Curriculum Vitae

Tianyu Shi

Ph.D. Candidate in Transportation Engineering
University of Toronto

📧 ty.shi@mail.utoronto.ca
🌐 https://shitianyu-hue.github.io/
📚 Google Scholar | H-index: 7 | Citations: 255

Education

Ph.D. in Transportation Engineering
University of Toronto | September 2021 - Present

  • Supervisor: Prof. Baher Abdulhai
  • Research: Deep reinforcement learning for intelligent transportation systems

Master of Engineering (MEng), Thesis Option
McGill University | January 2020 - September 2021

  • GPA: 3.8/4.0
  • Research: Deep reinforcement learning, Robotics, Intelligent transportation systems

Bachelor of Engineering in Mechanical Engineering
Beijing Institute of Technology | September 2015 - July 2019

  • Major GPA: 3.7/4.0, Overall Ranking: 1/66
  • First Class Honors Graduate

Selected Publications

Journal Articles

  1. T. Shi*, O. Elsamadisy*, I. Smironov, B. Abdulhai (2024). “SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving.” IET Intelligent Transport Systems.

  2. T. Shi, F.X. Devailly, D. Larocque, L. Charlin (2024). “Improving the generalizability and robustness of large-scale traffic signal control.” IEEE Open Journal of ITS.

  3. Y. Ma*, T. Shi*, et al. (2019). “A Comprehensive Evaluation of NEV Development.” Journal of Cleaner Production, 214:389-402.

Conference Proceedings

Selected from 15+ conference papers:

  1. T. Shi, Y. Ai, B. Abdulhai (2022). “Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model.” IEEE ITSC.

  2. T. Shi*, J. Wang*, et al. (2021). “Multi-agent Graph Reinforcement Learning for Connected Automated Driving.” ICML Workshop.

  3. T. Shi*, D. Chen, et al. (2021). “Offline Reinforcement Learning for Autonomous Driving with Safety and Exploration Enhancement.” NeurIPS Workshop.

Honors & Awards

  • 2023 - Silver Award, Kaggle Google Research Competition
  • 2023 - DiDi Autonomous Driving Graduate Award (2 recipients at UofT)
  • 2022 - Dr. Mazen Hassounah Graduate Scholarship
  • 2022 - University of Toronto Fellowship
  • 2021 - Graduate Merit-Based Entrance Scholarship (5 recipients at UofT)
  • 2020 - IVADO Excellence Scholarship (5 recipients at McGill)
  • 2020 - Graduate Excellence Fellowship, McGill
  • 2019 - MIIT Scholarship for Scientific Innovation (10 winners at BIT)
  • 2018 - First Prize of CASC Scholarship (1 winner in department)
  • 2018 - SWAT Scholarship (10 winners in department)
  • 2017 - Fast Gear Scholarship (1 winner in major)
  • 2016 - North Industry Scholarship for All-round Development

Research Experience

Ph.D. Student | Toronto ITS Centre, University of Toronto
September 2021 - Present | Supervisor: Prof. Baher Abdulhai

  • Developing bilateral car following models for mixed-autonomy traffic
  • Investigating reinforcement learning for traffic optimization

Research Assistant | Faculty of Nursing, University of Toronto
April 2022 - Present | Supervisors: Prof. C. Chu & Prof. S.S. Khan

  • Investigating age-related bias in AI systems
  • Developing fairness-aware machine learning models

Research Intern | Mila - Quebec AI Institute
August 2020 - September 2021 | Supervisors: Prof. L. Charlin & Prof. D. Larocque

  • Improved robustness of RL for traffic signal control

Research Assistant | Berkeley Deep Drive, UC Berkeley
July 2018 - September 2018 | Supervisors: Dr. C.Y. Chan & Dr. P. Wang

  • Developed deep reinforcement learning for automated driving

Industry Experience

Research Intern | Momenta.AI, Beijing
January 2019 - September 2019

  • Data-driven motion planning for L4 autonomous vehicles
  • Hierarchical imitation learning for lane change decisions

Research Intern | Megvii Technology, Beijing
September 2019 - December 2019

  • Neural architecture search for efficient networks
  • Improved ResNet performance with attention mechanisms

Teaching Experience

Teaching Assistant | University of Toronto & McGill University
2020 - Present

  • Courses: Machine Learning, AI, Reinforcement Learning, Transportation Engineering
  • 10+ courses taught, 500+ students mentored
  • Outstanding TA evaluations (4.7-4.8/5.0)

Professional Service

Reviewer

  • NeurIPS, IEEE T-ITS, Transportation Research Part C, IEEE ITSC, IEEE IV, ACM SIGKDD

Invited Talks

  • “Distributional RL for Traffic Signal Control” - UofT ITS Lab, UT Austin (2021)
  • “Multi-agent Graph RL for Connected Driving” - TRB, CMU, UCLA (2021)

Technical Skills

Programming: Python, C/C++, MATLAB
ML Frameworks: PyTorch, TensorFlow
Tools: Git, Docker, ROS, SUMO, CARLA
Languages: English (Fluent), Mandarin (Native)

Download PDF Version | Last Updated: January 2024