Min Bai

• I am currently an applied scientist at Amazon AWS.
• I was a PhD student at the University of Toronto in the Machine Learning and Computer Vision group since January 2017, supervised by Prof. Raquel Urtasun. Additionally, I was a research scientist at the Uber Advanced Technologies Group in Toronto, also led by Prof. Raquel Urtasun, from May 2017 to February 2021.
• Before this, I obtained my MSc. in Computer Science in the same group.
• My research interests include computer vision and various related machine learning techniques.
• I completed an undergraduate degree in 2013 at the University of Waterloo in Electrical Engineering with a focus on high frequency circuits, electromagnetism, and communication systems.
• My CV is available here.

Research

• Implicit Surface Contrastive Clustering for LiDAR Point Clouds
  Zaiwei Zhang, Min Bai, Erran Li
  In Computer Vision and Pattern Recognition (CVPR) 2023
• Improving Self-Supervised Representation Learning via Sequential Adversarial Masking
  Dylan Sam, Min Bai, Tristan J. McKinney, Li Erran Li
  In NeurIPS 2022 Workshop: Self-Supervised Learning - Theory and Practice, 2022
  
• Self-Supervised Pretraining for Large-Scale Point Clouds
  Zaiwei Zhang, Min Bai, Erran Li
  In Neural Information Processing Systems (NeurIPS) 2022
  
• Auto4D: Learning to Label 4D Objects from Sequential Point Clouds
  Bin Yang, Min Bai, Ming Liang, Wenyuan Zeng, Raquel Urtasun
  Submitted to International Conference on Intelligent Robotics and Systems (IROS) 2021
• Non-parametric Memory for Spatio-Temporal Segmentation of Construction Zones for Self-Driving
  Min Bai, Shenlong Wang, Kelvin Wong, Ersin Yumer, Raquel Urtasun
  In Arxiv preprint, arXiv:2101.06865, Jan 2021
• Exploiting Sparse Semantic HD Maps for Affordable Localization
  Wei-Chiu Ma*, Ignacio Tartavull*, Ioan Andrei Barsan*, Shenlong Wang*, Min Bai, Gellert Mattyus, Namdar Homayounfar, Shrinidhi Kowshika Lakshmikanth, Raquel Urtasun
  In International Conference on Intelligent Robotics and Systems (IROS) 2019
• UPSNet: A Unified Panoptic Segmentation Network
  Yuwen Xiong*, Renjie Liao*, Hengshuang Zhao*, Rui Hu, Min Bai, Ersin Yumer, Raquel Urtasun
  In Computer Vision and Pattern Recognition (CVPR) 2019
• Deep Multi-Sensor Lane Detection
  Min Bai*, Gellert Mattyus*, Namdar Homayounfar, Shenlong Wang, Shrinidhi Kowshika Lakshmikanth, Raquel Urtasun
  In International Conference on Intelligent Robotics and Systems (IROS) 2018
• Learning Deep Structured Active Contours End-to-End
  Diego Marcos, Devis Tuia, Benjamin Kellenberger, Lisa Zhang, Min Bai, Renjie Liao, Raquel Urtasun
  In Computer Vision and Pattern Recognition (CVPR) 2018
• TorontoCity: Seeing the World with a Million Eyes
  Shenlong Wang, Min Bai*, Gellert Mattyus*, Hang Chu*, Wenjie Luo, Bin Yang, Justin Liang, Joel Cheverie, Sanja Fidler, Raquel Urtasun.
  In International Conference on Computer Vision (ICCV) 2017 (spotlight)
• Deep Watershed Transform for Instance Segmentation
  Min Bai, Raquel Urtasun.
  In Computer Vision and Pattern Recognition (CVPR) 2017
• Exploiting Semantic Information and Deep Matching for Optical Flow
  Min Bai*, Wenjie Luo*, Kaustav Kundu, and Raquel Urtasun. (* denotes equal contribution)
  In European Conference on Computer Vision (ECCV) 2016

TA

• CSC148 Introduction to Computer Science, Summer 2016
• CSC411 Introduction to Machine Learning, Fall 2016
• ECE521 Inference Algorithms and Machine Learning, Winter 2017

Prior work experience

• From 2013-2015, I was a Wireless Systems Engineer at Apple Inc in Cupertino, California.
• During my undergrad, I completed various internships (mostly in electronics and electrical systems related fields) at NVIDIA Corporation, BlackBerry Limited, Independent Electricity Systems Operator of Ontario, and Genesys Telecommunications.

Email

mbai (at) cs (dot) toronto (dot) edu