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Events for the 4th week of December

  • USC Stem Cell Seminar: Alysson Muotri, University of California, San Diego

    USC Stem Cell Seminar: Alysson Muotri, University of California, San Diego

    Tue, Dec 20, 2016 @ 11:00 AM - 12:00 PM

    Alfred E. Mann Department of Biomedical Engineering

    Conferences, Lectures, & Seminars

    Speaker: Alysson Muotri, University of California, San Diego

    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-seminar

    Location: Eli & Edythe Broad CIRM Center for Regenerative Medicine & Stem Cell Resch. (BCC) - First Floor Conference Room

    WebCast 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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    Tue, Dec 20, 2016 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars

    Speaker: Latifur Khan, University of Texas at Dallas


    Series: Artificial Intelligence Seminar

    Abstract: Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Data streams demonstrate several unique properties that together conform to the characteristics of big data (i.e., volume, velocity, variety and veracity) and add challenges to data stream mining. In this talk we will present an organized picture on how to handle various data mining techniques in data streams. Most existing data stream classification techniques ignore one important aspect of stream data: arrival of a novel class. We address this issue and propose a data stream classification technique that integrates a novel class detection mechanism into traditional classifiers, enabling automatic detection of novel classes before the true labels of the novel class instances arrive. Novel class detection problem becomes more challenging in the presence of concept-drift, when the underlying data distributions evolve in streams. In this talk we will show how to make fast and correct classification decisions under this constraint with limited labeled training data and apply them to real benchmark data. In addition, we will present a number of stream classification applications such as adaptive malicious code detection, website fingerprinting, evolving insider threat detection and textual stream classification.

    This research was funded in part by NSF, NASA, Air Force Office of Scientific Research (AFOSR), IBM and Raytheon.

    Biography: Dr. Latifur Khan is currently a full Professor (tenured) in the Computer Science department at the University of Texas at Dallas, USA where he has been teaching and conducting research since September 2000. He received his Ph.D. and M.S. degrees in Computer Science from the University of Southern California in August of 2000, and December of 1996 respectively. Dr. Khan is an ACM Distinguished Scientist. He has received prestigious awards including the IEEE Technical Achievement Award for Intelligence and Security Informatics.

    Dr. Khan has published over 200 papers in prestigious journals, and in peer reviewed conference proceedings. Currently, his research area focuses on big data management and analytics, data mining, complex data management including geo-spatial data and multimedia data.

    Host: Jose Luis Ambite

    More Info: www.utdallas.edu/~lkhan/

    Webcast: http://webcastermshd.isi.edu/Mediasite/Play/9ca403ad75674c839ec10e1c0ac9aee71d

    Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey

    WebCast Link: http://webcastermshd.isi.edu/Mediasite/Play/9ca403ad75674c839ec10e1c0ac9aee71d

    Audiences: Everyone Is Invited

    Contact: Peter Zamar

    Event Link: www.utdallas.edu/~lkhan/

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