About Me
Postdoctoral Scholar & Incoming Assistant Professor
I am currently a Postdoctoral Scholar at Stanford University working with Prof. Mykel J. Kochenderfer at Stanford Intelligent Systems Laboratory (SISL), Stanford Center for AI Safety, and Stanford Artificial Intelligence Laboratory (SAIL). I obtained a Ph.D. degree from the University of California, Berkeley working with Prof. Masayoshi Tomizuka at Mechanical Systems Control Laboratory, Berkeley DeepDrive (BDD), and Berkeley AI Research (BAIR).
Attention! I will join the ECE Department and the CSE Department at UC Riverside as a tenure-track assistant professor! I am also the Director of Trustworthy Autonomous Systems Laboratory (TASL). I am actively looking for multiple highly motivated Ph.D. students (fully funded), master students, undergraduate students, and research interns to join my lab. If you are interested, please follow the application instructions HERE. Feel free to send me an email if any questions.
Research Interest
My research interest lies in the broad intersection of robotics, trustworthy AI & ML, reinforcement learning, control and optimization and their applications to intelligent autonomous systems (e.g., autonomous vehicles, mobile robots, drones, cyber-physical systems). I am particularly interested in human-robot interactions and multi-agent systems.
Please refer to the Research section or my lab website for more details about my recent research!
I am open to research discussion and collaboration, please feel free to get in touch!
News
- 08/2023: Selected as an ASME DSCD Rising Star and will give a talk at MECC 2023!
- 06/2023: Invited talk at IEEE IV 2023 Workshop on Social and Interactive and Safe Behaviors for Intelligent Vehicles: Benchmark, Models, and Applications!
- 06/2023: Organizing three IEEE IV 2023 workshops: Workshop on Socially Interactive Autonomous Mobility (SIAM) , Workshop on Interaction-driven Behavior Prediction and Planning for Autonomous Vehicles, and Workshop on 3D-Deep Learning for Automated Driving!
- 04/2023: I am thrilled to accept an offer of a Tenure-Track Assistant Professor position at the University of California, Riverside and will officially join UCR in January 2024!
- 01/2023: One paper about pedestrian action recognition and motion prediction is accepted at ICRA 2023!
- 10/2022: Serving on the program committee of RSS 2023 Pioneers Workshop at RSS 2023!
- 09/2022: Two papers about interaction modeling and physical dynamics modeling are accepted at NeurIPS 2022!
- 08/2022: Organizing NeurIPS 2022 Workshop on Machine Learning for Autonomous Driving and NeurIPS 2022 Workshop on Progress and Challenges in Trustworthy Embodies AI!
- 07/2022: One paper about occupancy prediction is accepted at IROS 2022!
- 06/2022: Invited talk in the MediaBrain Laboratory at Shanghai Jiao Tong University!
- 05/2022: Selected as one of the 30 Robotics Pioneers at the RSS 2022 Pioneers Workshop!
- 03/2022: One paper regarding diverse human motion forecasting is accepted as Oral presentation at CVPR 2022!
- 01/2022: Two papers regarding scene understanding and behavior modeling are accepted to ICRA 2022!
- 11/2021: Serving as a member of Program Committee for 4th Annual Learning for Dynamics & Control Conference!
- 11/2021: I joined Stanford Intelligent Systems Laboratory (SISL) as a postdoctoral scholar!
- 08/2021: One paper regarding continual learning for trajectory prediction is accepted to IEEE Robotics and Automation Letters!
- 08/2021: Organizing NeurIPS 2021 Workshop on Machine Learning for Autonomous Driving
- 07/2021: Two papers are accepted to ICCV 2021! Preprints are coming soon.
- 06/2021: One paper regarding graph-based multi-agent prediction and tracking is accepted to IEEE Transactions on Intelligent Transportation Systems!
- 06/2021: Keynote talk on "Multi-Agent Relational Reasoning for Autonomous Driving" at IV 2021 workshop!
- 06/2021: One paper regarding self-supervised learning for trajectory forecasting is accepted at ITSC 2021!
- 06/2021: One paper regarding a survey of autonomous driving strategies at intersections is accepted at ITSC 2021!
- 06/2021: One paper regarding motion planning of mobile robots is accepted at IROS 2021!
- 05/2021: Organizing IROS 2021 Workshop on Multi-Agent Interaction and Relational Reasoning
- 04/2021: Organizing ICCV 2021 Workshop on Multi-Agent Interaction and Relational Reasoning
- 03/2021: Invited talk titled "Relational Reasoning for Multi-Agent Systems" at Carnegie Mellon University!
- 02/2021: One paper regarding shared cross-modal trajectory forecasting is accepted at CVPR 2021 (Oral)!
- 02/2021: One paper regarding multi-agent decision making is accepted at ICRA 2021!
- 02/2021: One paper regarding multi-agent trajectory forecasting is accepted at ICRA 2021!
- 09/2020: One paper regarding multi-agent trajectory prediction and relational reasoning is accepted at NeurIPS 2020!
- 09/2020: Recognized as a Top Reviewer for ICML 2020!
- 06/2020: Organizing IEEE IV 2020 Workshop on Behavior Prediction and Generation for Autonomous Driving!
- 07/2019: One paper regarding a generic tracking and prediction framework is accepted to IEEE Transactions on Intelligent Transportation Systems!
- 06/2019: Organizing IEEE IV 2019 Workshop on Prediction and Decision Making for Autonomous Driving!
- 05/2019: One paper regarding deep generative models for behavior prediction is accepted at IROS 2019!
- 01/2019: One paper regarding generative adversarial networks is accepted at ICRA 2019!
Curriculum Vitae
Click here to download my full CV (08/2023).
Research
The ultimate goal of my research is to build trustworthy and interactive autonomous agents that can perceive, understand, and reason about the physical world; safely interact and collaborate with humans and other agents; and clearly explain their behaviors to build trust with humans so that they can benefit society in daily lives. To achieve this goal, I have been pursuing interdisciplinary research and unifying the techniques and tools from robotics, machine learning, reinforcement learning, explainable AI, control theory, optimization, and computer vision.
Selected Research Topics
Explainable Relational Reasoning and Multi-Agent Interaction Modeling (Social & Physical)

