Chuan Li — AI Researcher in Mobility, Geospatial Intelligence & Intelligent Transportation

Hi! I’m Chuan Li, an AI researcher specializing in intelligent mobility, geospatial artificial intelligence, graph learning, and data-driven decision systems.

I am currently a Postdoctoral Researcher at Institut VEDECOM, where I contribute to the Horizon Europe MetaCCAZE project. My work focuses on urban mobility demand forecasting, park-and-ride optimisation, intelligent transportation systems, traffic simulation, and generative mobility scenarios.

I am also completing my Ph.D. in Computer Science at Sorbonne University, with research affiliations including LIPADE at Université Paris Cité and SAMOVAR at Institut Polytechnique de Paris.

My research interests include:

• Large-scale optimisation for intelligent transportation systems
• Urban mobility modelling and spatiotemporal forecasting
• Geospatial AI and graph neural networks
• Electric-vehicle charging infrastructure planning
• Human mobility and mobile-data analytics
• AI agents and decision-support systems
• Digital contact tracing and public-health analytics

I have also taught computer science and artificial intelligence courses at Polytech Sorbonne, ENSTA Paris, and ESIEE Paris, covering algorithms, C/C++/Java programming, computer architecture, and deep reinforcement learning.

💼 Current Position

Postdoctoral Researcher — Institut VEDECOM

I contribute to MetaCCAZE, a European Union Horizon Europe project developing innovative solutions for connected, cooperative, and automated mobility.

My current work includes:

• Urban mobility demand forecasting
• Park-and-ride planning and optimisation
• Intelligent transportation systems and traffic simulation
• Generation of realistic mobility and traffic scenarios
• Data-driven decision tools for sustainable urban mobility
• Applications of machine learning and generative AI to transport systems

🎓 Education

• Ph.D. in Computer Science, Sorbonne University; Université Paris Cité & Institut Polytechnique de Paris, 2022–2025
• Business Foundations Programme, INSEAD
• MITx MicroMasters Program in Statistics and Data Science, Massachusetts Institute of Technology
• M.Eng. in Computer Science, Polytech Sorbonne
  GPA: 17.83/20, graduated in the Top 5% of the cohort
• B.Sc., Université de Poitiers

🔬 Research Highlights

Intelligent Mobility and Transportation

My research combines large-scale geospatial data, optimisation, machine learning, graph modelling, and simulation to address real-world mobility challenges.

Selected topics include:

• Optimisation of electric-vehicle charging infrastructure
• Urban travel-demand forecasting
• Spatiotemporal graph neural networks
• Parking-demand prediction and park-and-ride systems
• Mobility behaviour modelling using large-scale mobile data
• Equity, accessibility, congestion, and infrastructure coverage
• Generative traffic and mobility scenarios

Public Health and Human Mobility

I have also worked on real-world human-contact and mobility datasets to study:

• Digital contact tracing
• Infection-risk modelling
• Empirical contact networks
• Mobility-based epidemiological indicators
• Graph-based and physics-informed learning for public-health applications

📚 Selected Publications and Research Outputs

• Large-Scale Optimisation of EV Charging Infrastructure ACM SIGSPATIAL 2024 — Oral Presentation and ACM GIS Cup Award

• Assessing the Usefulness of Digital Contact Tracing Using Real-World Contact Data Accepted in Scientific Reports, Nature Portfolio

• Semi-Automatic Correction of 3D Tubular Structure Skeletons via Component-Wise MST and Filtered Delaunay Triangulation Accepted for oral presentation at ACM ICMR 2026

• TDLG at Scale: Large-Scale Temporal Graph Modelling of Human Mobility Accepted at NetMob

• Fine-Grained Urban-Grid Clustering for Mobility Analysis Accepted at IJCNN

• Groundwater Early-Warning Research Accepted at the ICML NewInML Workshop

Ongoing research topics include:

• Spatiotemporal mobility forecasting
• Distribution shift and robust graph learning
• AI agents for scientific and professional workflows
• Privacy-aware patent-analysis assistants
• Agent-ready conversational commerce systems

🧑‍🏫 Teaching Experience

Teaching highlights: Designed 11 Java assessments, supervised more than 10 practical examinations, taught approximately 140 equivalent tutorial hours, and mentored reinforcement-learning projects using PyTorch and OpenAI Gym.

🏆 Awards and Distinctions

• 🥇 1st Place — Cursor Track, RAISE Summit Hackathon 2026 Developed AssembleAI, an AI-powered assembly assistant combining computer vision, voice interaction, 3D visualisation, and augmented reality.

• 🏆 ACM GIS Cup Award — ACM SIGSPATIAL 2024 Large-Scale Optimisation of EV Charging Infrastructure

• 🥇 1st Place — ACM SIGSPATIAL GIS Cup 2024 Electric-vehicle charging infrastructure optimisation 📄 View Certificate

• 🥉 3rd Place — Datathon Sécurité Ferroviaire 2025 Rail-risk prediction using machine learning and mobility data

• 🧠 Top 5 — Mistral AI × Alan Hackathon Healthcare retrieval-augmented generation assistant

• 🏅 Honorable Mention — Hi! PARIS Hackathon 2024 AI for Society 📄 View Certificate

🚀 Innovation and Entrepreneurship

Beyond academic research, I actively develop AI-powered products and decision-support systems.

Recent projects include:

• AssembleAI — Multimodal AI assistant for complex assembly tasks
• EV Charger Planner — Geospatial decision platform for charging-infrastructure planning
• PatentFlow / ClaimPilot — Privacy-aware agentic workspaces for patent analysis and prosecution
• IntentPay — Agent-ready conversational commerce with programmable micropayments
• AI applications combining large language models, graph learning, computer vision, simulation, and geospatial analytics

I am involved in the INSEAD Alumni and Pépite Sorbonne Université entrepreneurship ecosystems and am interested in transforming scientific research into scalable real-world products.

🤝 Research and Collaboration

I am open to collaborations in:

• Geospatial AI and GeoAI
• Intelligent transportation systems
• Graph neural networks and spatiotemporal learning
• Electric mobility and charging infrastructure
• Human mobility and urban analytics
• Generative AI and AI-agent systems
• Horizon Europe and international research projects
• Research commercialisation and deep-tech entrepreneurship

📄 CV & Links

• 📥 Download CV (PDF)
• 📚 Google Scholar
• 🐙 GitHub
• 💼 LinkedIn

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