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Events for February 05, 2020

  • Viterbi Mock Interview Day

    Wed, Feb 05, 2020 @ 09:30 AM - 04:30 PM

    Viterbi School of Engineering Career Connections

    Receptions & Special Events

    Viterbi Mock Interviews Day
    Location: Michelson Hall (MCB 101)

    Sign up below for a time slot between 9:30 am and 4:30 pm to have a 20 minute mock interview with one of our employer participants on February 5th, 2020!

    Register: https://slotted.co/2020mocks

    Aera Energy,
    Air Force Test Center,
    CRW social gaming,
    DK Engineer, Corp,
    Ensign-Bickford Industries/Honeybee Robotics,
    Farmers Insurance,
    Granite Construction,
    MATT Construction Company,
    OnPrem Solution Partners,
    SCS Engineers,
    Suffolk Construction,
    Syng, Inc.,
    Turner Construction Company,
    Upful, Inc,
    W.E. O'Neil Construction,
    and West Basin Municipal Water District

    Location: Michelson Center for Convergent Bioscience (MCB) - 101

    Audiences: All Viterbi

    Contact: RTH 218 Viterbi Career Connections

  • NL Seminar-Why journalism is broken and how data can help fix it

    Wed, Feb 05, 2020 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars

    Speaker: Gabriel Kahn , USC Annenberg

    Talk Title: Why journalism is broken and how data can help fix it

    Series: Natural Language Seminar

    Abstract: Pizza gate, Russian trolls, deep fakes. We live in an information swamp and it sucks. At its core, the crisis in journalism is about a shifting economic model that has made it difficult for legitimate news organizations to survive. The consequences are dire. But harnessing data in the right ways can provide vital information to communities and can help news organizations do more with less. The future of a healthy news environment requires collaboration between news, data and computer science. Gabriel Kahn outlines the current problems and some potential solutions.

    Biography: Gabriel Kahn has worked as a newspaper correspondent and editor for three decades, including 10 years at The Wall Street Journal, where he served as Los Angeles bureau chief, deputy Hong Kong bureau chief and deputy Southern Europe bureau chief, based in Rome. He has reported from more than a dozen countries on three continents. He joined USC Annenberg in the fall of 2010, where he jointly runs the Media, Economics and Entrepreneurship program. The goal of M 2e is to bolster students understanding of economics and encourage innovation and experimentation with new ideas in communication and journalism. In addition to his teaching and reporting work, Kahn studies the economic models of the news industry and consults with startups and established news companies on strategy. In 2018, he launched Crosstown, which has pioneered a new approach to local news through data.

    Host: Emily Sheng

    More Info: https://nlg.isi.edu/nl-seminar

    Webcast: https://bluejeans.com/s/FVVU4/

    Location: Information Science Institute (ISI) - CR 689

    WebCast Link: https://bluejeans.com/s/FVVU4/

    Audiences: Everyone Is Invited

    Contact: Peter Zamar

    Event Link: https://nlg.isi.edu/nl-seminar

  • Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar

    Wed, Feb 05, 2020 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars

    Speaker: Christopher Ré, Department of Computer Science at Stanford University

    Talk Title: If You Want to be Rich, Get a lot of Money: Theory and Systems for Weak Supervision

    Series: Center for Cyber-Physical Systems and Internet of Things

    Abstract: If you want to build a high-quality machine learning product, build a large, high-quality training set. At first glance, this seems as useful as the statement "if you want to be rich, get a lot of money." However, a key idea driving our work is that new theoretical and systems concepts including weak supervision, automatic data augmentation policies, and more, can enable engineers to build training sets more quickly and cost effectively. Along with state-of-the-art results on benchmarks, these concepts have allowed our group and collaborators to build a range of state-of-the-art applications including patient-care monitoring on electronic health records, automatic triage systems for radiologists, and enabling cardiologists to spot rare abnormalities in video MRI-along with widely used products from Apple and Google. This talk describes the theoretical and systems challenges that such applications create.

    On the machine-learning theory side, a key problem is estimating the quality and correlation of various sources of training data-”but without ground truth labels. This problem connects to classical questions about estimating the covariance of latent variable models. We describe our new techniques that solve this case and can even improve fully supervised methods for estimating the structure of graphical models.

    On the machine-learning systems side, this theory opens up new ways to build machine-learning systems. Here, we describe our recent work on systems that help engineers build and maintain machine learning products-without writing low-level code in frameworks like TensorFlow. These systems draw on recent ideas in machine learning, e.g., zero-code deep learning systems, and twists on classical data management ideas, e.g., schemas to separate the model, the supervision, and down-stream serving code.
    Much of this work is open source and available at http://snorkel.org or my website.

    Biography: Christopher (Chris) Ré is an associate professor in the Department of Computer Science at Stanford University who is affiliated with the Statistical Machine Learning Group and Stanford AI Lab. His recent work is to understand how software and hardware systems will change as a result of machine learning along with a continuing, petulant drive to work on math problems. Research from his group has been incorporated into scientific and humanitarian efforts, such as the fight against human trafficking, along with products from technology and enterprise companies. He cofounded a company, based on his research into machine learning systems, that was acquired by Apple in 2017. More recently, he cofounded SambaNova systems based, in part, on his work on accelerating machine learning. He received a SIGMOD Dissertation Award in 2010, an NSF CAREER Award in 2011, an Alfred P. Sloan Fellowship in 2013, a Moore Data Driven Investigator Award in 2014, the VLDB early Career Award in 2015, the MacArthur Foundation Fellowship in 2015, and an Okawa Research Grant in 2016. His research contributions have spanned database theory, database systems, and machine learning, and his work has won best paper at a premier venue in each area, respectively, at PODS 2012, SIGMOD 2014, and ICML 2016.

    Host: Paul Bogdan, pbogdan@usc.edu

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132

    Audiences: Everyone Is Invited

    Contact: Talyia White

  • AME Seminar

    Wed, Feb 05, 2020 @ 03:30 PM - 04:30 PM

    Conferences, Lectures, & Seminars

    Speaker: Pavlos P. Vlachos, Purdue

    Talk Title: Fluid Mechanics in Clinical Echocardiography

    Abstract: In this talk we will probe flows in cardiac disease using in-vivo measurements in clinical settings, and we will discuss how traditional experimental fluids mechanics tools can translate into clinical practice.

    Flows in the cardiovascular system manifest intrinsic complexity, which is often associated with diseased states. Imaging modalities such as ultrasound/echocardiography and phase-contrast MRI provide unique opportunities and challenges for flow measurements in patients. Currently, the relationship between clinical flow measurements and clinical diagnostic parameters is qualitative, and often is reliant on heuristics and non-physical assumptions.

    In this talk we will discuss how to overcome these limitations by integrating medical imaging with experimental fluid mechanics, in order to, ultimately, improve accuracy, robustness, and clinical diagnostic utility of these tools.

    Specifically, we will discuss how fluid mechanics can be used in the analysis of echocardiographic imaging for heart failure. We will show an improved approach for clinical implementation of EchoPIV (echocardiographic Particle Image Velocimetry) and a new method for the velocity reconstruction of Color-Doppler flow imaging. Finally, we will present a use-case in the analysis of fetal and neonatal echocardiograms of babies born with single ventricle (hypoplastic left heart syndrome). If time permits, some additional examples of application to 4D flow MRI will be presented.

    Host: AME Department

    Location: James H. Zumberge Hall Of Science (ZHS) - 159

    Audiences: Everyone Is Invited

    Contact: Tessa Yao