Marcus A. Brubaker
Postdoctoral Fellow at University of Toronto, Scarborough
Following on Ali's presentation at the NIPS Machine Learning in Computational Biology Workshop we have published a technical report on arXiv: "Microscopic Advances with Large-Scale Learning: Stochastic Optimization for Cryo-EM".
We've also just published an arXiv paper about correcting magnification anisotropy in Cryo-EM images.
Ali Punjani and I will be presenting some of our recent work on 3D structure estimation in CryoEM at the NIPS Machine Learning in Computational Biology Workshop. If you're going to be at NIPS, come by and check it out.
A new paper with John Rubinstein has been posted to arXiv describing a method of particle movie alignment for CryoEM: Alignment of cryo-EM movies of individual particles by global optimization of image translations.
I will be, once again, teaching at UTSC this fall: CSCC11 - Introduction to Machine Learning
Recent work with Yali Wang and Raquel Urtasun on online filtering with Gaussian Processes is now available and will be presented at UAI 2014 in a few days.
Recent work with Yanshaui Cao, Aaron Hertzmann and David Fleet on sparse Gaussian Processes is now available on arXiv: Efficient Optimization for Sparse Gaussian Process Regression. This will be presented at NIPS 2013.
Stan 2.0 has just been released. Lots of big changes in this version so go check it out if you're interested in Bayesian statistical estimation and MCMC.
I will be speaking to the Toronto chapter of the IEEE Computer Society on September 26th on my recent localization work.
Recent work with Yanshaui Cao, Aaron Hertzmann and David Fleet on Gaussian Process sparsification has been accepted for publication at NIPS 2013. This paper will be available soon.
Also, this term I will be teaching CSCC11: Introduction to Machine Learning and Data Mining at the University of Toronto, Scarborough.
Source code for Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization is now available on the project website!
My recent project on vehicle localization using visual odoemtry has been written up in New Scientist.
I have also made available a PDF version of the slides used for my CVPR 2013 presentation. They can be accessed from the project website.
Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization was selected as the Best Paper Runner-Up at CVPR 2013! See the project website for the video, paper and poster, which was just recently uploaded.
My most recent project, "Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization" with Andreas Geiger and Raquel Urtasun, has been accepted for an oral presentation at CVPR 2013. See the project website for the video and paper.
My paper with Jianhua Zhao and John L. Rubinstein is now available: TMaCS: A hybrid template matching and classification system for partially-automated particle selection. Source can be found here.
A paper with Jianhua Zhao and John L. Rubinstein, "TMaCS: A hybrid template matching and classification system for partially-automated particle selection" has been accepted for publication in the Journal of Structural Biology. It's not available yet, but look for it soon.
This fall I will be teaching CSCD11: Machine Learning and Data Mining at the University of Toronto, Scarborough.
Better late than never, some of the video sequences used in my papers are now available from here. Videos of results from a number of my papers have also been uploaded to YouTube.
I've begun organizing a Computer Vision Reading Group at U of T. The first meeting will be May 15th.
Our paper (with Mathieu Salzmann and Raquel Urtasun) "A Family of MCMC Methods on Implicitly Defined Manifolds" will be presented at AISTATS 2012. Matlab code is available here.
I have finished my PhD thesis "Physical Models of Human Motion for Estimation and Scene Analysis" and have started a postdoc with Raquel Urtasun (Toyota Technological Institute at Chicago) and David Fleet (University of Toronto). I am now also consulting as Research Associate with Cadre Research Labs.
Our paper "A Bayesian Method for 3-D Macromolecular Structure Inference using Class Average Images from Single Particle Electron Microscopy" has been accepted into the journal Bioinformatics. A preprint of the paper is available here and the project website can be found here.
Citation information has been updated for the IJCV article to include the volume, issue and starting page numbers.
Three new papers have been made available: "Estimating Contact Dynamics" (ICCV 2009), "Physics-based Person Tracking Using the Anthropomorphic Walker" (IJCV 2010), and "Video-based People Tracking" (In Handbook of Ambient Intelligence and Smart Environments).
Leonid Sigal, David Fleet and I will be running a tutorial for ICCV 2009: "Physics-Based Human Motion Modelling for People Tracking".
I am currently a Postdoctoral Fellow at the University of Toronto Scarborough. I also work as a Research Associate with Cadre Research Labs. Previously I worked with Raquel Urtasun (Toyota Technological Institute at Chicago). I finished my Ph.D. in September of 2011 supervised by David Fleet at the University of Toronto in the Computer Vision group. I received my M.Sc. and Honors B.Sc. from the University of Toronto in November 2006 and June 2004 respectively.
Feel free to contact me at mbru...@cs.toronto.edu.
Generally, I have a strong interest in machine learning
and probabilistic methods, particularly when applied to
computer vision related problems.
Recently I looked at the use of map data in computer vision applications such as localization through the use of visual odometry.
My PhD research looked at the use of physics for tracking human motion. I explored abstract models of bipedal walking in the context of monocular tracking with particle filters and the dynamics of complex articulated models of the human body in motion estimation and dynamic scene analysis.
I have also been interested in the problems surrounding Electron Cryo-Microscopy, an imaging technique used to estimate the structure of small molecules such as proteins and viruses. With colleagues I looked at the use of Bayesian methods for single particle reconstruction and have recently helped investigate using modern Machine Learning techniques in semi-supervised particle picking.
Hobbies and Other Interests