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Events for March 08, 2016

  • Junior Faculty Candidate Mini-symposium: Department of Stem Cell Biology and Regenerative Medicine

    Tue, Mar 08, 2016 @ 08:30 AM - 04:30 PM

    Alfred E. Mann Department of Biomedical Engineering

    Conferences, Lectures, & Seminars


    Speaker: -, -

    Talk Title: -

    Abstract: 10:30 a.m.
    Blood cell engineering and drug discovery using iPS cells
    Sergei Doulatov, PhD
    Boston Children's Hospital

    11:45 a.m.
    Cell diversity in liver regeneration and cancer development
    Joan Font-Burgada, PhD
    University of California, San Diego

    2 p.m.
    Self-renewal of human hematopoietic progenitor cells: From the clinic to the laboratory and back to the clinic
    Hsiang-Ying (Sherry) Lee, PhD

    3:15 p.m.
    Towards engineering developmental systems: A new family of synthetic cell-cell communication pathways to control multicellular self-organization
    Leonardo Morsut, PhD
    University of California, San Francisco

    4:30 p.m.
    Development and evolution of the human cerebral cortex
    Alexander Pollen, PhD
    University of California, San Francisco

    Reception to follow

    Host: Department of Stem Cell Biology and Regenerative Medicine

    More Info: https://stemcell.usc.edu/events/details/?event_id=919129

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

    Audiences: Everyone Is Invited

    Contact: Cristy Lytal/USC Stem Cell

    Event Link: https://stemcell.usc.edu/events/details/?event_id=919129

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  • CS Colloquium: Andreas Haeberlen (U. of Pennsylvania) - Accountability for Distributed Systems

    Tue, Mar 08, 2016 @ 11:00 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Andreas Haeberlen, U. of Pennsylvania

    Talk Title: Accountability for Distributed Systems

    Series: CS Colloquium

    Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium

    Many of our everyday activities are now performed online - whether it is banking, shopping, or chatting with friends. Behind the scenes, these activities are implemented by large distributed systems that often contain machines from several different organizations. Usually, these machines do what we expect them to, but occasionally they 'misbehave' - sometimes by mistake, sometimes to gain an advantage, and sometimes because of a deliberate attack.

    In society, accountability is widely used to counter such threats.
    Accountability incentivizes good performance, exposes problems, and builds trust between competing individuals and organizations. In this talk, I will argue that accountability is also a powerful tool for designing distributed systems. An accountable distributed system ensures that 'misbehavior' can be detected, and that it can be linked to a specific machine via some form of digital evidence. The evidence can then be used just like in the 'offline' world, e.g., to correct the problem and/or to take action against the responsible organizations.

    I will give an overview of our progress towards accountable distributed systems, ranging from theoretical foundations and efficient algorithms to practical applications. I will also present one result in detail: a technique that can detect information leaks through covert timing channels.

    Biography: Andreas Haeberlen is a Raj and Neera Singh Assistant Professor at the University of Pennsylvania. His research interests are in distributed systems, networking, and security. Andreas received his PhD degree in Computer Science from Rice University in 2009; he is the recipient of a NSF CAREER award, and he was awarded the Otto Hahn Medal by the Max Planck Society.

    Host: CS Department

    Location: Olin Hall of Engineering (OHE) - 136

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

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  • Epstein Institute Seminar - ISE 651

    Tue, Mar 08, 2016 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Karen Smilowitz, Northwestern University

    Talk Title: Logistical Challenges at Mass Participation Events: Operations Research Models for Marathon Planning

    Host: John Carlsson

    More Information: March 8, 2016_Smilowitz.pdf

    Location: Ethel Percy Andrus Gerontology Center (GER) - 206

    Audiences: Everyone Is Invited

    Contact: Michele ISE

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  • CS Colloquium: Hristo Paskov (Stanford) -Learning with N-Grams: from Massive Scales to Compressed Representations

    Tue, Mar 08, 2016 @ 04:00 PM - 05:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Hristo Paskov, Stanford

    Talk Title: Learning with N-Grams: from Massive Scales to Compressed Representations

    Series: CS Colloquium

    Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium

    N-gram models are essential in any kind of text processing; they offer simple baselines that are surprisingly competitive with more complicated "state of the art" techniques. I will present a survey of my work for learning with arbitrarily long N-grams at massive scales. This framework combines fast matrix multiplication with a dual learning paradigm that I am developing to reconcile sparsity-inducing penalties with Kernels. The presentation will also introduce Dracula, a new form of deep learning based on classical ideas from compression. Dracula is a combinatorial optimization problem, and I will discuss some its problem structure and use this to visualize its solution surface.

    The lecture will be available to stream HERE. Open in new window or tab for best results.

    Biography: Hristo Paskov was born in Bulgaria and grew up in New York. He received a B.S. and M.Eng. in Computer Science from MIT while conducting research at the MIT Datacenter and Tomaso Poggio's group (CBCL). He is currently finishing a Ph.D. in Computer Science at Stanford under the advisement of John Mitchell and Trevor Hastie. His research spans machine learning, optimization, and algorithms in order to build large-scale statistical methods and data representations. He is developing a new deep learning paradigm that uses compression to find compact data representations that are useful for statistical inference. His work has provided state of the art methods for security and natural language processing.

    Host: CS Department

    Location: Henry Salvatori Computer Science Center (SAL) - 101

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

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