PhD Candidate @ Delft University of Technology
Hi, I am a Ph.D. candidate at Digitisation & AI for Mobility Network Dynamics (DAIMoND) Lab, Delft University of Technology. My research sits at the intersection of transportation systems and trustworthy AI, with a focus on pedestrian flow prediction, crowd simulation, and reinforcement learning. Previously, I received my master's degree in Computer Science and worked as a research assistant at the Trustworthy Machine Learning Group at HKBU.
I am a PhD candidate at Delft University of Technology, working at the intersection of transportation systems, simulation, and trustworthy AI. My research focuses on intelligent decision-making for human mobility systems, including pedestrian flow prediction, crowd simulation, and reinforcement learning for operational crowd management. I am particularly interested in simulation-driven AI: combining domain knowledge, mathematical modeling, machine learning, and control to support reliable and explainable decisions in complex real-world environments. My long-term goal is to develop trustworthy intelligent systems that help make mobility and public infrastructure safer, more efficient, and more adaptive.
Ph.D. in Transportation & AI
Delft University of Technology
2022 - Present
Beyond research, I am passionate about building production-quality open-source software. Here are some highlights from my experience developing PedNStream.
Published PedNStream as a pip-installable package on PyPI, making it easy for researchers and engineers to install and use with a single command.
Adopted a collaborative Git workflow with feature branching, pull requests, and code reviews. Managed contributions from multiple developers while maintaining code quality and consistency.
Set up documentation, testing, and project scaffolding to ensure long-term maintainability. Structured the project as a proper Python package with clear API design and usage examples.
Weiming Mai, Dorine Duives, Panchamy Krishnakumari, Serge Hoogendoorn
IEEE Transactions on Intelligent Transportation Systems, 2025
Weiming Mai, et al.
Transportation Research Part C: Emerging Technologies, 2025
Weiming Mai, et al.
ACM Transactions on Knowledge Discovery from Data, 2023
Started as a Machine Learning Engineer Intern at Woven by Toyota, focusing on data engineering and intelligent systems to build a guardrail system for the experimentation platform.
Apr 2026
Released PedNStream - a light-weight Python-native pedestrian traffic simulation tool based on the Link Transmission Model.
Oct 2025
Our research on pedestrian flow prediction with diffusion behavior was published in Transportation Research Part C.
May 2025
Our team participated in the Maritime Shipping Competition at AAMAS 2025, developing intelligent agents for trade auction bidding and fleet scheduling.
Mar 2025
Our paper on evaluating crowd flow forecasting algorithms was published in IEEE Transactions on Intelligent Transportation Systems.
Pedestrian Simulation
A light-weight Python-native pedestrian traffic simulation tool based on the Link Transmission Model (LTM). Enables modeling of pedestrian movements through complex networks with visualization tools.
IEEE T-ITS 2025
Evaluating crowd flow forecasting algorithms for indoor pedestrian spaces using a synthetic dataset. Features train station simulation with multiple scenarios.
TRC 2025
Novel model for pedestrian flow prediction using crowd diffusion theory. Features graph-based representation with GNN and online learning for real-time adaptation.
ACM TIST 2023
Server-Client Collaborative Distillation for Federated Reinforcement Learning. Addresses objective heterogeneity in FL while protecting data privacy.
AAMAS 2025 Competition
Maritime Shipping Competition solution for AAMAS 2025. Combines OR-Tools CP-SAT solver with intelligent bidding strategies for optimal fleet scheduling.
LLM Course Materials
This repository contains a complete T5-based transformer system for the ACM SIGSPATIAL GIS Cup 2025 challenge on human mobility prediction. The system predicts future trajectory coordinates given past movement patterns and temporal context.
Neural Network for MT4
A Neural Network Development Kit for MT4 (MetaTrader 4). Implements neural network architectures in C++ for algorithmic trading and financial prediction applications.
Medical Image Processing
Low-count PET image reconstruction using deep learning methods including SAGAN, MAPEM-Net, and U-Net denoising for medical imaging applications.
Feel free to reach out for collaborations, research discussions, or inquiries. I'm always interested in connecting with fellow researchers and practitioners in transportation, AI, and related fields.
Affiliation
Delft University of Technology, Netherlands
GitHub
github.com/WaimenMakLocation
Netherlands