and Pereira, F. and Pavone, M.
      Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand [PDF] [Code]
      in 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022
    • Rodrigues, F.
      Scaling Bayesian inference of mixed multinomial logit models to large datasets [PDF] [Code]
      Transportation Research Part B: Methodological, 2022
    • Sfeir., G. and Abou-Zeid, M. and Rodrigues, F.
      Gaussian process latent class choice models [PDF]
      Transportation Research Part C: Emerging Technologies, 2022
    • Gammelli, D. and Rodrigues, F.
      Recurrent Flow Networks: A Recurrent Latent Variable Model for Density Modelling of Urban Mobility [PDF] [Code]
      in Pattern Recognition, Elsevier, 2022
    • Gammelli, D. and Rolsted, K. and Pacino, D. and Rodrigues, F.
      Generalized Multi-Output Gaussian Process Censored Regression [PDF] [Code]
      in Pattern Recognition, Elsevier, 2022
    • Gammelli, D. and Wang, Y. and Prak, D. and Rodrigues, F. and Minner, S. and Pereira, F.
      Predictive and prescriptive performance of bike-sharing demand forecasts for inventory management [PDF] [Code]
      Transportation Research Part C: Emerging Technologies, 2022