Jundi Liu
Email: jundiliu@iastate.edu
Phone: 515-294-2002
Title(s):
Assistant Professor
Office
3016 Black Engineering Building
Information
Links
Research Interest
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.
Publications
- Liu, J., & Boyle, L. N. (2022, September). Analysis of driver behavior in mixed autonomous and non-autonomous traffic flows. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 66, No. 1, pp. 1447-1451). Sage CA: Los Angeles, CA: SAGE Publications.
- Liu, J., Boyle, L. N., & Banerjee, A. G. (2022). An inverse reinforcement learning approach for customizing automated lane change systems. IEEE Transactions on Vehicular Technology, 71(9), 9261-9271.
- Liu, J., Akash, K., Misu, T., & Wu, X. (2021, September). Clustering human trust dynamics for customized real-time prediction. In 2021 ieee international intelligent transportation systems conference (ITSC) (pp. 1705-1712). IEEE.
- Liu, J., Hwang, S., Yund, W., Neidig, J. D., Hartford, S. M., Ng Boyle, L., & Banerjee, A. G. (2020). 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), 031003.
- Liu, J., Boyle, L. N., & Banerjee, A. G. (2018). Predicting interstate motor carrier crash rate level using classification models. Accident Analysis & Prevention, 120, 211-218.
- Rahimi, N., Liu, J., Shishkarev, A., Buzytsky, I., & Banerjee, A. G. (2018). Auction Bidding Methods for Multiagent Consensus Optimization in Supply–Demand Networks. IEEE Robotics and Automation Letters, 3(4), 4415-4422.
- Liu, J., Hwang, S., Yund, W., Boyle, L. N., & Banerjee, A. G. (2018, August). Predicting purchase orders delivery times using regression models with dimension reduction. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 51739, p. V01BT02A034). American Society of Mechanical Engineers.