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Events for August 31, 2018

  • Meet USC: Admission Presentation, Campus Tour, and Engineering Talk

    Fri, Aug 31, 2018

    Viterbi School of Engineering Undergraduate Admission

    University Calendar

    This half day program is designed for prospective freshmen (HS juniors and younger) and family members. Meet USC includes an information session on the University and the Admission process, a student led walking tour of campus, and a meeting with us in the Viterbi School. During the engineering session we will discuss the curriculum, research opportunities, hands-on projects, entrepreneurial support programs, and other aspects of the engineering school. Meet USC is designed to answer all of your questions about USC, the application process, and financial aid.

    Reservations are required for Meet USC. This program occurs twice, once at 8:30 a.m. and again at 12:30 p.m.

    Please make sure to check availability and register online for the session you wish to attend. Also, remember to list an Engineering major as your "intended major" on the webform!


    Location: Ronald Tutor Campus Center (TCC) - USC Admission Office

    Audiences: Everyone Is Invited

    Contact: Viterbi Admission

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    Fri, Aug 31, 2018 @ 12:00 PM - 12:50 PM

    USC Viterbi School of Engineering

    Conferences, Lectures, & Seminars

    Speaker: Lisa Thomas, Project Manager, Exploration Technologies Group, Honeybee Robotics

    Talk Title: Engineering is a Team Sport

    Host: Engineering Honors Program & Dr. Prata

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

    Audiences: Everyone Is Invited

    Contact: Su Stevens

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  • PhD Defense - Zhengping Che

    Fri, Aug 31, 2018 @ 02:30 PM - 04:30 PM

    Thomas Lord Department of Computer Science

    University Calendar

    Title: Deep Learning Models for Temporal Data in Health Care
    Date, Time, and Location: Friday, August 31, 2018, at 2:30 PM in SAL 322

    Ph.D. Candidate: Zhengping Che

    Professor Yan Liu (chair)
    Professor Kevin Knight
    Professor Shinyi Wu (outside member)

    The national push for electronic health records has resulted in an exponential surge in volume, detail, and availability of digital health data which offers an unprecedented opportunity to solve many difficult and important problems in health care. Clinicians are collaborating with computer scientists by using this opportunity to improve the state of data-driven and personalized health care services. Meanwhile, recent success and development of deep learning is revolutionizing many domains and provides promising solutions to the problem of prediction and feature discovery on health care data, which have made us closest ever towards the ultimate goal of improving health quality, reducing cost, and most importantly saving lives. However, the recent rise of this research field with more available data and new applications has also introduced several challenges which have not been answered well. Our work focuses on providing deep learning-based solutions to three major challenges in this field from data heterogeneity, data availability, and the difficulty of applying deep learning in healthcare applications in practice. In this talk, we will first introduce a hierarchical deep generative model for multi-rate multivariate time series which can capture multi-scale temporal dependencies and complex underlying generation mechanism of temporal healthcare data. Then we will introduce a semi-supervised learning framework with modified generative adversarial networks to boost prediction performance with a limited amount of labeled data. Finally, we will describe our deep learning solutions to opioid usage and addiction study on a large-scale dataset and demonstrate how deep learning can be applied to important and urgent healthcare tasks in the real world.

    Location: Henry Salvatori Computer Science Center (SAL) - 322

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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