Micha is a PhD candidate towards the end of his PhD, in the Computational Vision Group / Artificial Intelligence Lab, part of the Department of Computer Science at University of Toronto, and in the Vector Institute, Toronto. He is supervised by prof. David Fleet, and has been concentrating for the better part of the last decade on machine learning and optimization problems. Specifically on 3D video tracking combined with physical models. Micha holds a BSc. in Electrical Engineering and a BSc. in Physics from the Technion, Israel Institute of Technology, both with Summa Cum Laude honors (top 3% of his class). He obtained his MSc. from University of Toronto under David's supervision, where he researched inferring attributes, such as gender, weight, happiness and anxiety level, from motion capture data and from video tracking.

Micha also founded a Computer Vision/Machine Learning lab, with the goal of pushing ahead CV/ML research, while giving smaller start-ups (who typically cannot afford to hire ML researchers) accessibility to one of the biggest revolutions that mankind is about to experience. He also believes that the social aspect of that revolution is ignored, by large. As part of his efforts for a better future for all, he is working to encourage an open discussion about the social implications of the AI revolution, in order reduce fears from AI, and replace it with openness and better understanding of what possibilities lies ahead.

In his spare time he enjoys hiking, canoeing, SUP, bike riding, skiing, ice skating, or basically every outdoor/indoor sport activity he finds the time to put into.

You can find Micha's CV here.

## Contact Information

E-mail

livne [at] seraphlabs [dot] ca


Mail

Dept. of Computer Science
University of Toronto
10 King's College Road, Rm. 3303
Toronto, ON
M5S 3G4


Office

Pratt Building, Rm. 263A
6 King's College Road
Toronto, ON
M5S 3G8


Or

Vector Institute
MaRS Centre, West Tower
661 University Ave., Suite 710
Toronto, ON
M5G 1M1


# Publications

• “High Mutual Information in Representation Learning with Symmetric Variational Inference”
• M. Livne, K. Swersky, and D. J. Fleet
• NIPS 2019, Bayesian Deep Learning Workshop, Poster and Spotlight Talk (~ top 10% of accepted papers), 2019
• pdf bibtex
• “TzK Flow - Conditional Generative Model”
• M. Livne, and D. J. Fleet
• NIPS 2018, Bayesian Deep Learning Workshop, 2018
• pdf bibtex
• “Walking on thin air: Environment-free physics-based markerless motion capture”
• M. Livne, L. Sigal, M. A. Brubaker, and D. J. Fleet
• Computer and Robot Vision, no. 15th, 2018.
• project page pdf bibtex
• “Spinal cord segmentation by one dimensional normalized template matching: A novel, quantitative technique to analyze advanced magnetic resonance imaging data”
• A. Cadotte, D. W. Cadotte, M. Livne, J. Cohen-Adad, D. Fleet, D. Mikulis, and M. G. Fehlings
• PLoS ONE, vol. 10, no. 10, p. e0139323, 10 2015.
• pdf bibtex
• “Characterizing the location of spinal and vertebral levels in the human cervical spinal cord”
• D. Cadotte, A. Cadotte, J. Cohen-Adad, D. Fleet, M. Livne, J. Wilson, D. Mikulis, N. Nugaeva, and M. Fehlings
• American Journal of Neuroradiology, 2014
• pdf bibtex
• “Human attributes from 3d pose tracking”
• M. Livne, L. Sigal, N. F. Troje, and D. J. Fleet
• Computer Vision and Image Understanding (CVIU), vol. 116, pp. 648–660, 2012
• pdf bibtex
• “Human attributes from 3d pose tracking”
• L. Sigal, D. J. Fleet, N. F. Troje, and M. Livne
• ECCV 2010 European Conference on Computer Vision. European Conference on Computer Vision (ECCV), Heraklion, Greece, 2010
• bibtex

# Open Source Contributions

Micha created an open source research-support framework to help small companies, independent researchers, and students to concentrate on their research work. The frameworks are meant to supply powerful utilities, classes, and tools to be used with many existing research frameworks. It is a work in progress, and all feedback/requests are welcomed. If you would like to be involved please contact Micha via email.

NOTE: repos are temporarily down for maintenance

• Settings - the base for all other libraries. Contains many useful scripts, and helps in deploying multiple research environments on local/remote machines.
• Common - The heart of the framework. It contains many powerful classes and utilities that Micha developed over the course of my PhD.
• Deployment - A collection of helper scripts to install external research frames on Linux and OS X.

# Research Highlights

Here you can find a collection of videos that highlights some of Micha's research projects.

MIM

VAE

# Fun

Random projects that are guaranteed to possibly put a smile on your face!

• Mike and the City - The exciting tale of Mike and the City, based on (mostly) true events.