I investigate relational reasoning and interaction modeling in the context of the trajectory prediction task, which aims to generate accurate, diverse future trajectory hypotheses or state sequences based on historical observations. My research introduced the first unified relational reasoning toolbox that systematically infers the underlying relations/interactions between entities at different scales (e.g., pairwise, group-wise) and different abstraction levels (e.g., multiplex) by learning dynamic latent interaction graphs and hypergraphs from observable states (e.g., positions) in an unsupervised manner. The learned latent graphs are explainable and generalizable, significantly improving the performance of downstream tasks, including prediction, sequential decision making, and control. We also proposed a physics-guided relational learning approach for physical dynamics modeling.
Related Publications:

Improving Generalizability by Learning Context Relations
How to generalize the prediction to different scenarios is largely underexplored. In contrast to recent works which use the Cartesian coordinate system and global context images directly as input, we propose to leverage human prior knowledge including the comprehension of pairwise relations between agents and pairwise context information extracted by self-supervised learning approaches to attain an effective Frenet-based representation. We demonstrate that our approach achieves superior performance in terms of overall performance, zeroshot and few-shot transferability across different traffic scenarios with diverse layouts.
Related Publications:

Interaction-Aware Decision Making and Model-Based Control
Although autonomous navigation in simple, static environments has been well studied, it remains challenging for robots to navigate in highly dynamic, interactive scenarios (e.g., intersections, narrow corridors) where humans are involved. Robots must learn a safe and efficient behavior policy that can model the interactions, coordinate with surrounding static/dynamic entities, and generalize to out-of-distribution (OOD) situations. My research introduced a novel interaction-aware decision making framework for autonomous vehicles based on reinforcement learning, which integrates human internal state inference, domain knowledge, trajectory prediction, and counterfactual reasoning in a systematic manner. I also investigate model-based control methods that leverages the learned pairwise and group-wise relations for social robot navigation around human crowds. Both methods achieve superior performance in the corresponding tasks in terms of a wide range of evaluation metrics and provide explainable, human-understandable intermediate representations to build both users’ and developers’ trust.
Related Publications:

Continual / Lifelong Learning from Incremental Data
The current mainstream research focuses on how to achieve accurate prediction on one large dataset. However, whether the multi-agent trajectory prediction model can be trained with a sequence of datasets, i.e., continual learning settings, remains a question. Can the current prediction methods avoid catastrophic forgetting? Can we utilize the continual learning strategy in the multi-agent trajectory prediction application? Motivated by the generative replay methods in continual learning literature, we propose a multi-agent interaction behavior prediction framework with a graph-neural-network-based conditional generative memory system to mitigate catastrophic forgetting. To the best of our knowledge, this work is the first attempt to study the continual learning problem in multi-agent interaction behavior prediction problems. We empirically show that several approaches in literature indeed suffer from catastrophic forgetting, and our approach succeeds in maintaining a low prediction error when datasets come in a sequential way.
Related Publications:

Explainable Scene Understanding and Situational Awareness
Inferring the short-term and long-term intentions of traffic participants and understanding the contextual semantics of scenes are the key to scene understanding and situational awareness of autonomous vehicles. Moreover, how to enable autonomous agents (e.g., self-driving cars) to explain their reasoning, prediction, and decision making processes to human users (e.g., drivers, passengers) in a human understandable form (e.g., natural language) to build humans’ trust remains largely underexplored. Therefore, together with Honda, we created the first multimodal dataset for a new risk object ranking and natural language explanation task in urban scenarios and a rich dataset for intention prediction in autonomous driving, establishing benchmarks for corresponding tasks. Meanwhile, my research introduced novel methods that achieve superior performance on these problems.
Related Publications:

