Greg Mori

Research Director
Borealis AI Vancouver

School of Computing Science
Simon Fraser University
8888 University Drive
Burnaby, BC

Faculty office: TASC1 8007
Phone: (778) 782-7111
Fax: (778) 782-3045

Ph.D. in Computer Science, University of California at Berkeley, 2004.
Hon. B.Sc. in Computer Science and Mathematics, University of Toronto, 1999.
Greg Mori


[Publication list], [Students and Postdocs], [Teaching], [Curriculum Vitae]


Dr. Greg Mori was born in Vancouver and grew up in Richmond, BC. He received the Ph.D. degree in Computer Science from the University of California, Berkeley in 2004. He received an Hon. B.Sc. in Computer Science and Mathematics with High Distinction from the University of Toronto in 1999. He spent one year (1997-1998) as an intern at Advanced Telecommunications Research (ATR) in Kyoto, Japan. After graduating from Berkeley, he returned home to Vancouver and is currently a Professor in the School of Computing Science at Simon Fraser University.

He was a Visiting Scientist at Google in Mountain View, California in 2014-2015. He served as Director of the School of Computing Science from 2015-2018. He is now Research Director for RBC's Borealis AI Vancouver lab.

Dr. Mori conducts research in computer vision and machine learning, and teaches classes in data structures and programming, artificial intelligence, computer vision, and machine learning. He serves on the editorial boards of IJCV and T-PAMI, the top journals in computer vision, and on the organizing committees for CVPR, ICCV, and ECCV, the top conferences in computer vision. He is privileged to have worked with many excellent students while at SFU.


My research is in computer vision, and is concerned with developing algorithms that automatically interpret images and videos, particularly those containing people. I have made significant contributions towards solving the problems of human pose estimation and human action recognition. At a broad level, the methodology followed is to construct features and representations that capture our intuition regarding these vision problems. We operationalize these via machine learning algorithms, adapting them to suit our purposes.

Specific examples of features and representations include work on superpixels for representing images, motion features for human action recognition, and our structured models for video sequences and group activities. We have developed variants of machine learning algorithms such as hidden Conditional Random Fields (hCRF), Latent Dirichlet Allocation (LDA), latent SVMs, and deep networks to implement these ideas.

Research interests keywords:

  • computer vision
  • machine learning
  • video analysis
  • human activity recognition
  • human body pose estimation
  • pedestrian detection and tracking
  • object recognition
Please see the Vision and Media Lab for a list of research projects, or my list of publications for more details.


Jiawei He (PhD) had a paper accepted to the European Conference on Computer Vision (ECCV), 2018. A temporal variational auto-encoder for synthesizing controllable sequences of human motion is presented.
Changan Chen (BSc) and Fred Tung (PDF) had a paper accepted to the European Conference on Computer Vision (ECCV), 2018. A deep network compression algorithm for meeting operational performance constraints is presented.
Ruizhi Deng (MSc) and Zhiwei Deng (PhD) had a paper accepted to the European Conference on Computer Vision (ECCV), 2018. A sparse aggregation approach for deep neural networks is proposed and analyzed.
Fabien Baradel (visiting PhD) had a paper accepted to the European Conference on Computer Vision (ECCV), 2018. An object reasoning approach to video understanding is presented.
Moustafa S. Ibrahim (PhD) had a paper accepted to the European Conference on Computer Vision (ECCV), 2018. A relational neural network layer for supervised and unsupervised learning of human-contextual feature learning for group activity recognition is presented.
Mengyao Zhai (PhD), Ruizhi Deng (MSc), Jiacheng Chen (BSc), Lei Chen (PhD) and Zhiwei Deng (PhD) had a paper accepted to the British Machine Vision Conference (BMVC), 2018. An adaptive rendering approach for generating images of human action is developed.
Fred Tung (PDF) had a paper accepted to IEEE Computer Vision and Pattern Recognition (CVPR), 2018. Deep network weight pruning and quantization are learned in parallel with training.
Jiawei He (PhD), Zhiwei Deng (PhD), and Moustafa S. Ibrahim (PhD) had a paper accepted to IEEE Winter Conf. on Applications of Computer Vision (WACV), 2018. A class-independent action tublet network for human action localization is presented.
Akash Abdu Jyothi successfully defended his M.Sc. thesis Generating Natural Language Summaries for Image Sets. Congratulations Akash!
Nelson Nauata successfully defended his M.Sc. thesis Structured Label Inference for Visual Understanding. Congratulations Nelson!
Jon Smith successfully defended his M.Sc. thesis REP3D: 3D Human Motion Capture Dataset for Athletic Movement. Congratulations Jon!
[less new news]



