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 hold a PhD from University of Coimbra (Portugal), during which I worked on probabilistic models for learning from crowdsourced data.

My research interests include:

• Machine Learning and Pattern Recognition
• Crowdsourcing and Learning from Crowds
• Probabilistic Graphical Models and Bayesian inference
• Intelligent Transportation Systems and Urban Mobility

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

Selected publications:

• • • 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
    • 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
    • Rodrigues, F.
      On the importance of stationarity, strong baselines and benchmarks in transport prediction problems [PDF] [Code]
      in arXiv:2203.02954, 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
    • Sfeir, G. and Abou-Zeid, M. and Rodrigues, F. and Pereira, F. and Kaysi, I.
      Semi-nonparametric Latent Class Choice Model with a Flexible Class Membership Component: A Mixture Model Approach
      in Journal of Choice Modelling, Elsevier, 2021
    • Kostic, B. and Loft, M., Rodrigues, F. and Borysov, S.
      Deep survival modelling for shared mobility  [PDF]
      in Transportation Research Part C, Elsevier, 2021
    • Rodrigues, F. and Pereira, F. C.
      Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problems [PDF] [Code]
      in IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2020
    • Rodrigues, F. and Ortelli, N. and Bierlaire, M. and Pereira, F. C.
      Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models [PDF] [Code]
      in IEEE Transactions on Intelligent Transportation Systems, 2020
    • Rodrigues, F. and Pereira, F. C.
      Deep Learning from Crowds [PDF] [Code] [Data]
      in The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018 (oral)
    • Rodrigues, F. and Markou, I. and Pereira, F. C.
      Combining Time-Series and Textual Data for Taxi Demand Prediction in Event Areas: a Deep Learning Approach [PDF] [Code+Data]
      in Information Fusion, Elsevier, 2018
    • Rodrigues, F. and Lourenço, M. and Ribeiro, B. and Pereira, F. C.
      Learning Supervised Topic Models for Classification and Regression from Crowds [PDF] [Code] [Data]
      in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017
    • Rodrigues, F. and Borysov, S. and Ribeiro, B. and Pereira, F. C.
      A Bayesian Additive Model for Understanding Public Transport Usage in Special Events [PDF] [Code]
      in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016
    • Rodrigues, F. and Pereira, F.C. and Ribeiro, B.
      Gaussian Process Classification and Active Learning with Multiple Annotators [PDF] [Code] [Data]
      in International Conference on Machine Learning (ICML), 2014