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Events for April 22, 2014
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Brain MRI Statistical Feature Extraction for Characterizing Neurodegenerative Diseases
Tue, Apr 22, 2014 @ 03:00 PM - 04:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Norbert Schuff, Ph.D., Dept. of Radiology and Biomedical Imaging, University of California, San Francisco
Talk Title: Brain MRI Statistical Feature Extraction for Characterizing Neurodegenerative Diseases
Series: Medical Imaging Seminar Series
Abstract: Brain lesions from neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease, are difficult to detect on MRI with the naked eye. This may explain why MRI is not yet used as a diagnostic tool for these conditions - except for ruling out other major brain diseases. It is therefore important to find effective solutions for the extraction of imaging features that can be used for diagnosing neurodegenerative diseases. I will discuss new approaches mainly anchored in information theory for extracting features from structural as well as functional brain MRI data. In particular, I will show new approaches for quantifying complexity of resting-state fMRI. Lastly, I will present initial results from MRI vascular fingerprinting - an approach for studying brain microvasculature.
Biography: I am a Professor in the Department of Radiology and Biomedical Imaging at the University of California, San Francisco. I am also Co-Director of the Neurodegenerative Diseases Research Interest Group and a researcher at the San Francisco Veterans Affairs Medical Center. I earned my PhD in Physics from the Ruprecht Karls University of Heidelberg in 1983. From 1984 to 1992, I developed NMR & MRI systems first with Bruker GmbH in Karlsruhe/Germany and later with Varian, Palo Alto, California. In 1993, I joined UCSF.
I study neurodegenerative diseases such as Alzheimer and Parkinson using MRI. My interest is to better capture abnormal brain structure and function for improving prediction, diagnosis and monitoring progression of these devastating conditions. I accomplish this by developing new methods for extracting image features using MRI physics as well as modern concepts of probability, statistical learning and information theory.
Host: Prof. Justin Haldar
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Talyia Veal
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Epstein Institute / ISE 651 Seminar Series
Tue, Apr 22, 2014 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Meisam Razaviyayn, Department of Electrical Engineering, University of Minnesota
Talk Title: "Successive Convex Approximation: A Unified Analysis and Applications in Big Data"
Abstract: The (randomized) block coordinate descent (BCD) method is widely used for minimizing a continuous function of several block variables. At each iteration of this method, a single block of variables is optimized, while the remaining variables are held fixed. Unfortunately, the requirement for BCD convergence is often too restrictive for many practical scenarios. In this talk, we study an alternative inexact BCD approach which updates the variable blocks by successively minimizing a sequence of approximations which are either locally tight upper bounds for the objective or strictly convex local approximations. The main contributions of this work include the characterizations of the convergence conditions for a fairly wide class of such methods, especially for the cases where the objective functions are either non-differentiable or nonconvex. Our results unify and extend the existing convergence results for many classical algorithms such as the BCD method, the difference of convex functions (DC) method, the expectation maximization(EM) algorithm, as well as the block forward-backward splitting algorithm, all of which are popular for large scale optimization problems involving big data. At the end of the talk, we will see applications of this framework in tensor decomposition, dictionary learning for image processing, and beamformer design for wireless communications.
TUESDAY, APRIL 22, 2014
VON KLEINSMID CENTER (VKC) ROOM 100
3:30 - 4:50 PM
Biography: Meisam Razaviyayn is a visiting PhD student at Daniel J. Epstein Department of Industrial and Systems Engineering at University of Southern California. He received his undergraduate degree in Electrical Engineering from Isfahan University of Technology in 2008. He obtained his M.S. in Mathematics and Electrical Engineering from University of Minnesota. Now he is in the last year of his PhD, advised by Professor Tom Luo at the university of Minnesota. Meisam received different awards and fellowships such as University of Minnesota Doctoral Dissertation Fellowship, Electrical and Computer Engineering Department Fellowship, Fifth Place in ACM International Programming Regional Contest, and Iran national mathematics Olympiad silver medal. He has also been among the finalist of the best paper prize for young researcher in continuous optimization, ICCOPT 2013, and the finalist for the best student paper award, SPAWC 2010. Meisam's research interests include large scale optimization, machine learning, computational issues in wireless data communication, and statistical signal processing.
Host: Daniel J. Epstein Department of Industrial and Systems Engineering
More Information: Seminar-Razaviyayn.doc
Location: Von Kleinsmid Center For International & Public Affairs (VKC) - Room 100
Audiences: Everyone Is Invited
Contact: Georgia Lum
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CS Colloquium: Amir Houmansadr (UTexas - Austin) - The Cyberspace Battle for Information: Combating Internet Censorship
Tue, Apr 22, 2014 @ 04:00 PM - 05:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Amir Houmansadr, UTexas - Austin
Talk Title: The Cyberspace Battle for Information: Combating Internet Censorship
Series: CS Colloquium
Abstract: The lecture will be available to stream from your browser here.
The Internet has become ubiquitous, bringing many benefits to people across the globe. Unfortunately, Internet users face threats to their security and privacy: repressive regimes deprive them of freedom of speech and open access to information, governments and corporations monitor their online behavior, advertisers collect and sell their private data, and cybercriminals hurt them financially through security breaches.
My research aims to make Internet communications more secure and privacy-preserving. In this talk, I will focus on the design, implementation, and analysis of tools that help users bypass Internet censorship. I will discuss the major challenges in building robust censorship circumvention tools, introduce two novel classes of systems that we have developed to overcome these challenges, and conclude with several directions for future research.
Biography: Amir Houmansadr is a postdoctoral scholar at the University of Texas at Austin. He received his Ph.D. from the University of Illinois at Urbana-Champaign in August 2012. Amir’s research revolves around various network security and privacy problems, including Internet censorship circumvention, network traffic analysis, and anonymous communications. He has received several awards for his research, including the Best Practical Paper Award at the IEEE Symposium on Security & Privacy (Oakland) 2013.
Host: Ethan Katz-Bassett
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Amir Houmansadr (UTexas - Austin) - The Cyberspace Battle for Information: Combating Internet Censorship
Tue, Apr 22, 2014 @ 04:00 PM - 05:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Amir Houmansadr, UTexas - Austin
Talk Title: The Cyberspace Battle for Information: Combating Internet Censorship
Series: CS Colloquium
Abstract: The lecture is available to stream from your browser here starting at 4 PM. Please right-click the link to open in a new tab or window for best performance.
The Internet has become ubiquitous, bringing many benefits to people across the globe. Unfortunately, Internet users face threats to their security and privacy: repressive regimes deprive them of freedom of speech and open access to information, governments and corporations monitor their online behavior, advertisers collect and sell their private data, and cybercriminals hurt them financially through security breaches.
My research aims to make Internet communications more secure and privacy-preserving. In this talk, I will focus on the design, implementation, and analysis of tools that help users bypass Internet censorship. I will discuss the major challenges in building robust censorship circumvention tools, introduce two novel classes of systems that we have developed to overcome these challenges, and conclude with several directions for future research.
Biography: Amir Houmansadr is a postdoctoral scholar at the University of Texas at Austin. He received his Ph.D. from the University of Illinois at Urbana-Champaign in August 2012. Amir’s research revolves around various network security and privacy problems, including Internet censorship circumvention, network traffic analysis, and anonymous communications. He has received several awards for his research, including the Best Practical Paper Award at the IEEE Symposium on Security & Privacy (Oakland) 2013.
Host: Ethan Katz-Bassett
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