Current Former
  • Kevin Cannons, Postdoctoral fellow 2011-2013 (next IKOMED)
  • Wang Yan, Postdoctoral fellow 2013-2017 (next ScopeMedia)
  • Yang Wang, Learning Structured Models for Human Actions and Poses, Ph.D. Summer 2009 (next NSERC postdoc at UIUC, now Asst. Prof. at U Manitoba)
  • Mani Ranjbar, Optimizing Non-Decomposable Loss Functions In Structured Prediction, Ph.D. Summer 2012 (now at D-Wave Systems)
  • Weilong Yang, Discriminative Latent Variable Models For Visual Recognition, Ph.D. Fall 2012 (now at Google Research)
  • Tian Lan, From Flat to Hierarchical: Modeling Structures in Visual Recognition, Ph.D. Summer 2013 (next postdoc at Stanford, now at Amazon)
  • Nataliya Shapovalova, Towards Action Recognition and Localization in Videos with Weakly Supervised Learning, Ph.D. Fall 2014 (now at Amazon)
  • Arash Vahdat, Weakly Supervised Models For Recognizing And Clustering High-Level Complex Events In Video, Ph.D. Fall 2014 (next research faculty at SFU, now at D-Wave Systems)
  • Hossein Hajimirsadeghi, Multiple Instance Learning for Visual Recognition: Learning Latent Probabilistic Models, Ph.D. Fall 2015 (now at Oracle Labs Vancouver).
  • Guang-Tong Zhou, Toward Scene Recognition by Discovering Semantic Structures and Parts, Ph.D. Fall 2015 (now at Oracle Labs Vancouver).
  • Payam Sabzmeydani, Detecting Pedestrians in Still Images Using Learned Shape Features, M.Sc. Fall 2006 (next Koolhaus Games, then AirG)
  • Andy Rova, Eigen-CSS Shape Matching and Recognizing Fish in Underwater Video, M.Sc. Spring 2007
  • Topher Johnson, Responsive Video-Based Motion Synthesis Using Motion Graphs, M.Sc. Summer 2007 (next Blast Radius)
  • Maryam Moslemi, Clustering and Visualizing Actions of Humans and Animals Using Motion Features, M.Sc. Fall 2007 (next UIUC M.Sc. student, now at
  • Alireza Fathi, Efficient Human Figure Tracking Using Motion Exemplars, M.Sc. Summer 2008 (next Georgia Tech Ph.D. student)
  • William Ma, Motion Estimation For Functional Medical Imaging Studies Using A Stereo Video Head Pose Tracking System, M.Sc. Summer 2009 (now at Leovation)
  • Mohammad Norouzi, Convolutional Restricted Boltzmann Machines for Feature Learning, M.Sc. Fall 2009 (next U of Toronto Ph.D. student)
  • Weilong Yang, Learning Transferable Distance Functions For Human Action Recognition And Detection, M.Sc. Spring 2010 (next SFU Ph.D. student)
  • Bahman Yari Saeed Khanloo, Combining Simple Trackers Using Structural SVMs For Offline Single Object Tracking, M.Sc. Summer 2010 (next CWI Ph.D. student)
  • Tian Lan, Beyond Actions: Discriminative Models For Contextual Group Activities, M.Sc. Summer 2010 (next SFU Ph.D. student)
  • Ferdinand Stefanus, Automatic Pedestrian Detection and Tracking with a Multiple-Cue Max-Margin Framework, M.Sc. Fall 2010 (next MacDonald, Dettwiler and Associates Ltd. (MDA))
  • Arash Vahdat, A Key Pose Model For Human Interaction Recognition And Color From Gray By Optimized Color Ordering, M.Sc. Spring 2011 (next SFU Ph.D. student)
  • Bo Gao, Exemplar-Based Human Interaction Recognition: Features And Key Pose Sequence Model, M.Sc. Summer 2011 (next Software Engineer at Trusterra Inc., then Fortinet, then Microsoft)
  • Pengfei Yu, Image Classification Using Latent Spatial Pyramid Matching, M.Sc. Summer 2011 (next Microsoft, then Facebook)
  • Brian Milligan, Selecting And Commanding Groups Of Robots Using A Vision-Based Natural User Interface, M.Sc. Summer 2012 (next Big Park / Microsoft)
  • Zhi Feng Huang, Latent Boosting For Action Recognition, M.Sc. Summer 2012 (next Apple)