Improving Diverse Prediction and Generation with Deep Generative Models
The objective of generative models is to approximate the true data distribution, with which one can generate new samples similar to real data points with a proper variance. Generative models have been widely employed in representation learning and distribution approximation. We designed novel trajectory or human skeleton motion prediction methods based on deep generative models, which generate accurate and diverse prediction hypotheses. These methods can be broadly applied to time series prediction problems.
Related Publications:

Improving State Estimation with Learning-Based Models
I proposed a constrained mixture sequential Monte Carlo method that mitigates mode collapse in sequential Monte Carlo methods for tracking multiple targets and significantly improves tracking accuracy. Since prediction is a step in state estimation, I also proposed that the prior update in the state estimation framework can be implemented with any learning-based interaction-aware prediction model. The results in complex traffic scenarios show that using the prediction model outperforms purely physical models by a large margin due to the capability of relational reasoning. In particular, our method performs significantly better when handling missing or noisy sensor measurements.
Related Publications:
Publications
Under Review / Preprints
2023
2022
2021
2020
2019
2018
2017
2016
* indicates equal contribution/advising
Academic Services
Program Committee
- Lead-organizer and Co-organizer of Workshop at Advances in Neural Information Processing Systems (NeurIPS), 2021-2022
- Lead-organizer of Workshop at International Conference on Computer Vision (ICCV), 2021
- Co-organizer of Workshop at IEEE Conference on Robotics and Systems (IROS), 2021
- Co-organizer of Workshops at IEEE Intelligent Vehicles Symposium (IV), 2019, 2020, 2021
- Co-organizer of Workshop at IEEE Intelligent Transporation Systems Conference (ITSC), 2021
- Associate Editor at IEEE Intelligent Vehicles Symposium, 2020, 2021
- Program Committee of 4th Annual Learning for Dynamics & Control Conference, 2022
- Program Committee of Workshop on Cooperative AI at NeurIPS 2021
- Program Committee of Workshop on AI for Autonomous Driving at IJCAI 2021
Journal Reviewer
- IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), 2022 -- Present
- Transactions of Machine Learning Research, 2022 -- Present
- IEEE Transactions of Industrial Informatics (T-II), 2022 -- Present
- Transportation Research Part C: Emerging Technologies, 2022 -- Present
- IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2020 -- Present
- IEEE Transactions on Robotics (T-RO), 2021 -- Present
- IEEE Robotics and Automation Letters (RA-L), 2021 -- Present
- IEEE Transactions on Vehicular Technology (T-VT), 2021 -- Present
- IEEE Intelligent Systems, 2021 -- Present
- Neurocomputing, 2021 -- Present
- Signal Processing, 2021 -- Present
- Neural Computation and Applications (NCAA), 2020 -- Present
- IEEE Transactions on Mechatronics (T-MECH), 2019 -- Present
- IEEE Transactions on Intelligent Vehicles (T-IV), 2018 -- Present
- IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2018 -- Present
- IEEE Transactions on Industrial Electronics (T-IE), 2017 -- Present
Conference Reviewer
- AAAI Conference on Artificial Intelligence (AAAI), 2023
- Conference on Robot Learning (CoRL), 2022
- European Conference on Computer Vision (ECCV), 2022
- IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
- International Conference on Learning Representations (ICLR), 2022 -- 2023
- ACM International Conference on Information and Knowledge Management (CIKM), 2022
- International Conference on Computer Vision (ICCV), 2021
- Adcances in Neural Information Processing Systems (NeurIPS), 2020 -- 2021
- International Conference on Machine Learning (ICML), 2020 -- 2022
- IEEE International Conference on Robotics and Automation (ICRA), 2019 -- 2022
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019 -- 2022
- IEEE Intelligent Vehicles Symposium (IV), 2019 -- 2022
- IEEE Intelligent Transportation Systems Conference (ITSC), 2019 -- 2022
- IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021
Research Mentoring
- Undergraduate students, 2019 -- Present
- Graduate students, 2020 -- Present
Join My Lab
I am currently seeking multiple highly motivated talents to join my laboratory as Ph.D. students (fully funded), master students, undergraduate students, or onsite/remote research interns (outside of UC Riverside), visiting scholars, or postdoctoral scholars. If you are interested in working with me, please refer to my lab website for application instructions.
Prospective students must also submit an application for a certain program on the UCR official website before the corresponding deadline. If you are applying for a Ph.D. program in the ECE or CSE departments, please indicate Prof. Jiachen Li as your prospective advisor in the Statement of Purpose.