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, computer vision, and optimization problems.
In the past, I focused mainly on 3D video tracking combined with physical models. Recently I have 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! I am always looking for interesting collaborations.
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.
I am about to graduate and looking for my next opportunity! If you find my research interests aligned with yours, please do not hesitate to reach out to me.
News and Updates
- SentenceMIM preprint (see below) is available - we break 5 SOTA records when training a probabilistic autoencoder with MIM learning!
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
- “High Mutual Information in Representation Learning with Symmetric Variational Inference”
- “TzK Flow - Conditional Generative Model”
- “Walking on thin air: Environment-free physics-based markerless motion capture”
- “Spinal cord segmentation by one dimensional normalized template matching: A novel, quantitative technique to analyze advanced magnetic resonance imaging data”
- “Characterizing the location of spinal and vertebral levels in the human cervical spinal cord”
- “Human attributes from 3d pose tracking”
- “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
- “SentenceMIM: A Latent Variable Language Model”
- “MIM: Mutual Information Machine”
- “TzK: Flow-Based Conditional Generative Model”
- M. Livne, and D. J. Fleet
- Preprint, 2019
- TzK: Flow-Based Conditional Generative Model
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.
Here you can find a collection of videos that highlights some of Micha's research projects.
MIM: Mutual Information Machine
PNN - Physical Neural Network
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.