Lei Lin

Lei Lin’s research interests include Transportation Big Data, Connected and Automated Vehicle and Machine Learning. Prior to joining the University of Rochester, Lei worked as a research associate at Center for Connected and Automated Transportation (CCAT) at Purdue University, where he led a project about Human-driven Vehicle Trajectory Prediction for Connected and Automated Vehicle Platoon Control. Before that, Lei was a researcher at Conduent/PARC/Xerox from 2015 to 2017, where he worked on connected vehicle and transportation energy optimization projects.

Lei Lin holds a Ph.D. degree in Civil Engineering and an M.S. degree in Computer Science, both from University at Buffalo, the State University of New York. He has published more than 35 peer-reviewed journal and conference papers.

Publications

• Lei Lin, Beilei Xu, Wencheng Wu, Trevor W. Richardson, and Edgar A. Bernal.  “Medical Time Series Classification with Hierarchical Attention-based Temporal Convolutional Networks: A Case Study of Myotonic Dystrophy Diagnosis.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 83-86. 2019.
• Li, Tao, and Lei Lin. “AnonymousNet: Natural face de-identification with measurable privacy.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 0-0. 2019.
• Ren, Shuyun, Fengji Luo, Lin Lei, Shu-Chien Hsu, and Xuran Ivan Li. “A novel dynamic pricing scheme for a large-scale electric vehicle sharing network considering vehicle relocation and vehicle-grid-integration.” International Journal of Production Economics(2019).
• Lishan Liu, Ning Jia, Lei Lin, and Zhengbing He. “A cohesion-based heuristic feature selection for short-term traffic forecasting.” IEEE Access 7 (2019): 2169-3536.
• Lei Lin, Zhengbing He, and Srinivas Peeta. “Predicting Station-level Hourly Demand in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach.” Transportation Research Part C: Emerging Techniques 97 (2018): 258-276.
• Lei Lin, John Handley, Yiming Gu, Lei Zhu, Xuejin Wen, and Adel W. Sadek. “Quantifying Uncertainty in Short-term Traffic Prediction and its Application to Optimal Staffing Plan Development.” Transportation Research Part C: Emerging Technologies 92 (2018): 323-348.
• Lei Zhu, Jeffrey Gonder, and Lei Lin. “Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data.” Journal of Advanced Transportation, 2017.

Conference Proceedings and Presentations

• Lin. L.  Vehicle Trajectory Prediction Using LSTM with Hierarchical Attention Mechanism, 2019 TransInfo Symposium, The State University of New York at Buffalo, Buffalo, NY, August 2019.
• L. Lin, B. Xu, W. Wu, T. Richardson, E. Bernal. Interpretable Diagnosis of Myotonic Dystrophy from Handgrip Time Series Data with Attention-based Temporal Convolutional Network, 2019 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 2019.
• W. Johnson, L. Lin, R. Raqueño, T. Richardson, W. Wu, Corning Presentation, Corning, NY April 2019.
• E. Bernal, L. Lin, T. Richardson, Careers in Data Science, Georgan Institute for Data Science, University of Rochester, Rochester, NY, February 2019.
• Tao Li, Lei Lin, and Siyuan Gong Multi-Party Computation for Secure and Privacy- Preserving Cooperative Control of Connected Autonomous Vehicles.” The AAAI’s Workshop on Artificial Intelligence Safety, January 27-February 1, 2019 Honolulu, Hawaii, USA.
• Lei Lin, Srinivas Peeta, and Jian Wang. Collection of Connected Vehicle Data based on Compressive Sensing.” The 21st IEEE International Conference on Intelligent Transportation Systems, November 4-7, 2018, Hawaii, USA
• Lei Lin, Siyuan Gong, Tao Li, and Srinivas Peeta. Deep Learning-based Human-Driven Vehicle Trajectory Prediction and its Application for Platoon Control of Connected and Autonomous Vehicles”. The Automated Vehicle Symposium, July 9-12, 2018, San Francisco, USA.
• Lin Liu, Chunyuan Li, Yongfu Li, Srinivas Peeta, and Lei Lin. Car-following Behavior of Connected Vehicles in a Mixed Traffic Flow: Modeling and Stability Analysis.” The 8th Annual IEEE Int. Conf. on CYBER Technology in Automation, Control, and Intelligent Systems. July 19-23, 2018, Tianjin, China.
• Lei Lin, and Srinivas Peeta. Real-Time Compression of Connected Vehicle Data based on Compressive Sensing.” The 2018 Global Symposium for Connected and Automated Vehicles and Infrastructure, March 7-8, 2018, Ann Arbor, Michigan, USA.
• Liu Lin, Srinivas Peeta, Siyuan Gong, Lei Lin, and Jian Wang. Car Following Behavior of Connected Vehicles in a Mixed Traffic Flow: Modeling and Stability Analysis”. The 2018 Global Symposium for Connected and Automated Vehicles and Infrastructure, March 7-8, 2018, Ann Arbor, Michigan, USA.
• Zhenhua Zhang, Lei Lin, Lei Zhu, and Anuj Sharma. Bi-National Delay Pattern Analysis For Commercial and Passenger Vehicles at Niagara Frontier Border”. The 97th Annual Transportation Research Board Meeting, January 7-11, 2018, Washington, D.C., USA.
• Lei Lin, John Handley and Adel W. Sadek. Interval Prediction of Short-term Traffic Volume based on Extreme Learning Machine and Particle Swarm Optimization”. The 96th Annual Transportation Research Board Meeting, January 8-12, 2017, Washington, D.C., USA.