Yang Li

·Ph.D. in Computer Science, Stanford University, 2011 - 2017.

Dissertation: Exploiting Shared Structures in Large GPS Trajectory Datasets under Uncertainty

Advisor: Leonidas J. Guibas

·B.A. Double major in Computer Science and Mathematics, Smith College, 2007 - 2011.

·Associate Professor (December 2022 – Present)

Institute of Data and Information, Tsinghua Shenzhen International Graduate School

·Assistant Professor (October 2019 – Present)

Data Science and Information Technology Research Center, Tsinghua-Berkeley Shenzhen Institute

·Postdoc Researcher (September 2017 – September 2019

IoT and Cyber-Physical System Lab, Tsinghua-Berkeley Shenzhen Institute

Principal Investigator: Lin Zhang, Khalid M. Mosalam

Transfer learning, multi-task and multi-modal representation learning, analysis of geometric and spatial-temporal data under uncertainty. 

1. Yang Tan, Yang Li* and Shao-lun Huang. OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15779-15788. 2021.

2. Yang Jingyun, Jie Hu, Yicong Li, Heng Liu*, Yang Li*. Joint PVL Detection and Manual Ability Classification Using Semi-supervised Multi-task Learning. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2021. Lecture Notes in Computer Science, vol 12907. Springer, 2021  

3. Yajie Bao#; Yang Li#; Shao-Lun Huang*; Lin Zhang; Lizhong Zheng; Amir Zamir; Leonidas Guibas. An Information-Theoretic Metric of Transferability for Task Transfer Learning, 26th IEEE International Conference on Image Processing (ICIP 2019), Taipei, Taiwan, Sept 22-29, 2019   

4. Jing Lian, Yang Li, Weixi Gu, Shao-Lun Huang, and Lin Zhang. Joint mobility pattern mining with urban region partitions. In Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, pp. 362-371. 2018. （Best paper reward）

5. Yang Li, Dimitrios Gunopulos, Cewu Lu, and Leonidas Guibas*. Urban travel time prediction using a small number of GPS floating cars. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1-10. 2017.