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Events for March 21, 2017
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USC Stem Cell Seminar: Flora Vaccarino, Yale University
Tue, Mar 21, 2017 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Flora Vaccarino, Yale University
Talk Title: TBD
Series: Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC Distinguished Speakers Series
Host: USC Stem Cell
More Info: http://stemcell.usc.edu/events
Webcast: http://keckmedia.usc.edu/stem-cell-seminarWebCast Link: http://keckmedia.usc.edu/stem-cell-seminar
Audiences: Everyone Is Invited
Contact: Cristy Lytal/USC Stem Cell
Event Link: http://stemcell.usc.edu/events
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CS Colloquium: Justin Cheng (Stanford) - Antisocial Computing: Explaining and Predicting Negative Behavior Online
Tue, Mar 21, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Justin Cheng, Stanford University
Talk Title: Antisocial Computing: Explaining and Predicting Negative Behavior Online
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Antisocial behavior and misinformation are increasingly prevalent online. As users interact with one another on social platforms, negative interactions can cascade, resulting in complex changes in behavior that are difficult to predict. My research introduces computational methods for explaining the causes of such negative behavior and for predicting its spread in online communities. It complements data mining with crowdsourcing, which enables both large-scale analysis that is ecologically valid and experiments that establish causality. First, in contrast to past literature which has characterized trolling as confined to a vocal, antisocial minority, I instead demonstrate that ordinary individuals, under the right circumstances, can become trolls, and that this behavior can percolate and escalate through a community. Second, despite prior work arguing that such behavioral and informational cascades are fundamentally unpredictable, I demonstrate how their future growth can be reliably predicted. Through revealing the mechanisms of antisocial behavior online, my work explores a future where systems can better mediate interpersonal interactions and instead promote the spread of positive norms in communities.
Biography: Justin Cheng is a PhD candidate in the Computer Science Department at Stanford University, where he is advised by Jure Leskovec and Michael Bernstein. His research lies at the intersection of data science and human-computer interaction, and focuses on cascading behavior in social networks. This work has received a best paper award, as well as several best paper nominations at CHI, CSCW, and ICWSM. He is also a recipient of a Microsoft Research PhD Fellowship and a Stanford Graduate Fellowship.
Host: CS Department
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Improved Myocardial Arterial Spin Labeled Perfusion Imaging
Tue, Mar 21, 2017 @ 01:00 PM - 02:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Hung Phi Do, Department of Physics and Astronomy, University of Southern California
Talk Title: Improved Myocardial Arterial Spin Labeled Perfusion Imaging
Series: Medical Imaging Seminar Series
Abstract: Coronary artery disease (CAD) affects more than 15.5 million Americans and causes approximately 310,000 deaths per year. Several different diagnostic tests are performed to diagnose and manage this disease. One of the most common is perfusion stress testing, primarily performed using single photon emission computed tomography (SPECT) or first-pass cardiovascular magnetic resonance (CMR). These methods require the use of ionizing radiation or exogenous contrast agents that carry associated risks to patients, especially those who require frequent assessment or have kidney dysfunction. Myocardial arterial spin labeling (ASL) is a promising MRI-based perfusion imaging method that can quantitatively measure myocardial tissue perfusion without the use of ionizing radiation or exogenous contrast agents. Its feasibility has been previously demonstrated by our lab, however several challenges remain, including low sensitivity, coarse spatial resolution, and limited spatial coverage. The contributions of this dissertation are (1) improving sensitivity, (2) exploring clinical applications, and (3) developing a new and advantageous labeling method for myocardial ASL.
Biography: Hung Phi Do is a Physics Ph.D. student working under the supervision of Prof. Nayak at the Magnetic Resonance Engineering Laboratory. His research focuses are MR physics and MR pulse sequence development for quantitative cardiovascular magnetic resonance. He received an M.S. in Electrical Engineering from the University of Southern California in 2014, a Diploma in Physics from the International Center for Theoretical Physics in 2009, and a B.S. in Physics from the Hanoi National University of Education in 2007.
Host: Prof. Krishna Nayak
Location: Hughes Aircraft Electrical Engineering Center (EEB) - PHE223
Audiences: Everyone Is Invited
Contact: Talyia White
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MHI Seminar Series - Visitor Program
Tue, Mar 21, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Magnús Már Halldórsson, Professor at Reykjavik University's School of Computer Science
Talk Title: Algorithms and Models for the Capacity of Arbitrary Wireless Networks
Abstract: At the heart of wireless network operation is the fundamental question of their capacity: How much communication can be achieved in a network, utilizing all the tools and diversity available: power control, scheduling, routing, channel assignment and rate adjustment?
The obvious aims of obtaining general purpose algorithms to solve this question run into two (walls) challenges:
- How to model communication and interference faithfully, and
- How to reason algorithmically in the more accurate models, which are also more intricate and harder to analyze.
We overview recent progress in developing algorithms for capacity and scheduling in the physical (or SINR) model with good performance guarantees on arbitrary networks. In particular, we indicate how many of the complications of the physical models can be abstracted away, at a small cost in performance. We also outline various efforts to add additional realism to the models, while maintaining generality and algorithmic tractability. We conclude with open questions and challenges.
This is based on joint work with Tigran Tonoyan
Biography: Prof. Magnús Már Halldórsson from Reyjkjavik University in Iceland will visit USC in late March 2017. He is a leading expert in algorithms for distributed computing and wireless networks. He has been the Chair of top conferences in the area including PODC 2014 and ICALP 2015. In 2017 he is leading the organization a Dagstuhl conference on "Foundations of Wireless Networking" together with Profs. C. Fragouli (UCLA), K. Jamieson (Princeton) and B. Krishnamachari (USC).
Host: Bhaskar Krishnamachari
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Cathy
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CS Colloquium: Nihar Shah (UC Berkeley) - Learning from People
Tue, Mar 21, 2017 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Nihar Shah, UC Berkeley
Talk Title: Learning from People
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Learning from people represents a new and expanding frontier for data science. Two critical challenges in this domain are of developing algorithms for robust learning and designing incentive mechanisms for eliciting high-quality data. In this talk, I describe progress on these challenges in the context of two canonical settings, namely those of ranking and classification. In addressing the first challenge, I introduce a class of "permutation-based" models that are considerably richer than classical models, and present algorithms for estimation that are both rate-optimal and significantly more robust than prior state-of-the-art methods. I also discuss how these estimators automatically adapt and are simultaneously also rate-optimal over the classical models, thereby enjoying a surprising a win-win in the bias-variance tradeoff. As for the second challenge, I present a class of "multiplicative" incentive mechanisms, and show that they are the unique mechanisms that can guarantee honest responses. Extensive experiments on a popular crowdsourcing platform reveal that the theoretical guarantees of robustness and efficiency indeed translate to practice, yielding several-fold improvements over prior art.
Biography: Nihar B. Shah is a PhD candidate in the EECS department at the University of California, Berkeley. He is the recipient of the Microsoft Research PhD Fellowship 2014-16, the Berkeley Fellowship 2011-13, the IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011/2012, and the SVC Aiya Medal from the Indian Institute of Science for the best master's thesis in the department. His research interests include statistics and machine learning, with a current focus on applications to learning from people.
Host: CS Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
