About Micha

Micha is a machine learning researcher (a post-doc fellow in University of Toronto, and Vector Institute), focusing on latent variable models, representation learning, Bayesian inference, and semi-supervised/unsupervised learning. He recently graduated his PhD under the supervision of prof. David Fleet 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. Micha has been working on machine learning, computer vision, physical inference, and optimization problems for the last decade.

In the past, he focused mainly on 3D video tracking combined with physical models. Currently he has been focusing on representation learning, generative models, Bayesian modelling (always go Bayesian!), and in particular the Mutual Information Machine (MIM). MIM is trained with a new symmetric variational inference, which learns a latent representation with high mutual information, and low marginal entropy. Importantly, MIM is not prone to suffer from posterior collapse, unlike VAEs. If you use VAE, you might find MIM useful!

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. During his PhD Micha worked with MERL (Mitsubishi Electric Research Labs), Disney Research Pittsburgh (i.e., which has been closed since then), and Adobe CTL (Creative Technologies Labs) Seattle, and Creative Destruction Labs (Toronto), among others.

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 humanity is experiencing. He also believes that the social/moral aspects of that revolution are 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.

Micha is always looking for the next opportunity! If you find his research interests aligned with yours, please do not hesitate to reach out to him.

News and Updates

Contact Information


livne [at] seraphlabs [dot] ca



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


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


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



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

Research Highlights

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

MIM: Mutual Information Machine



PNN - Physical Neural Network

Performance Capture

Spine Segmentation

Physical Tracking


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