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Events for April 22, 2024

  • Repeating EventIncident Investigation/Analysis IIA 24-2

    Mon, Apr 22, 2024 @ 08:00 AM - 04:00 PM

    Aviation Safety and Security Program

    University Calendar


    This course is designed for managers and supervisors who may be required to investigate, implement or review safety findings and recommendations resulting from aviation incidents. The course presents the principles of Management, Investigation and Analysis. It will explain how incidents are discovered, investigated, and reported in writing. The student will learn the techniques of data collection and analysis.

    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=24AIIA2

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  • Repeating EventHuman Factors in Aviation Maintenance

    Mon, Apr 22, 2024 @ 08:00 AM - 04:00 PM

    Aviation Safety and Security Program

    University Calendar


    This course is designed to provide knowledge and understanding of human factors in the realm of aviation safety with a focus on the role of the maintainer. It presents human factors issues as conditions/hazards that must be managed. Specific issues such as fatigue management, deviations from approved procedures, situation awareness, and the Dirty Dozen are presented. Data collection methodologies such as MEDA and LOSA are examined as viable safety information methods and hazard identification tools in an organization’s SMS. This course satisfies the Human Factors Course requirement for the USC Safety & Security Certificate.

    Location: Century Boulevard Building (CBB) - 960

    Audiences: Everyone Is Invited

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    Contact: Daniel Scalese

    Event Link: https://avsafe.usc.edu/wconnect/CourseStatus.awp?&course=24AHFMX2

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  • Repeating EventEiS Communications Hub Drop-In Hours

    Mon, Apr 22, 2024 @ 10:00 AM - 01:00 PM

    Viterbi School of Engineering Student Affairs

    Workshops & Infosessions


    Viterbi Ph.D. students are invited to stop by the EiS Communications Hub for one-on-one instruction for their academic and professional communications tasks. All instruction is provided by Viterbi faculty at the Engineering in Society Program.

    Location: Ronald Tutor Hall of Engineering (RTH) - 222A

    Audiences: Viterbi Ph.D. Students

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    Contact: Helen Choi

    Event Link: https://sites.google.com/usc.edu/eishub/home?authuser=0

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  • Repeating EventEiS Communications Hub Drop-In Hours

    Mon, Apr 22, 2024 @ 10:00 AM - 01:00 PM

    Engineering in Society Program

    Student Activity


    Drop-in hours for writing and speaking support for Viterbi Ph.D. students

    Location: Ronald Tutor Hall of Engineering (RTH) - 222

    Audiences: Everyone Is Invited

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    Contact: Helen Choi

    Event Link: https://sites.google.com/usc.edu/eishub/home

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  • PhD Thesis Proposal - Qinyuan Ye

    Mon, Apr 22, 2024 @ 10:00 AM - 11:30 AM

    Thomas Lord Department of Computer Science

    University Calendar


    Title: Cross-Task Generalization Abilities of Large Language Models
     
    Committee Members: Xiang Ren (Chair), Robin Jia, Swabha Swayamdipta, Jesse Thomason, Morteza Dehghani
     
    Date & Time: Monday, April 22, 10am-11:30am\
    Location: SAL 213
     
    Abstract: Humans can learn a new language task efficiently with only a few examples, by leveraging their knowledge and experience obtained when learning prior tasks. Enabling similar cross-task generalization abilities in NLP systems is fundamental for achieving the goal of general intelligence and enabling broader and more scalable adoption of language technology in future applications. In this thesis proposal, I will present my work on (1) benchmarking cross-task generalization abilities with diverse NLP tasks; (2) developing new model architecture for improving cross-task generalization abilities; (3) analyzing and predicting the generalization landscape of current state-of-the-art large language models. Additionally, I will outline future research directions, along with preliminary thoughts on addressing them.
     
    Zoom Link: https://usc.zoom.us/j/93269270403?pwd=NVNmN085bm5SWXNnNGErcXczeVkxdz09

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

    Audiences: Everyone Is Invited

    Contact: Qinyuan Ye

    Event Link: https://usc.zoom.us/j/93269270403?pwd=NVNmN085bm5SWXNnNGErcXczeVkxdz09

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  • PhD Defense- Tiancheng Jin

    Mon, Apr 22, 2024 @ 04:00 PM - 05:30 PM

    Thomas Lord Department of Computer Science

    Student Activity


    PhD Defense- Tiancheng Jin
    Title: Robust and Adaptive Online Reinforcement Learning 
    Committee: Haipeng Luo (Chair), Rahul Jain, Vatsal Sharron
     
    Abstract: Reinforcement learning (RL) is a machine learning (ML) technique on learning to make optimal sequential decisions via interactions with an environment. In recent years, RL achieved great success in many artificial intelligence tasks, and has been widely regarded as one of the keys towards Artificial General Intelligence (AGI). However, most RL models are trained on simulators, and suffer from the reality gap: a mismatch between simulated and real-world performance. Moreover, recent work has shown that RL models are especially vulnerable to adversarial attacks. This motivates the research on improving the robustness of RL, that is, the ability of ensuring worst-case guarantees.

    On the other hand, it is not favorable to be too conservative/pessimistic and sacrifice too much performance while the environment is not difficult to deal with.In other words, adaptivity --- the capability of automatically adapting to the maliciousness of the environment, is especially desirable to RL algorithms: they should not only target worst-case guarantee, but also pursue instance optimality and achieve better performance against benign environments.
    In this thesis, we focus on designing practical, robust and adaptive reinforcement algorithms.

    Specifically, we take inspiration from the online learning literature, and consider interacting with a sequence of Markov Decision Processes (MDPs), which captures the nature of changing environment. We hope that the techniques and insight developed in this thesis could shed light on improving existing deep RL algorithms for future applications.

    Location: Kaprielian Hall (KAP) - 141

    Audiences: Everyone Is Invited

    Contact: Tiancheng Jin

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