Leizhen Wang

Leizhen Wang

he/him

PhD Candidate in Data Science and Artificial Intelligence at Monash University. Research interests include Large Language Models, Reinforcement Learning, and Urban Transportation Systems.

Leizhen Wang - PhD Candidate at Monash University

About Me

I am a PhD candidate in Data Science and Artificial Intelligence at Monash University, with a research focus on Large Language Models (LLMs), Reinforcement Learning, and Urban Transportation Analysis and Optimization. My work lies at the intersection of artificial intelligence and transportation engineering, aiming to improve the efficiency and sustainability of transport systems.

Currently, I am also a visiting PhD student at the KTH Royal Institute of Technology (2025–2026).

Education

  • KTH Royal Institute of Technology
    Visiting PhD Student (2025-2026)

  • Monash University
    PhD in Data Science and AI (2022–2026)
    Research areas: Large Language Models, Reinforcement Learning, Urban Transportation, Public Transport

  • Southeast University
    Master’s degree in Transportation Engineering (2018–2021)

  • Shandong Jiaotong University
    Bachelor’s degree in Transportation Engineering

Selected Papers

Agentic Large Language Models for Day-to-Day Route Choices

L Wang, P Duan, Z He, C Lyu, X Chen, N Zheng, L Yao, Z Ma. (2025), Transportation Research Part C: Emerging Technologies
📄 Paper | 💻 Code

Scalable and Reliable Multi-agent Reinforcement Learning for Traffic Assignment

L Wang, P Duan, C Lyu, Z Wang, Z He, N Zheng, Z Ma. (2025), Communications in Transportation Research
📄 Paper | 💻 Code

EvolveSignal: A Large Language Model Powered Coding Agent for Discovering Traffic Signal Control Algorithms

L Wang, P Duan, H Wang, Y Wang, J Xu, N Zheng, Z Ma. (2025), Under Review
📄 Paper

Reinforcement learning-based sequential route recommendation for system-optimal traffic assignment

L Wang, P Duan, C Lyu, Z Ma. (2025), Under Review
📄 Paper

Data-driven analysis and modeling of individual longitudinal behavior response to fare incentives in public transport

L Wang, X Chen, Z Ma, P Zhang, B Mo, P Duan. (2024), Transportation
📄 Paper

Human‐centric multimodal deep (HMD) traffic signal control

L Wang, Z Ma, C Dong, H Wang. (2023), IET Intelligent Transport Systems
📄 Paper

For a full and up-to-date list of my publications: Google Scholar profile.

Experience

  • Senior AI Engineer
    Bosch (Full-time), 2021–2022
    AI-based parameter tuning for vehicle application and simulation calibration.

  • Teaching Assistant

    • Head TA, Monash University (2022–2025)
      • FIT5215 Deep Learning: led practice classes on neural networks (Python/PyTorch/TensorFlow), graded assignments and exams.
      • FIT5225 Cloud Computing and Security: designed and conducted practice classes on cloud computing, graded assignments and exams.
    • Teaching Assistant, KTH Royal Institute of Technology (2024–2025)
      • AH2178-H22 AI for Transportation: designed and implemented practice classes on Large Language Models and Reinforcement Learning.
    • Teaching Assistant, Monash University (2021–2022)
      • ENG5005 Research Project: supervised adaptive traffic signal control projects using deep reinforcement learning, mentored students on programming and writing.
      • ENG5305 Transport Demand Modelling: conducted practice classes on four-step travel demand modeling with Python.
      • ENG5001 Advanced Engineering Data Analysis: led practice classes on taxi demand prediction using large-scale New York taxi datasets in Python.

Research Interests

  • Large Language Models (LLMs)
  • Reinforcement Learning
  • Urban Transportation Analysis and Optimization
  • Public Transport

Find Me Online

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