I’m a fourth-year Ph.D. candidate in Centre for Intelligent Machines at McGill University, co-supervised by Prof. Lijun Sun @McGill and Prof. Seongjin Choi @UMN. Prior to that, I received my Master’s degree in Electrical Computer Engineering (ECE) and Computational Science and Engineering (CSE) from the Georgia Institute of Technology in 2021. I earned my Bachelor’s degree in Electrical Engineering from Northeastern University in 2019. I was also a visiting student at University of Cambridge in Sidney Sussex College in 2018.

My research explores the intersection of Machine Learning, Large Language Models and Autonomous Systems, with a strong focus on Robustness. My work spans a range of topics including reinforcement learning, Vision-Language Models (VLMs) and Vision-Language-Action (VLA) models for autonomous vehicles. I’m especially interested in understanding how autonomous systems maintain reliable performance—particularly in risky or long-tail scenarios—and how Language Model-based methods can enhance control, perception, and decision-making under uncertainty.

📬 I’m always open to collaborations—please feel free to reach out!

📢 I am actively looking for industrial internships and full-time opportunities. I am happy to engage in discussions regarding potential opportunities!

News

August 2025
One paper AgentThink got accepted at EMNLP 2025!

August 2025
I’m thrilled to join the 2077AI-Foundation—contributing to an open-source ecosystem that powers next-gen foundation-model datasets for everyone! 🌟 Let’s build the future of AI together!

August 2025
Excited to start my Applied Scientist internship at Abaka AI in Mountain View, California! 🚀 I’ll be working on next-generation auto-labeling frameworks for multimodal foundation models and autonomous driving data — open to collaboration and discussion!

July 2025
Our survey paper “A Survey on Vision-Language-Action Models for Autonomous Driving” is accepted at Foundation Models for Autonomous Driving on ICCV 2025.

June 2025
We released the first comprehensive survey on Vision–Language–Action (VLA) models for autonomous driving, in collaboration with Tsinghua University, Xiaomi, and the University of Wisconsin–Madison.
📄 Paper Link
🌟 The companion repo Awesome-VLA4AD received over 100 stars within the first three days!
📰 Coverage by several tech media outlets: [AutoDriving Heart] [Embodied Evolution] [Zhihu]

May 2025
Our collaborative work with Tsinghua and Xiaomi, AgentThink: A Unified Framework for Tool-Augmented VLM Reasoning in Autonomous Driving! This is the first framework that integrates tool-augmented VLM reasoning in driving tasks — thank you Xiaomi for the support!
📄 Paper Link

May 2025
I presented our paper Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting at AISTATS 2025.
This is the first work systematically investigating the robustness of LLMs in time series forecasting.
📄 Paper Link
🚀 Github Repo

April 2025
I was invited to contribute to the Humanity’s Last Exam benchmark — a milestone initiative for evaluating AGI safety. Grateful to be part of this important effort!

March 2025
We released FASIONAD, a new exploration of fast-slow uncertainty switching in autonomous driving, in collaboration with Tsinghua University.
📄 Paper Link

Feb 2025
Our work Temporally Sparse Attack for Fooling Large Language Models in Time Series Forecasting was accepted to the ICLR 2025 Trustworthy LLM Workshop!
📄 Paper Link

Recent Publications

• S. Jiang*, Z. Huang*, K. Qian*, Z. Luo, T. Zhu, et al.
  A Survey on Vision–Language–Action Models for Autonomous Driving.
  ICCV-W 2025
  PDF GitHub

• K. Qian*, S. Jiang*, Y. Zhong*, Z. Luo, Z. Huang, et al.
  AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving.
  EMNLP 2025
  PDF

• F. Liu*, S. Jiang*, L. Miranda-Moreno, S. Choi, L. Sun
  Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting.
  AISTATS 2025
  PDF GitHub

• Z. Luo*, S. Jiang*, K. Qian*, Z. Huang, J. Miao, et al.
  FasionAD+: Integrating High-level Instruction and Information Bottleneck in Fat–Slow Fusion Systems for Enhanced Safety in Autonomous Driving.
  Under review, 2025.
  PDF

• S. Jiang, S. Choi, L. Sun
  Communication-Aware Reinforcement Learning for Cooperative Adaptive Cruise Control.
  TRB Annual Meeting (Oral), 2024.
  PDF

(* equal contribution)

Awards

• McGill Engineering Doctoral Award (MEDA), 2021–2024
• TISED Doctoral Recruitment Award (DRA), McGill University, 2021
• Outstanding Graduate of Liaoning Province, 2019
• Most Influential Graduate, Northeastern University, 2019
• 1st Class Academic Scholarship, Northeastern University, 2018
• National 1st Prize, China Undergraduate Mathematical Contest in Modeling, 2017
• 1nd Class Academic Scholarship, Northeastern University, 2016

Academic Service

Conference Reviewer:

• NeurIPS (Neural Information Processing Systems) 2024
• AISTATS (International Conference on Artificial Intelligence and Statistics) 2025
• IROS (IEEE/RSJ International Conference on Intelligent Robots and Systems) 2021, 2023, 2025
• RO-MAN (IEEE International Conference on Robot and Human Interactive Communication) 2024, 2025
• AIM (IEEE/ASME International Conference on Advanced Intelligent Mechatronics) 2024, 2025
• ICRA (IEEE International Conference on Robotics and Automation) 2023, 2024
• COLM (Conference on Language Modeling) 2025
• IV (IEEE Intelligent Vehicles Symposium) 2024
• ITSC (IEEE International Conference on Intelligent Transportation Systems) 2024
• AAAI (Association for the Advancement of Artificial Intelligence) 2023, 2024

Journal Reviewer:

• IEEE Robotics and Automation Letters (RA-L)
• Transportation Research Part C: Emerging Technologies (TRC)
• IEEE Transactions on Intelligent Transportation Systems (T-ITS)