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Events for May 04, 2017
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AI Seminar
Thu, May 04, 2017 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Yan Liu, Associate Professor, USC
Talk Title: Deep Learning Models for Time Series Data Analysis with Applications to Healthcare
Abstract: Many emerging applications of big data involve time series data. We'll discuss a collection of deep learning models to effectively analyze and model large-scale time series data. We'll show experiment results to demonstrate the effectiveness of our models in healthcare.
Biography: Yan Liu is an associate professor in Computer Science Department at University of Southern California from 2010. Before that, she was a Research Staff Member at IBM Research. She received her M.Sc and Ph.D. degree from Carnegie Mellon University in 2004 and 2007. Her research interest includes developing scalable machine learning and data mining algorithms for time series data and structured data with applications to social media analysis, computational biology, climate modeling and health care. She has received several awards, including NSF CAREER Award, Okawa Foundation Research Award, ACM Dissertation Award Honorable Mention, Best Paper Award in SIAM Data Mining Conference, Yahoo, IBM and Facebook Faculty Award and the winner of several data mining competitions, such as KDD Cup and INFORMS data mining competition.
Host: Mayank Kejriwal
More Info: http://webcastermshd.isi.edu/Mediasite/Play/5447fbec7809488a9444c23f8b3619ce1d
Location: Information Science Institute (ISI) - 11th floor large conference room
Audiences: Everyone Is Invited
Contact: Kary LAU
Event Link: http://webcastermshd.isi.edu/Mediasite/Play/5447fbec7809488a9444c23f8b3619ce1d
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The FuzzyLog Approach to Building Distributed Services
Thu, May 04, 2017 @ 04:00 PM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mahesh Balakrishnan, Yale University
Talk Title: The FuzzyLog Approach to Building Distributed Services
Abstract: Control plane applications such as coordination services, SDN controllers, filesystem namespaces, and big data schedulers have strong requirements for consistency as well as performance. Building such applications is currently a black art, requiring a slew of complex distributed protocols that are inefficient when layered and difficult to combine. The shared log approach (seen in the Corfu, Tango, and CorfuDB systems) achieves simplicity for distributed applications by replacing complex protocols with a single shared log; however, it does so by introducing a global ordering over all updates in the system, which can be expensive, unnecessary, and sometimes impossible. We propose the FuzzyLog abstraction, which provides applications the simplicity of a shared log without its drawbacks. The FuzzyLog allows applications to construct and access a durable, iterable partial order of updates in the system. FuzzyLog applications retain the simplicity of their shared log counterparts while extracting parallelism, providing a range of consistency guarantees and tolerating network partitions.
Biography: Mahesh Balakrishnan is an Associate Professor (pre-tenure) at Yale University since Fall 2015. He received a PhD in Computer Science from Cornell University in 2009. He worked at Microsoft Research Silicon Valley from 2008 to 2014, where he co-led the CORFU and Tango projects on shared log systems, and briefly at VMware Research in 2015. His research interests span distributed systems, storage and networking. Currently, his research centers on new abstractions that simplify the construction of fast, reliable and consistent systems, while hiding the complexity of concurrency, failures and hardware details from programmers. He has published 35+ peer-reviewed papers in systems conferences such as SOSP, NSDI and FAST and journals such as TOCS. His current research is funded by NSF, Facebook Awards, and a VMware Early Career faculty grant.
Host: Xuehai Qian, x04459, xuehai.qian@usc.edu
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
