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Events for May 02, 2024

  • Repeating EventAircraft Accident Investigation AAI 24-4

    Thu, May 02, 2024 @ 08:00 AM - 04:00 PM

    Aviation Safety and Security Program

    University Calendar


    The course is designed for individuals who have limited investigation experience. All aspects of the investigation process are addressed, starting with preparation for the investigation through writing the final report. It covers National Transportation Safety Board and International Civil Aviation Organization (ICAO) procedures. Investigative techniques are examined with an emphasis on fixed-wing investigation. Data collection, wreckage reconstruction, and cause analysis are discussed in the classroom and applied in the lab.
    The USC Aircraft Accident Investigation lab serves as the location for practical exercises. Thirteen aircraft wreckages form the basis of these investigative exercises. The crash laboratory gives the student an opportunity to learn the observation and documentation skills required of accident investigators. The wreckage is examined and reviewed with investigators who have extensive actual real-world investigation experience. Examination techniques and methods are demonstrated along with participative group discussions of actual wreckage examination, reviews of witness interview information, and investigation group personal dynamics discussions.

    Location: WESTMINSTER AVENUE BUILDING (WAB) - Unit E

    Audiences: Everyone Is Invited

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    Contact: Daniel Scalese

    Event Link: https://avsafe.usc.edu/wconnect/CourseStatus.awp?&course=24AAAI4

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  • Repeating EventSafety Management for Aviation Maintenance MAINT 24-2

    Thu, May 02, 2024 @ 08:00 AM - 04:00 PM

    Aviation Safety and Security Program

    University Calendar


    This course provides supervisors with aviation safety principles and practices needed to manage the problems associated with aircraft maintenance operations. In addition, it prepares attendees to assume safety responsibilities in their areas of operation. It does not teach aircraft maintenance and assumes the attendee has a maintenance background.

    Location: Century Boulevard Building (CBB) - 920

    Audiences: Everyone Is Invited

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    Contact: Daniel Scalese

    Event Link: https://avsafe.usc.edu/wconnect/CourseStatus.awp?&course=24AMAINT2

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  • Computational Science Distinguished Seminar Series

    Thu, May 02, 2024 @ 09:30 AM - 10:30 AM

    USC School of Advanced Computing

    Conferences, Lectures, & Seminars


    Speaker: Yannis Kevrekidis, Johns Hopkins University

    Talk Title: No Equations, No Variables, No Space and No Time: Data and the Modeling of Complex Systems

    Abstract: I will give an overview of a research path in data driven modeling of complex systems over the last 30 or so years – from the early days of shallow neural networks and autoencoders for nonlinear dynamical system identification, to the more recent derivation of data driven “emergent” spaces in which to better learn generative PDE laws. In all illustrations presented, I will try to point out connections between the “traditional” numerical analysis we know and love, and the more modern data-driven tools and techniques we now have – and some mathematical questions they hopefully make possible for us to answer.

    Biography: Yannis Kevrekidis studied Chemical Engineering at the National Technical University in Athens. He then followed the steps of many alumni of that department to the University of Minnesota, where he studied with Rutherford Aris and Lanny Schmidt (as well as Don Aronson and Dick McGehee in Math). He was a Director's Fellow at the Center for Nonlinear Studies in Los Alamos in 1985-86 (when Soviets still existed and research funds were plentiful). He then had the good fortune of joining the faculty at Princeton, where he taught Chemical Engineering and also Applied and Computational Mathematics for 31 years; seven years ago he became Emeritus and started fresh at Johns Hopkins (where he somehow is also Professor of Urology). His work always had to do with nonlinear dynamics (from instabilities and bifurcation algorithms to spatiotemporal patterns to data science in the 90s, nonlinear identification, multiscale modeling, and back to data science/ML); and he had the additional good fortune to work with several truly talented experimentalists, like G. Ertl's group in Berlin. Currently -on leave from Hopkins- he works with the Defense Sciences Office at DARPA. When young and promising he was a Packard Fellow, a Presidential Young Investigator and the Ulam Scholar at Los Alamos National Laboratory. He holds the Colburn, CAST Wilhelm and Walker awards of the AIChE, the Crawford and the Reid prizes of SIAM, he is a member of the NAE, the American Academy of Arts and Sciences, and the Academy of Athens. 

