Team

Faculty

Francisco Pereira
Professor
  • Machine Learning
  • Intel. Transport Systems
  • Simulation Modelling
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Carlos Lima Azevedo
Associate Professor
  • Transport Simulation
  • Demand Management
  • Behaviour Modelling
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Filipe Rodrigues
Associate Professor
  • Machine Learning
  • Urban Mobility
  • Econometrics
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Rico Krueger
Assistant Professor
  • Choice Modelling
  • Machine Learning
  • Bayesian Methods
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Samitha Samaranayake (Cornell)
Visiting Professor
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Postdocs

Georges Sfeir
  • Choice Modeling
  • Machine Learning
  • Travel Behavior
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Miguel Costa
  • Intelligent Transportation Systems
  • Cycling Safety
  • Machine Learning
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PhD Students

Frederik Boe Hüttel
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Atefeh H. Golsefidi
  • Mathematical Modelling
  • EV Charging Planning
  • EV Infrastructure
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Carolin Schmidt
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Lorena Torres Lahoz
  • Machine learning
  • Discrete Choice
  • Policy Making
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Xiaoyi Wu
  • Machine Learning
  • Transp. Optimization
  • Simulation
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Danya Li
  • Machine Learning
  • Traffic Forecasting
  • Transportation
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Dang Viet Anh Nguyen (Andrew)
  • Operations Research
  • Reinforcement Learning
  • Transportation
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Ongoing Collaborations

Bianca Pascariu (Université Gustave Eiffel)
Visiting Postdoc
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Fatemeh Siar (DTU)
Co-supervision with Prof. Felix Wilhelm Siebert
  • Computer Vision
  • Video Understanding
  • Road Safety
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Mana Meskar (SUT)
Co-supervision with Prof. Mohammad Modares Yazdi
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Santa Maiti (TUM)
Visiting PostDoc
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Alfredo Jose Ojeda Diaz (DTU)
Co-supervision with Prof. Sonja Haustein
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Mohamed Eldafrawi (Sapienza University of Rome)
Co-supervision with Prof. Guido Gentile
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Lampros Yfantis (UCL)
Co-supervision with Prof. Maria Kamargianni
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Keren-Or Rosenbaum (Technion - Israel Institute of Technology)
Co-supervision with Prof. Yoram Shiftan
Ana Martins (IST)
Co-supervision with Prof. Filipe Moura
Cloe Cortes Balcells (EPFL)
Co-supervision with Prof. Michel Bierlaire
 

Research Assistants

Nina Friser Holst
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Past Members

