About Me
Ph.D., McGill University — Bayesian learning, trustworthy AI, and driver world models for multi-agent traffic systems.
I received my Ph.D. in Civil Engineering from McGill University in 2026, advised by Prof. Lijun Sun, and my B.Eng. in Vehicle Engineering from Chongqing University in 2019. I was also a visiting researcher at Carnegie Mellon University with Prof. Changliu Liu in 2023 and Prof. Ding Zhao in 2018, and at UC Berkeley with Prof. Masayoshi Tomizuka from 2019 to 2020.
My research builds trustworthy AI for multi-agent traffic systems, combining Bayesian statistics with generative models to create interpretable driver world models that capture the stochasticity of human behavior — a statistically grounded path toward safe, reliable autonomous systems.
Open to postdoc positions. I’m actively seeking a postdoctoral role focused on stochastic modeling of human social behaviors, dynamic interaction modeling (drivers and pedestrians), and Bayesian approaches to trustworthy, cognitively grounded world models. If this aligns with your group’s interests, I’d be glad to connect.
Featured Research
Traffic Flow Theory & Simulation
- Bayesian calibration of car-following models (IEEE T-ITS)
- Calibrating Car-Following Models via Bayesian Dynamic Regression (ISTTT25 & TR Part C)
Multi-Agent Social Interactions & Driver World Model
- Social interactions for autonomous driving: A review and perspectives (Foundations and Trends® in Robotics)
- Spatiotemporal learning of multivehicle interaction patterns in lane-change scenarios (IEEE T-ITS)
- Interactive Car-Following: Matters but NOT Always (IEEE ITSC23)
Spatiotemporal Data & Interpretable Patterns
- Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behavior (arXiv: 2506.14762)
- Discovering dynamic patterns from spatiotemporal data with time-varying low-rank autoregression (IEEE TKDE)
- Forecasting sparse movement speed of urban road networks with nonstationary temporal matrix factorization (Transportation Science)
Robust Uncertainty & Trustworthy AI
- When Context Is Not Enough: Modeling Unexplained Variability in Car-Following Behavior (ISTTT26)
- Active Simulation-Based Inference for Scalable Car-Following Model Calibration (arXiv: 2602.05246)
Selected Publications
News
- 05/2026 I am delighted to share that I successfully defended my PhD thesis, From Micro Interactions to Traffic Flow: Stochastic Driver Models for Realistic Traffic Simulation, at McGill University. Sincere thanks to my advisor Prof. Lijun Sun, my committee, and everyone who supported me along the way.
- 02/2026 New preprint: "Active Simulation-Based Inference for Scalable Car-Following Model Calibration" — arXiv: 2602.05246.
- 01/2026 Two papers accepted at IEEE IV 2026: "Online Calibration of Context-Driven Car-Following Models" and "AutoTune: A Unified Benchmark for Highway Traffic Microsimulation Calibration."
- 11/2025 Paper accepted at ISTTT26: "When Context Is Not Enough: Modeling Unexplained Variability in Car-Following Behavior" [arXiv]. See you in Munich!
- 06/2025 New preprint: "Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behavior" — arXiv: 2506.14762.
