Ph.D. in Industrial & Systems Engineering, University of Washington
B.S. in Computer Science and Engineering, Shanghai Jiao Tong University
Human-in-the-loop Reinforcement Learning, Human Factors with applications to customized automation, and calibrating human trust in human-robot interaction.
I am a Postdoc Research Fellow in the Department of Industrial and Operations Engineering at the University of Michigan. My long-term research interest is to build customized AI systems as trustworthy teammates to better collaborate with human users in complex decision-making tasks. Specifically, my work aims to design human-in-the-loop learning algorithms leveraging implicit and hidden human feedback to achieve transparent and responsive interaction without interrupting or intruding.
- Liu, Jundi, Linda Ng Boyle, and Ashis G. Banerjee. “An inverse reinforcement learning approach for customizing automated lane change systems.” IEEE Transactions on Vehicular Technology 71.9 (2022): 9261-9271.
- Liu, Jundi, et al. “Clustering Human Trust Dynamics for Customized Real-time Prediction.” 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). IEEE, 2021.
- Liu, Jundi, et al. “A predictive analytics tool to provide visibility into completion of work orders in supply chain systems.” Journal of Computing and Information Science in Engineering 20.3 (2020).
- Liu, Jundi, Linda N. Boyle, and Ashis G. Banerjee. “Predicting interstate motor carrier crash rate level using classification models.” Accident Analysis & Prevention 120 (2018): 211-218. Rahimi, Niyousha, et al. “Auction Bidding Methods for Multiagent Consensus Optimization in Supply–Demand Networks.” IEEE Robotics and Automation Letters 3.4 (2018): 4415-4422.
- Liu, Jundi, et al. “Predicting purchase orders delivery times using regression models with dimension reduction.” International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Vol. 51739. American Society of Mechanical Engineers, 2018.