I’m an Assistant Professor of Computer Science at York University, a Faculty Affiliate at the Vector Institute and an Adjunct Professor in the University of Toronto Department of Computer Science. Previously I was a Researcher Director (2018-2020) at Borealis AI where I continue to work as an Academic Advisor. I am also a co-founder and advisor of Structura Biotechnology (makers of cryoSPARC), one of the original contributors to Stan and a Research Associate at Cadre Research Labs (makers of TopMatch-GS). I studied at the University of Toronto where I received my PhD in 2011. I also did postdocs at the Toyota Technological Institute at Chicago and the University of Toronto. My research interests span fundamental methods in computer vision, machine learning and statistics. Recently I have been focusing on probabilistic generative models (like normalizing flows) and methods for electron cryomicroscopy (cryo-EM).
I am a member of the Centre for Vision Research and core member of the Vision: Science to Application (VISTA) program. I currently serve as an Associate Editor for IET Computer Vision and have been an Area Chair for a number of conferences.
I am interested in building rich, detailed models which capture fundamental relationships between the world and our observations of it. Such models ultimately enable us to measure and predict sometimes surprising details.
Most recently I have been focusing on probabilistic generative models, specifically normalizing flows. My research has been exploring theoretical aspects of normalizing flows and their applications. I also have an interest on the problem of estimating the 3D structure of biological molecules such as proteins and viruses with Cryo-EM. Beyond those current focuses, I have also worked on vehicle localization for robotics, physically realistic models of human motion, probabilistic programming languages, Bayesian methods, MCMC and forensic ballistics.
Interested in joining my group? I’m also on the look out for exceptional students and colleagues to work with. More information is available here.
The code and paper for our NeurIPS 2020 paper, Wavelet Flow: Fast Training of High Resolution Normalizing Flows, is now available. The code is availble through the Wavelet Flow GitHub Project. The paper can be found on arXiv. All of this, and additional image results, can be found on the Wavelet Flow Project Page. If you have questions, feel free to get in touch or check us out at our poster session at NeurIPS 2020.
Two papers on normalizing flows have been accepted at NeurIPS 2020. Congratulations and thanks to the students, Jason and Ruizhi, and my excellent collaborators!
- Wavelet Flow: Fast Training of High Resolution Normalizing Flows with Jason Yu and Konstantinos Derpanis (arxiv preprint to come)
- Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows with Ruizhi Deng, Bo Chang, Greg Mori and Andreas Lehrmann
I’ve finally launched a new website based on GitHub Pages. This should make it easier for me to maintain and add content. Keep an eye on things over the coming weeks as I work to expand and improve its contents. My old York EECS website is still availble here and my ancient UofT DCS website is still available here, although I am planning to take down my DCS page soon.
Our paper on the “Tails of Lipschitz Triangular Flows” has been accepted for publication at ICML 2020. If you’re attending, be sure to stop by and chat.
I recently co-authored a review article (preprint version) on Normalizing Flows which will be published in IEEE Transactions of Pattern Analysis and Machine Intellgience. Normalizing Flows is a topic that I’m excited about and looking to work more on in the future. If you’ve not seen it before, please check it out.
I have returned full time to academia! At the moment I am working on expanding my lab and restarting my research program. Watch this space for updates in the near future!
For more news see here.