Research
Scalable Multi-Objective Reinforcement Learning with Fairness Guarantees using Lorenz Dominance
Journal of Artificial Intelligence Research 85 (2026)
A Summary of: Tackling School Segregation with Transportation Network Interventions — An Agent-Based Modelling Approach
JAAMAS Track — AAMAS 2025
PTFA: An LLM-Based Agent that Facilitates Online Consensus Building through Parallel Thinking
arXiv preprint arXiv:2503.12499 (2025)
Understanding Fairness in Congestion Games with Learning Agents
Adaptive and Learning Agents Workshop, AAMAS (2025)
Enabling Inclusive Urban Transport Planning Through Civic Artificial Intelligence
Strengthening European Mobility Policy. Palgrave Macmillan, Cham (2024)
Tackling School Segregation with Transportation Network Interventions: An Agent-Based Modelling Approach
Autonomous Agents and Multi-Agent Systems 38, 22 (2024)
Most Visionary Paper Award, CMAS-AAMAS 2023
Mitigating School Segregation through Targeted School Relocation
CMAS Workshop, AAMAS (2024)
Fair Transport Network Design using Multi-Agent Reinforcement Learning
Doctoral Consortium — AAMAS 2023
Balancing Fairness and Efficiency in Transport Network Design through Reinforcement Learning
AAMAS 2023
Fairness in Transport Network Design — A Multi-Objective Reinforcement Learning Approach
Adaptive and Learning Agents Workshop, AAMAS (2023)
Equitable Public Transit Network Reduction
Transportation Research Board Annual Meeting (2023)
1st Place, Network Modeling Competition, TRB 2022
Socially-Aware Artificial Intelligence for Fair Mobility
AAAI/ACM Conference on AI, Ethics, and Society (AIES 2022)
Real Time Location Based Sentiment Analysis on Twitter - The AirSent System
10th Hellenic Conference on Artificial Intelligence (2018)
Blog Posts
About Me
I am a Postdoctoral Researcher at the SIAS Group of the University of Amsterdam.
I was previously a PhD Candidate at the Civic AI Lab, and will defend my thesis in February, 2026.
In my research, I use Machine Learning to design inclusive, accessible and sustainable cities.
My current focus is on using Reinforcement Learning to explore the trade-off between utility and equity/fairness when designing public transport networks.
In the near future, I would like to investigate how urban populations would adapt to such changes in a city's public transport networks.
My goal is to create a multi-agent framework that helps to envision alternative urban designs and their evolution in time.
Sounds interesting? Then please, get in touch!
Teaching & Supervision
Master's Students Supervision
-
Riccardo Fiorista (LinkedIn)
Equitable Public Transport Network Reduction: A Reinforcement Learning Approach — MSc Thesis, Spring 2022 -
Paul Stepanov (LinkedIn)
Fairness in Facility Location through Clustering — MSc Thesis, Spring 2022 -
Lodewijk van Keizerswaard (LinkedIn)
Rewarding rationality to improve reinforcement learning models on the metro network design problem — MSc AI Project, Summer 2022 -
Laura Brafine (LinkedIn)
Measuring the Impact of Transportation Systems on Urban Segregation Through Random Walks — MSc Thesis, Spring 2023 -
Leah van Oorschot (LinkedIn)
Where (not) to Cross the Street: Automatically Detecting Crosswalks for Accessible Navigation — MSc Thesis, Spring 2024
Courses Taught
- Reinforcement Learning — Artificial Intelligence MSc (2023)
- Fundamentals of Data Science — Information Studies MSc, Data Science Track (2021, 2022)
- Fairness Accountability Confidentiality and Transparency in AI — Artificial Intelligence MSc (2022, 2023)
(Sub-)Reviewing
- Transportation Research Part C (2024)
- NetSciX (2024)
- AAAI (2024)
- ECAI (2023)
- Multi-Objective Decision Making Workshop (ECAI 2023, ECAI 2024)
- Public Transport Journal (2022)