Faculty / Researchers
Ricardo DazianoVisiting from Cornell
Postdocs
PhD students
Mathias Niemann Tygesen 2024. Thesis title: Spatio-temporal Machine Learning for Future Mobility: Precision, Resilience, and Adaptability
Ioanna Arkoudi 2024. Thesis title: Embedding Representations for Discrete Choice and Travel Demand Models
Christoffer Riis 2024. Thesis title: Bayesian Machine Learning for Simulation Metamodeling
– Antoine Dubois 2021 – 2023
– Sergio Garrido 2019 – 2022
Visiting PhD students / External Collaborations
Gabriel Valença (IST), Supervised by Supervised by Filipe Moura and Ana Morais de Sá
Zhongcan Li (Southwest Jiaotong University), Co-supervision with Profs. Liping Fu and Chao Wen
Xu Yan (Southwest Jiaotong University), Co-supervision with Prof. Evelen van der Hunk
– Yunhai Gong (Dalian University of Technology) 2022, Co-supervision with Prof. Shaopeng Zhong
– Mingzhuang Hua (Southeast University) 2022, Co-supervision with Prof. Xuewu Chen
Vishnu Baburajan 2021, Thesis title: Automated Text Analysis on Open Ended Response Surveys: Measuring Attitudes Regarding Autonomous Vehicles
Nassim Motamedi (TUM) 2019, Thesis title: “Data­driven modeling of lane changing on freeways”
MSc students
– Dimitrios Agyros 2023, MSc Transport & Logistics, Thesis: Simulation and Assessment of Congestion Pricing Control through Machine-Learning
– Anders Lassen 2023, MSc Mathematical Modelling and Computation, Thesis: Reinforcement Learning for Adaptive Congestion Pricing
– Þórður Örn Stefánsson 2023, MSc Human-Centred AI, Thesis: Investigating the effects of activity patterns and urban context on individuals’ emotional state
– Hildur Lára Jónsdóttir, Gudrun Gudnadottir 2023, MSc Industrial Engineering, Thesis: Data Exploration, Reliability and Simulation of Travel in the Public Transportation Network: A Case Study of Greater Copenhagen
– Frederik Sandström Ommundsen 2023, MSc Business Analytics, Thesis: Spatial analysis of mental health, mobility and urban context data using machine-learning techniques
– Alexandre Bernard-Michinov 2023, MSc Transport & Logistics, Thesis: Exploring Bayesian Optimisation for Scenario Discovery in Complex Mobility Simulation
– Dimitrios Zerzis 2023, MSc Transport & Logistics, Thesis: Calibration of Activity-based models using Bayesian Optimisation
– Jesper Hauch 2023, MSc Mathematical Modelling and Computation, Thesis: Learning and Generalizing Polynomials in Simulation Metamodeling
– Carl Johan Astrup, Julian Simon Róin Skovhus 2023, MSc Mathematical Modelling and Computation, Thesis: Uncovering Censorship: Poisson Regression for Estimating Latent Demand in EV chargers
– Lukas Ralf Schinzel 2022, MSc Mathematical Modelling and Computation, Thesis: Exploring advanced visualization for microscopic mobility insights
– Pernille Sand 2022, MSc Mathematical Modelling and Computation, Thesis: Causality, Invariance, Hierarchical Bayes
– Lawrence Egyir 2022, MSc Mathematical Modelling and Computation, Thesis: SAS cargo predictions
– Mark Tselikov 2022, MSc Business Analytics, Thesis: Causal Metamodels for Simulators
– Theis Hjortkjær 2022, MSc Math. Modelling and Comp., Thesis: Zero-Shot learning of shared mobility demand using meta learning and graph neural networks
– Toke Bøgelund-Andersen 2022, MSc Business Analytics, Thesis: Predicting Car Pick-Up Times in Shared Mobility with Deep Graph Neural Networks
– Peter Groth 2022, MSc Math. Modelling and Comp., Thesis: Trajectory Forecasting with Graph Neural Networks
– Lorena Torres Lahoz 2022, MSc Math. Modelling and Comp., Thesis: Latent Class Choice Models using Machine Learning: Application to Individual Decisions in Sustainable Mobility
– Aikaterini Antonoudi & Andreas Arampatzis 2021, MSc Transport & Logistics, Thesis: Estimating Spatio-Temporal Demand for Electric Vehicle Charging
– Gauthier Belpaire 2021, MSc Business Analytics, Thesis: EV charging demand prediction from GPS traces
– Jacques Michel 2021, MSc Business Analytics, Thesis: Machine Learning applied to shipbuilding market analysis
– Mattia Andreotti 2021, MSc Industrial Engineering, Thesis: Predictive Maintenance at Haldor Topsoe
– Andreas Kaae 2021, MSc Transport & Logistics, Thesis: Survival Analysis Modeling of Electric Vehicle Charging
– Aijie Shu 2021, MSc Transport & Logistics, Thesis: Multi-task classification of trip mode and purpose: Leveraging transfer-learning and artificial neural networks on noisy and class-imbalanced GPS trajectories”
– Joan Omella 2021, MSc Transport & Logistics, Thesis: Metamodel optimization for traffic simulation calibration
– Kleio Milia & Magnus Hedengran 2021, MSc Transport & Logistics, Thesis: Smart parking: Forecasting cruising time using big data”
– Michael Wamberg 2021, MSc Math. Modelling and Comp., Thesis: Bayesian inference for probabilistic skill assessment: Ranking and rating of football clubs and players
– Simone Vestergaard & Rebecca Sommer 2021, MSc Business Analytics, Thesis: Automization of the supplier selection for SimpleFeast to ensure maximization of value
– Christian Glissov & Tobias Konradsen 2021, MSc Math. Modelling and Comp., Thesis: Recurrent Flows for Video Generation
– Francisco Jose Perez Dominguez 2020, MSc Transport & Logistics, Thesis: Applying a multi-layer network model for multi-modal trip planning in Maas
– Antonios Koutounidis 2020, MSc Transport & Logistics, Thesis: A travel and energy behaviour sensitive optimisation for charging of private EV fleet in the Danish context
– David Zhuravlev 2020, MSc Industrial Engineering, Thesis: Developing a reinforcement learning approach to trading strategy in energy balancing markets
– Nikolaos Nakis 2020, MSc Math. Modelling and Comp., Thesis: Task-free Variational Continual Learning
– Kasper Rolsted 2020, MSc Math. Modelling and Comp., Thesis: Generalized Heteroscedastic Multi-output Censored Gaussian Processes for Demand Prediction
– Mathias Tygesen 2020, MSc Math. Modelling and Comp., Thesis: Density estimation with Normalizing Flows
– Johannes Benjamin Eckert 2019, MSc Industrial Engineering, Thesis: Exploring blockchain in user-centric emission trading systems for multi-modal mobility
– Kris Walther 2019, MSc Math. Modelling and Comp., Thesis: Spatio-temporal modelling and forecasting of road traffic using Gaussian processes and deep neural nets
– Anders Parslov 2019, MSc Math. Modelling and Comp., Thesis: Uncertainty Estimation and Quantile Regression in Neural Networks for Bus Arrival Time Prediction
– Mathilde Loft 2019, MSc Math. Modelling and Comp., Thesis: Deep Survival Analysis for Shared Mobility
– Sebastian Balle 2019, MSc Math. Modelling and Comp., Thesis: Spatio-temporal analysis of bicycle incidents in the urban area of Copenhagen
Research assistants
– Antonios Koutounidis 2020-2022