November 12, 2015 at 1:00 p.m. Marcus Brubaker, Ph.D., will be presenting “Efficient 3D Molecular Structure Estimation with Electron Cryomicroscopy”.
Speaker: Marcus Brubaker, Ph.D.
Postdoctoral at University of Toronto
Day & Time: Thursday, November 12, 2015
1:00 p.m. – 2:00 p.m.
Location: Room ENG106, Ryerson University
350 Victoria Street, Toronto, Ontario M5B 2K3
Click here to see the Map – Look for ENG
Organizer: Instrumentation & Measurement and Magnetics Chapters at IEEE Toronto
Contact: Dr. Maryam Davoudpour: email@example.com
Abstract: Discovering the 3D structure of molecules such as proteins and viruses is a fundamental research problem in biology and medicine. Electron Cryomicroscopy (Cryo-EM) is a promising vision-based technique for structure estimation which attempts to reconstruct 3D structures from 2D images. This talk reviews the computational problems in Cryo-EM which are closely related to classical vision problems such as object detection, multiview reconstruction and computed tomography. Finally, a framework is introduced for reconstruction of 3D molecular structure which exploits modern methods for stochastic optimization and importance sampling. The result is a method which is efficient, robust to initialization and flexible.
Biography: Marcus Brubaker received his Ph.D. in Computer Science from the University of Toronto in 2011. After that he worked with Raquel Urtasun as a postdoctoral researcher at Toyota Technological Institute at Chicago and is currently a postdoc at University of Toronto, Scarborough. He also consults with Cadre Research Labs on machine learning and computer vision related projects and teaches at the University of Toronto. He was won a number of fellowships and awards, including OGS and NSERC graduate fellowships as well as an NSERC Postdoctoral Fellowship.
His most recent work on autonomous vehicle localization (“Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization,” CVPR 2013) and the estimation of the 3D structure of proteins and viruses (“Building Proteins in a Day,” CVPR 2015) have won awards and attention in the lay press. His interests span computer vision, machine learning and statistics and he works on a range of problems including video-based human motion estimation, physical models of human motion, Bayesian inference, Markov Chain Monte Carlo methods, ballistic forensics, electron cryo-microscopy and autonomous vehicle localization.