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Events for May 07, 2024

  • Repeating EventAircraft Accident Investigation AAI 24-4

    Tue, May 07, 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.


    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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  • PhD Defense- Yilei Zeng

    Tue, May 07, 2024 @ 10:30 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    Student Activity

    PhD Defense- Yilei Zeng

    Title: Learning Social Sequential Decision-Making in Online Games
    Committee: Emilio Ferrara (chair), Dmitri Williams, Michael Zyda


    A paradigm shift towards human-centered intelligent gaming systems is gradually setting in. This dissertation explores the complexities of social sequential decision-making within online gaming environments and presents comprehensive AI solutions to enhance personalized single and multi-agent experiences. The three core contributions of the dissertation are intricately interrelated, creating a cohesive framework for understanding and improving AI in gaming. I begin by delving into the dynamics of gaming sessions and sequential in-game individual and social decision-making, which establishes a baseline of how decisions evolve, providing the necessary context for the subsequent integration of diverse information sources; two, the integration of heterogeneous information and multi-modal trajectories, which enhances decision-making generation models; and three, the creation of a reinforcement learning with human feedback framework to train gaming AIs that effectively align with human preferences and strategies, which enables the system not only learning but also interacting with humans. Collectively, this dissertation combines innovative data-driven, generative AI, representation learning, and human-AI collaboration solutions to help advance both the fields of computational social science and artificial intelligence applications of gaming.

    Location: Henry Salvatori Computer Science Center (SAL) - 213

    Audiences: Everyone Is Invited

    Contact: Yilei Zeng

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  • Alfred E.Mann Department of Biomedical Engineering - Seminar series

    Tue, May 07, 2024 @ 10:45 AM - 11:45 AM

    Alfred E. Mann Department of Biomedical Engineering

    Conferences, Lectures, & Seminars

    Speaker: Dr. Rong Li, Professor of Mechanobiology Institute, National University of Singapore Department of Cell Biology and Department of Chemical and Biomolecular Engineering, Johns Hopkins University School of Medicine

    Talk Title: Mechanics and stress in cellular development, adaptation, and aging

    Abstract: Mechanical processes are central to diverse cellular functions but can also be sources of cellular stress leading to aging phenotypes. My lab currently investigates three problems related to cell mechanics and stress: 1) how intracellular fluid dynamics coupled with cytoskeletal forces drive early mammalian development and reproductive aging; 2) how stress-induced protein aggregation and subsequent disaggregation are orchestrated by and affect organelles such as mitochondria and ER; and 3) the interplay between biophysical stress and chromosome instability and its contribution to cellular adaptation and cancer evolution. I will present a combination of recent findings in the first two areas of our research. 

    Biography: Professor Rong Li came from Johns Hopkins University where she served as the Director of the Centre for Cell Dynamics in the Johns Hopkins School of Medicine. She was recruited to NUS in 2019 as the second Director of Mechanobiology Institute (MBI). Professor Li is a globally respected leader in the study of cellular dynamics and mechanics. Her interdisciplinary research integrates genetics, quantitative imaging, biophysical measurements, mathematical modelling, genomics and proteomics — to understand how eukaryotic cells transmit their genomes, adapt to the environment, and establish distinct organisation to perform specialised functions. The diverse projects in Professor Rong Li’s lab contribute to two main research thrusts: cell and tissue aging; cellular and organismal adaptation. The study on aging focuses on understanding dynamic changes of crucial cellular components during the aging process and how these changes alter the mechanical functions of cells and tissues. The insights gained will be applied to the development of new methods for prolonging healthy aging and the repair and regeneration of deteriorated functions. The study of adaptation aims to understand the dynamics of genetic and epigenetic determinants of cells and tissues under acute or chronic stress which lead to adaptive behaviors ultimately beneficial or detrimental to the fitness of the organism. A potential application of the discoveries in this area is the prevention of cancer associated with chronic inflammatory diseases. 

    Location: Corwin D. Denney Research Center (DRB) - 145

    Audiences: Everyone Is Invited

    Contact: Carla Stanard

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  • PhD Dissertation Defense - I-Hung Hsu

    Tue, May 07, 2024 @ 02:10 PM - 04:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    Title: Towards Generalized Event Understanding in Text via Generative Models
    Committee Members: Dr. Prem Natarajan (Chair), Dr. Nanyun Peng (Co-Chair), Dr. Dan O'Leary, Dr. Emilio Ferrara
    Date and Time:  May 7th, 2024 - 2:10p - 4:00p
    Abstract: Human languages in the world, such as news or narratives, are structured around events. Focusing on these events allows Natural Language Processing (NLP) systems to better understand plots, infer motivations, consequences, and the dynamics of situations. Despite the rapidly evolving landscape of NLP technology, comprehending complex events, particularly those rarely encountered in training such as in niche domains or low-resource languages, remains a formidable challenge. This thesis explores methods to enhance NLP model generalizability for better adaptability to unfamiliar events and languages unseen during training.
    My approach includes two main strategies: (1) Model Perspective: I propose a novel generation-based event extraction framework, largely different from typical solutions that make predictions by learning to classify input tokens. This new framework utilizes indirect supervision from natural language generation, leveraging large-scale unsupervised data without requiring additional training modules dependent on limited event-specific data. Hence, it facilitates the models’ ability on understanding general event concepts. I further explore advanced methods to extend this framework for cross-lingual adaptation and to utilize cross-domain robust resources effectively. (2) Data Perspective: I develop techniques to generate pseudo-training data broaden the training scope for event understanding models. This includes translating structured event labels into other languages with higher accuracy and fidelity, and synthesizing novel events for the existing knowledge base.
    Overall, my work introduces a novel learning platform to the NLP community, emphasizing an innovative modeling paradigm and comprehensive data preparation to foster more generalized event understanding models.

    Location: Information Science Institute (ISI) - 727

    Audiences: Everyone Is Invited

    Contact: I-Hung Hsu

    Event Link: https:/usc.zoom.us/j/95785927723?pwd=dFlGbEcwbXlGalJ6OVk3YW41RDMrdz09

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