Renjie Liao is an Assistant Professor at the Department of Electrical and Computer Engineering and an Associated Member of the Department of Computer Science at the University of British Columbia (UBC). He is a faculty member at the Vector Institute and a Canada CIFAR AI Chair. Before joining UBC, he was a Visiting Faculty Researcher at Google Brain, working with Geoffrey Hinton and David Fleet. He received his Ph.D. in CS in 2021 from the University of Toronto under the supervision of Richard Zemel and Raquel Urtasun. During his Ph.D., he worked as a Senior Research Scientist at Uber Advanced Technologies Group. He received an M.Phil. degree in CS in 2015 from the Chinese University of Hong Kong and a B.Eng. degree in Automation in 2011 from Beihang University. He is broadly interested in machine learning and its intersection with computer vision, self-driving, healthcare, and other areas, with a focus on probabilistic and geometric deep learning.
Assistant Professor, Department of Electrical and Computer Engineering, University of British Columbia
Canada CIFAR Artificial Intelligence Chair
Research Interests
- Deep Generative Models
- Geometric Deep Learning
- Probabilistic Inference
- Computer Vision
- Self-driving
- AI for Healthcare
Highlights
- Best paper award, International Workshop on Machine Learning in Medical Imaging, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023
- Canada CIFAR AI Chair in 2023
- Top Reviewer, ICML (2020)
- Best Reviewer, NeurIPS (2019)
- Best Paper Award, ICML Workshop on Tractable Probabilistic Modeling (2019)
- RBC Graduate Fellowship, RBC (2019-2021)
- Connaught International Scholarship for Doctoral Students, University of Toronto (2015-2019)
Publications
Systems and Methods for Latent Distribution Modeling for Scene-Consistent Motion Forecasting
2021
Systems and Methods for Jointly Performing Perception, Perception, and Motion Planning for an Autonomous System
2021
Systems and Methods for Autonomous Vehicle Systems Simulation
2021
Deep learning analysis of endometrial histology as a promising tool to predict the chance of pregnancy after frozen embryo transfers
2023
EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms
2023
Gemtrans: A general, echocardiography-based, multi-level transformer framework for cardiovascular diagnosis
2023