    Host: The School of Advanced Computing

    More Info: https://sac.usc.edu/events/?hash=1m3nCA4M337

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

    Audiences: Everyone Is Invited

    Contact: Tessa Yao

    Event Link: https://sac.usc.edu/events/?hash=1m3nCA4M337

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  • Machine Learning Center Seminar

    Thu, May 02, 2024 @ 12:00 PM - 01:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Pengtao Xie , Assistant Professor, Department of Electrical and Computer Engineering - University of California, San Diego

    Talk Title: Foundation Models and Generative AI for Medical Imaging Segmentation in Ultra-Low Data Regimes

    Abstract: Semantic segmentation of medical images is pivotal in disease diagnosis and treatment planning. While deep learning has excelled in automating this task, a major hurdle is the need for numerous annotated masks, which are resource-intensive to produce due to the required expertise and time. This scenario often leads to ultra-low data regimes where annotated images are scarce, challenging the generalization of deep learning models on test images. To address this, we introduce two complementary approaches. One involves developing foundation models. The other involves generating high-fidelity training data consisting of paired segmentation masks and medical images. In the former, our bi-level optimization based method can effectively adapt the general-domain Segment Anything Model (SAM) to the medical domain with just a few medical images. In the latter, our multi-level optimization based method can perform end-to-end generation of high-quality training data from a minimal number of real images. On eight segmentation tasks involving various diseases, organs, and imaging modalities, our methods demonstrate strong generalization performance in both in-domain and out-of-domain settings. Our methods require 8-12 times less training data than baselines to achieve comparable performance.

    Biography: Pengtao Xie is an assistant professor in the Department of Electrical and Computer Engineering at the University of California San Diego. His research interest lies in machine learning for healthcare. His PhD thesis was selected as a top-5 finalist for the Doctoral Dissertation Award of the American Medical Informatics Association (AMIA). He was recognized as Global Top-100 Chinese Young Scholars in Artificial Intelligence by Baidu, Tencent AI-Lab Faculty Award, Innovator Award by the Pittsburgh Business Times, Amazon AWS Machine Learning Research Award, among others. He serves as an associate editor for the ACM Transactions on Computing for Healthcare, senior area chair for AAAI, area chairs for ICML and NeurIPS, etc.

    Host: Machine Learning Center

    Location: Ronald Tutor Hall of Engineering (RTH) - 211

    Audiences: Everyone Is Invited

    Contact: Thomas Lord Department of Computer Science

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  • DEN@Viterbi - 'Limited Status: How to Get Started' Virtual Info Session

    Thu, May 02, 2024 @ 12:00 PM - 01:00 PM

    DEN@Viterbi, Viterbi School of Engineering Graduate Admission

    Workshops & Infosessions


    Join USC Viterbi for our upcoming Limited Status: How to Get Started Virtual Information Session via WebEx to learn about the Limited Status enrollment option. The Limited Status enrollment option allows individuals with an undergraduate degree in engineering or related field, with a 3.0 GPA or above to take courses before applying for formal admission into a Viterbi graduate degree program. USC Viterbi representatives will provide a step-by-step guide for how to get started as a Limited Status student and enroll in courses online via DEN@Viterbi as early as the Summer 2024 semester. 

    WebCast Link: https://uscviterbi.webex.com/weblink/register/r80d33c78db1dcbf7f1baca0b7fd56d3b

    Audiences: Everyone Is Invited

    Contact: Corporate & Professional Programs

    Event Link: https://uscviterbi.webex.com/weblink/register/r80d33c78db1dcbf7f1baca0b7fd56d3b

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