  • Amir Bakhtiari, Detecting Pedestrians Using Motion Patterns: A Latent Tracking Approach, M.Sc. Fall 2013 (next Vidigami)
  • Yasaman Sefidgar, Discriminative Key-Segment Model for Interaction Detection, M.Sc. Spring 2014 (next The Jonah Group)
  • Jinling Li, Road User Detection and Analysis in Traffic Surveillance Videos, M.Sc. Summer 2014 (next Netra)
  • Mehran Khodabandeh, Discovering Human Interactions in Videos with Limited Data Labeling, M.Sc. Spring 2015 (next SFU PhD student)
  • Mengyao Zhai, Object Detection in Surveillance Video from Dense Trajectories, M.Sc. Fall 2015 (next SFU PhD student)
  • Zhiwei Deng, Deep Structured Models for Group Activity Recognition, M.Sc. Fall 2015 (next SFU PhD student)
  • Lei Chen, Learning Action Primitives for Multi-Level Video Event Understanding, M.Sc. Fall 2015 (next SFU PhD student)
  • Srikanth Muralidharan, A Hierarchical Deep Temporal Model for Group Activity Recognition, M.Sc. Spring 2016 (next SFU PhD student)
  • Yatao Zhong, Learning Person Trajectory Features for Sports Video Analysis, M.Sc. Spring 2017 (next Oracle Labs Vancouver)
  • Karoon Rashedi Nia, Automatic Building Damage Assessment Using Deep Learning and Ground-Level Image Data, M.Sc. Spring 2017 (next Oracle Labs Vancouver)
  • Nazanin Mehrsasa, Learning Person Trajectory Representations for Team Activity Analysis, M.Sc. Spring 2017 (next SFU PhD student)
  • Xiaoyu Liu, Joint Constrained Clustering and Feature Learning based on Deep Neural Networks, M.Sc. Summer 2017 (next Altumview Systems)
  • Jon Smith, REP3D: 3D Human Motion Capture Dataset for Athletic Movement, M.Sc. Fall 2017 (next CBC)
  • Nelson Nauata, Structured Label Inference for Visual Understanding, M.Sc. Spring 2018 (next SFU PhD student)
  • Jen Fernquist, NSERC Undergraduate Student Research Award (USRA), Summer 2006 (next MDA, then UBC M.Sc. student, then Google)
  • Chris Lundgren, NSERC Undergraduate Student Research Award (USRA), Summer 2006 (next Safe Software)
  • Angelica Lim, NSERC Undergraduate Student Research Award (USRA), Spring 2008 (next Google, then Monbukagakusho Ph.D. at Kyoto U.)
  • Bo Chen, CMPT 415 Directed Studies, RA, Spring - Summer 2007, Summer 2008 (next UBC M.Sc. student, then Caltech Ph.D. student)
  • Mark Bayazit, NSERC Undergraduate Student Research Award (USRA), RA, Summer - Fall 2008 (next at Enterprises Inc.)
  • Aditya Ramesh, NSERC Undergraduate Student Research Award (USRA), Summer 2009 (next Stanford M.Sc. student)
  • Ben Reilly, RA, Spring 2011 (next Toronto M.Sc.)
  • Jia Sun, RA, Spring 2011
  • Wesley May, NSERC Undergraduate Student Research Award (USRA), Summer 2011 (next Toronto M.Sc.)
  • Youyou Yang, RA, Fall 2011 - Spring 2012
  • Pouria Mahmoudi Saghalati, NSERC Undergraduate Student Research Award (USRA), Summer 2012
  • Yuke Zhu, RA, Summer - Fall 2012 (next Stanford Ph.D. student)
  • Jeff Hsu, NSERC Undergraduate Student Research Award (USRA), Summer 2014
  • Yuhao Liu, RA, Summer 2014 - Spring 2015 (next UC Berkeley M.Sc. student)
  • Hexiang Hu, RA, Summer 2015 - Spring 2016 (next UCLA Ph.D. student)
  • Jordan Yap, RA, USRA, Summer 2013 - Fall 2016 (next MetaOptima)
  • Bicheng Xu, VPR USRA, RA, Summer 2016 - Summer 2017 (next UBC MSc student)
  • Changan Chen, RA, Summer 2017 - Spring 2018

Are you a prospective student? Please read this page for prospective students.




Vision and Media Lab

Scientific Data Aquisition, Transportation and Storage Research Lab (SDATS)

Technology for Injury Prevention in Seniors (TIPS)