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Machine Learning in Medicine Seminar Series: René Vidal (Johns Hopkins University) - Automatic Methods for the Interpretation of Biomedical Data

Monday, May 8, 2017 at 4:15pm

Phillips Hall, 233

Title: Automatic Methods for the Interpretation of Biomedical Data
Speaker: René Vidal, Johns Hopkins University

Access via Zoom:

  • Meeting ID: 389 371 795 
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 All are welcome to attend

Abstract:
In this talk, I will overview our recent work on the development of automatic methods for the interpretation of biomedical data from multiple modalities and scales. At the cellular scale, I will present a structured matrix factorization method for segmenting neurons and finding their spiking patterns in calcium imaging videos, and a shape analysis method for classifying embryonic cardiomyocytes in optical imaging videos. At the organ scale, I will present a Riemannian framework for processing diffusion magnetic resonance images of the brain, and a stochastic tracking method for detecting Purkinje fibers in cardiac MRI. At the patient scale, I will present dynamical system and machine learning methods for recognizing surgical gestures and assessing surgeon skill in medical robotic motion and video data.

Biography:
René Vidal received his B.S. degree in Electrical Engineering (valedictorian) from the Pontificia Universidad Catolica de Chile in 1997 and his M.S. and Ph.D. degrees in Electrical Engineering and Computer Sciences from the University of California at Berkeley in 2000 and 2003, respectively. He is a professor in the Department of Biomedical Engineering at The Johns Hopkins University. He directs the Vision Dynamics and Learning Lab, which is part of the Center for Imaging Science (CIS). He is also a faculty member in the Institute for Computational Medicine (ICM) and the Laboratory for Computational Sensing and Robotics (LCSR). His research areas are biomedical image analysis, computer vision, machine learning, dynamical systems theory and robotics. He is particularly interested in the development of mathematical methods for the interpretation of high-dimensional data, such as images, videos, and biomedical data. In particular, he has developed methods from algebraic geometry, sparse and low-rank representation theory for clustering and classification of high-dimensional data, and methods from dynamical systems theory for modeling and comparison of time series data. Applications include motion segmentation, dynamic texture classification, object and activity recognition in images and videos, surgical skill and gesture recognition in kinematic and video, segmentation and registration of brain images, and classification of cardiac myocytes. He was a research fellow at the National ICT Australia in 2003 and has been a faculty member in the Department of Biomedical Engineering and the Center for Imaging Science of The Johns Hopkins University since 2004. He has held several visiting faculty positions at Stanford, INRIA/ENS Paris, the Catholic University of Chile, Universite Henri Poincare, and the Australian National University. Dr. Vidal is co-author of the book ``Generalized Principal Component Analysis" (2016), co-editor of the book ``Dynamical Vision" and co-author of over 200 articles in machine learning, computer vision, biomedical image analysis, hybrid systems, robotics and signal processing. Dr. Vidal is or has been Associate Editor of Medical Image Analysis, the IEEE Transactions on Pattern Analysis and Machine Intelligence, the SIAM Journal on Imaging Sciences, Computer Vision and Image Understanding, and the Journal of Mathematical Imaging and Vision, and guest editor of the International Journal on Computer Vision and Signal Processing Magazine. He is or has been program chair for ICCV 2015, CVPR 2014, WMVC 2009 and PSIVT 2007. He was area chair for AAAI 2016, NIPS 2015, MICCAI 2013 and 2014, ICCV 2007, 2011, 2013 and 2017, and CVPR 2005, 2013 and 2017. He is a fellow of the IEEE (2014), fellow of the IAPR (2016), and a member of the ACM and SIAM.

 

This month’s lecture is sponsored by the Jerry F Goff ’74 Bio-Engineering Lecture Fund.

This talk is a part of the regular series of seminars from the Machine Learning in Medicine working group. This initiative aims to create and sustain collaborations between researchers at Weill Cornell Medicine and Cornell-Ithaca that have interests in machine learning applied to clinical questions. Seminars are normally held on the second Monday of every month from 10-11:00 am.

 

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Event Type

Seminar

Departments

Electrical and Computer Engineering, Biomedical Engineering, Webinar, Engineering

Tags

electrical engineering, computer engineering, ECE, CornellECE, Cornell ECE

Website

http://www.ece.cornell.edu/ece/news/e...

Contact E-Mail

tdr27@cornell.edu

Contact Name

T. Daniel Richter

Speaker

René Vidal

Speaker Affiliation

Johns Hopkins University

Open To

all

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