Marcus A. Brubaker's Webpage

Marcus A. Brubaker


mbrubake [at] cs [dot] toronto [dot] edu
Postdoctoral Researcher with Raquel Urtasun
Toyota Technological Institute at Chicago

Contents


News
April 2013
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; code will be made available shortly.

February 2013
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.

December 2012
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.

August 2012
This fall I will be teaching CSCD11: Machine Learning and Data Mining at the University of Toronto, Scarborough.

July 2012
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.

April 2012
I've begun organizing a Computer Vision Reading Group at U of T.  The first meeting will be May 15th.

February 2012
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.

October 2011
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.

August 2010
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.

January 2010
Citation information has been updated for the IJCV article to include the volume, issue and starting page numbers.

August 2009
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".

Projects

Map Localization through Visual Odometry

Self-localization is key for building autonomous systems such as self-driving cars. This project explores a novel localization system which relies only on visual input and freely available, community developed maps from the OpenStreetMap project. Based on these inputs, our system is able to quickly and efficiently determine the location of a vehicle to an accuracy of ~3m after only a few seconds of driving.

Code implementing the proposed method will be made available for non-commercial use.

Markov Chain Monte Carlo on Constrained Spaces

Traditional MCMC methods are only applicable to distributions defined on $\mathbb{R}^n$. However, there exist many application domains where the distributions cannot easily be defined on a Euclidean space.  To address this limitation, we propose a general constrained version of Hamiltonian Monte Carlo, and give conditions under which the Markov chain is convergent.

Code implementing the proposed methods is available.

Stan

Stan is a package for performing Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. It provides a simple language in which to specify statistical models and uses automatic differentiation to efficiently compute the needed gradients to perform HMC.

I have been contributing to Stan since early 2012. My main focus has been working to include and optimize matrix calculations to allow Stan to efficiently work with larger models and bigger datasets.

Estimating Human Interactions using 3D Physics-based Models

Physics-based models of human motion provide both an important cue for estimation but, perhaps more importantly, provide significant information on the interactions of a person with the world.  This work explores this connection by estimating 3D contact geometry and the forces driving a motion using a plausible 3D physical model of human motion.

Code to implement the model described was provided as part of our tutorial at ICCV 2009: "Physics-Based Human Motion Modelling for People Tracking".

Abstract Physics-based Models for Human Pose Tracking

This project explored the use of abstract, physics-based models of human locomation in tracking.  Abstract models are used in robotics and biomechanics to capture key aspects of the dynamics of human locomotion.  Some of these models exhibit humanoid-like walking as passive limit cycles of their motion, exhibiting their stability and applicability to human motion modelling.  This project used these abstractions to build motion models which were then used to constrain human pose tracking.

Code and data is available.

Bayesian Methods for Electron Cryo-Microscopy

Electron Cryo-Microscopy (Cryo-EM) is a technique for recovering the 3D structure of molecules such as proteins and viruses.  This project explored a technique for 3D density estimation based on a Bayesian formulation of the problem.  The method performs ab initio inference of the three-dimensional structures of macromolecules from single particle electron cryo-microscopy experiments using class average images.

Background Information

I am currently a Postdoctoral Researcher with Raquel Urtasun (Toyota Technological Institute at Chicago).  I also work as a Research Associate with Cadre Research Labs.  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.  Though American by birth I am living in Canada by choice, by wife and, more or less, by politics.

Feel free to contact me at the email address above.

Research Interests
My research has looked at the use of physics for tracking human motion.  We began by exploring abstract models of bipedal walking in the context of monocular tracking with particle filters.  More recently we have looked at the dynamics of complex articulated models of the human body in motion estimation and dynamic scene analysis.

I have also investigated the use of Bayesian methods in Electron Cryo-Microscopy with Ryan Lilien, Navdeep Jaitly and John Rubinstein.

Generally, I have a strong interest in machine learning and probabilistic methods, particularly when applied to computer vision related problems.

Hobbies and Other Interests
  • Cooking and food in general
  • Coffee
  • Politics and economics
  • Rock Climbing
  • Golf