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SUNMONTUEWEDTHUFRISAT
Events for October 27, 2016
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Biotechnology Lecture SeriesThu, Oct 27, 2016 @ 10:30 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Various, Amgen
Talk Title: R&D Insights from Lab Bench to Patient Bedside
Abstract: USC researchers have the opportunity to gain research and development insights with a new biotechnology lecture series sponsored by Amgen and the Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC.
The weekly lecture series, "R&D Insights from Lab Bench to Patient Bedside" takes place Thursdays at 10:30AM-12:00PM at USC's Health Sciences Campus from September 1, 2016 through November 10, 2016.
The talks will feature Amgen scientists speaking about:
Identifying a possible therapeutic target and its role in disease
Increasing therapeutic efficacy and safety
Process development, devices and manufacturing
Case studies from bench to clinic
Lectures will take place at the BCC First Floor Seminar Room or ZNI Herklotz Seminar Room.
RSVP at http://www.usc.edu/esvp (use code: amgenlecture). Space is limited. Preference will be given to SCRM master's students, PhDs, and postdocs, and attending all lectures is mandatory.
Please contact qliumich@usc.edu or karenw03@amgen.com for further details.
Host: USC Stem Cell/Amgen
More Info: https://calendar.usc.edu/event/biotechnology_lecture_series_rd_insights_from_lab_bench_to_patient_bedside?utm_campaign=widget&utm_medium=widget&utm_source=USC+Event+Calendar#.V8dKNLX8vW4
Audiences: Everyone Is Invited
Contact: Cristy Lytal/USC Stem Cell
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Machine Learning Center and Ming Hsieh Institute Series on Mathematical Foundations of Learning from Data and Signals Joint Seminar
Thu, Oct 27, 2016 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Sewoong Oh, University of Illinois Urbana-Champaign
Talk Title: Fundamental Limits and Efficient Algorithms in Adaptive Crowdsourcing
Series: MHI
Abstract: Adaptive schemes, where tasks are assigned based on the data collected thus far, are widely used in practical crowdsourcing systems to efficiently allocate the budget. However, existing theoretical analyses of crowdsourcing systems suggest that the gain of adaptive task assignments is minimal. To bridge this gap, we propose a new model for representing practical crowdsourcing systems, which strictly generalizes the popular Dawid-Skense model, and characterize the fundamental trade-off between budget and accuracy. We introduce a novel adaptive scheme that matches this fundamental limit. We introduce new techniques to analyze the spectral analyses of non-back-tracking operators, using density evolution techniques from coding theory.
Biography: Sewing Oh is an Assistant Professor of Industrial and Enterprise Systems Engineering at UIUC. He received his PhD from the department of Electrical Engineering at Stanford University. Following his PhD, he worked as a postdoctoral researcher at Laboratory for Information and Decision Systems (LIDS) at MIT. He was co-awarded the Kenneth C. Sevcik outstanding student paper award at the Sigmetrics 2010, the best paper award at the SIGMETRICS 2015, and NSF CAREER award in 2016.
Host: Mahdi Soltanolkotabi
More Information: Oh Seminar Announcement.png
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Gloria Halfacre
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CS Colloquium: Sewoong Oh (UIUC) - Fundamental Limits and Efficient Algorithms in Adaptive Crowdsourcing
Thu, Oct 27, 2016 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Sewoong Oh , UIUC
Talk Title: Fundamental Limits and Efficient Algorithms in Adaptive Crowdsourcing
Series: Yahoo! Labs Machine Learning Seminar Series
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium. Part of Yahoo! Labs Machine Learning Seminar Series.
Adaptive schemes, where tasks are assigned based on the data collected thus far, are widely used in practical crowdsourcing systems to efficiently allocate the budget. However, existing theoretical analyses of crowdsourcing systems suggest that the gain of adaptive task assignments is minimal. To bridge this gap, we propose a new model for representing practical crowdsourcing systems, which strictly generalizes the popular Dawid-Skene model, and characterize the fundamental trade-off between budget and accuracy. We introduce a novel adaptive scheme that matches this fundamental limit. We introduce new techniques to analyze the spectral analyses of non-back-tracking operators, using density evolution techniques from coding theory.
Biography: Sewoong Oh is an Assistant Professor of Industrial and Enterprise Systems Engineering at UIUC. He received his PhD from the department of Electrical Engineering at Stanford University. Following his PhD, he worked as a postdoctoral researcher at Laboratory for Information and Decision Systems (LIDS) at MIT. He was co-awarded the Kenneth C. Sevcik outstanding student paper award at the Sigmetrics 2010, the best paper award at the SIGMETRICS 2015, and NSF CAREER award in 2016.
Host: Yan Liu
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Toward Exascale Resilience: Hardware Mechanisms and Containment Domains
Thu, Oct 27, 2016 @ 04:00 PM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mattan Erez, University of Texas, Austin
Talk Title: Toward Exascale Resilience: Hardware Mechanisms and Containment Domains
Series: EE 598 Computer Engineering Seminar Series
Abstract: In this talk I will present a scalable and efficient resiliency scheme based on the concept of Containment Domains. Containment domains are programming and system constructs that encapsulate and express application resiliency needs and interact with the system to tune and specialize error detection, state preservation and restoration, and recovery schemes. Containment domains have weak transactional semantics and are nested to take advantage of the machine hierarchy and to enable distributed and hierarchical state preservation, restoration, and recovery as an alternative to non-scalable and inefficient checkpoint-restart (and variants). One of the key motivations behind this work is the idea of proportionality, where the resources devoted to a feature are proportional to the application and scenario needs. Proportionality is critical to continued scaling and performance under the increasing constraints of bandwidth, power, and energy. Essentially, one-size-fits-all and worst-case design approaches are no longer sufficient to building reliable and efficient systems. I will also briefly discuss some of the hardware mechanisms necessary for reliability and resilience and the tradeoffs they offer for proportionality.
Biography: Mattan Erez is an Associate Professor and holder of the Temple Foundation Professor Fellowship (#4) at the Department of Electrical and Computer Engineering at the University of Texas at Austin. His research focuses on improving the performance, efficiency, and scalability of computing systems through advances in hardware architecture, software systems, and programming models. The vision is to increase the cooperation across system layers and develop flexible and adaptive mechanisms for proportional resource usage. Mattan received a B.Sc. in Electrical Engineering and a B.A. in Physics from the Technion, Israel Institute of Technology and his M.S and Ph.D. in Electrical Engineering from Stanford University. He is a recipient of a Presidential Early Career Award for Scientists and Engineers, an Early Career Research Award from the Department of Energy, and an NSF CAREER Award.
Host: Xuehai Qian, x04459, xuehai.qian@usc.edu
Location: Olin Hall of Engineering (OHE) - 100D
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
