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Events for March 03, 2021

  • CS Colloquium: Zhuoran Yang (Princeton University) - Demystifying (Deep) Reinforcement Learning: The Pessimist, The Optimist, and Their Provable Efficiency

    Wed, Mar 03, 2021 @ 09:00 AM - 10:00 AM

    Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Zhuoran Yang, Princeton University

    Talk Title: Demystifying (Deep) Reinforcement Learning: The Pessimist, The Optimist, and Their Provable Efficiency

    Series: CS Colloquium

    Abstract: Coupled with powerful function approximators such as deep neural networks, reinforcement learning (RL) achieves tremendous empirical successes. However, its theoretical understandings lag behind. In particular, it remains unclear how to provably attain the optimal policy with a finite regret or sample complexity. In this talk, we will present the two sides of the same coin, which demonstrates an intriguing duality between pessimism and optimism.

    - In the offline setting, we aim to learn the optimal policy based on a dataset collected a priori. Due to a lack of active interactions with the environment, we suffer from the insufficient coverage of the dataset. To maximally exploit the dataset, we propose a pessimistic least-squares value iteration algorithm, which achieves a minimax-optimal sample complexity.

    - In the online setting, we aim to learn the optimal policy by actively interacting with an environment. To strike a balance between exploration and exploitation, we propose an optimistic least-squares value iteration algorithm, which achieves a \sqrt{T} regret in the presence of linear, kernel, and neural function approximators.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.

    Biography: Zhuoran Yang is a final-year Ph.D. student in the Department of Operations Research and Financial Engineering at Princeton University, advised by Professor Jianqing Fan and Professor Han Liu. Before attending Princeton, He obtained a Bachelor of Mathematics degree from Tsinghua University. His research interests lie in the interface between machine learning, statistics, and optimization. The primary goal of his research is to design a new generation of machine learning algorithms for large-scale and multi-agent decision-making problems, with both statistical and computational guarantees. Besides, he is also interested in the application of learning-based decision-making algorithms to real-world problems that arise in robotics, personalized medicine, and computational social science.

    Host: Haipeng Luo

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

  • Spring 2021 Viterbi Industry Networking Event

    Wed, Mar 03, 2021 @ 01:00 PM - 05:00 PM

    Viterbi School of Engineering Career Connections

    University Calendar

    The Virtual Viterbi Industry Networking Events connect students with Viterbi Alumni and industry professionals from across the world in an online networking event.

    The event will have twelve different booths, organized by industry. Move between booths to connect randomly with a Viterbi Alumni or organization rep based on the booth you enter. This approach will help you network with industry professionals and make new connections with people from various backgrounds. There will also be a Viterbi Career Connections booth with advisors to answer your career or event-related questions.

    This event is open to all Viterbi Students including current Bachelors, Masters & Doctoral students.

    Registration coming soon!

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

  • Repeating EventUndergraduate Advisement Drop-in Hours

    Wed, Mar 03, 2021 @ 01:30 PM - 02:30 PM

    Computer Science

    Workshops & Infosessions

    Do you have a quick question? The CS advisement team will be available for drop-in live chat advisement for declared undergraduate students in our four majors during the spring semester on Tuesdays, Wednesdays, and Thursdays from 1:30pm to 2:30pm Pacific Time. Access the live chat on our website at: https://www.cs.usc.edu/chat/

    Location: Online

    Audiences: Undergrad

    View All Dates

    Contact: USC Computer Science

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

    Wed, Mar 03, 2021 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars

    Speaker: Gabor Orosz, Mechanical Engineering, University of Michigan

    Talk Title: Safety Verification and Conflict Analysis for Connected Automated Vehicles

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

    Abstract: We demonstrate how wireless vehicle-to-everything (V2X) communication can be utilized to improve safety and prevent conflicts between road participants in mixed traffic scenarios where connected automated vehicles (CAVs) interact with connected human-driven vehicles (CHVs). The key idea is to find boundaries in state space that allow CAVs to make safe decisions far away from the conflict zone. This way CAVs are able to maintain safety while using mild control actions that benefit both the CAVs as well as the surrounding human-dominated traffic. Requirements for the quality of V2V communications are determined to ensure the performance of the decision making and control algorithms. The results are demonstrated experimentally using real automobiles and class-8 trucks.

    Biography: Gabor Orosz received the M.Sc. degree in Engineering Physics from the Budapest University of Technology, Hungary, in 2002 and the Ph.D. degree in Engineering Mathematics from University of Bristol, UK, in 2006. He held postdoctoral positions at the University of Exeter, UK, and at the University of California, Santa Barbara. In 2010, he joined the University of Michigan, Ann Arbor where he is currently an Associate Professor in Mechanical Engineering and in Civil and Environmental Engineering. During 2017-2018 he was a Visiting Professor in Control and Dynamical Systems at the California Institute of Technology. His research interests include nonlinear dynamics and control, time delay systems, and machine learning with applications to connected and automated vehicles, traffic flow, and biological networks. He served as the Program Chair of the 2015 IFAC Workshop on Time Delay Systems and served as the General Chair of the 2019 IAVSD Workshop on Dynamics of Road Vehicles: Connected and Automated Vehicles. Since 2018 he has been serving as an editor for the journal Transportation Research Part C and since 2021 he has been serving as an editor for the IEEE Transactions on Control Systems Technology.

    Host: Pierluigi Nuzzo, nuzzo@usc.edu

    Webcast: https://usc.zoom.us/webinar/register/WN_Qk4-7AthThudso7LXs2OiA

    Location: Online

    WebCast Link: https://usc.zoom.us/webinar/register/WN_Qk4-7AthThudso7LXs2OiA

    Audiences: Everyone Is Invited

    Contact: Talyia White

  • AME Seminar

    Wed, Mar 03, 2021 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars

    Speaker: Samantha Daly, University of California at Santa Barbara

    Talk Title: Machine Learning for High-Throughput Experiment and Analysis of Processing-Property Relationships

    Abstract: Materials have hierarchical and heterogeneous structures that drive their deformation and failure mechanisms. The relationship between structure and behavior -- such as the impact of the microstructure of a polycrystalline metal on twinning, dislocation slip, grain boundary sliding, and multi-crack systems -- includes complex stochastic and deterministic factors whose interactions are under active debate. In this talk, the application of data-driven approaches to microscale displacement data for the high-throughput segmentation, identification, and analysis of twinning in magnesium (a deformation mechanism that is critical to its ductility and forming) will be discussed. This will include an analysis of deformation twinning over thousands of grains per test, including an analysis of the impact of microstructure on the relative activity of specific twin variants (automatically identified from microscale strain fields) and their evolution under load. The newly developed experimental and analytical approaches are length scale independent and material agnostic, and can be modified to identify a range of deformation and failure mechanisms.

    Biography: Samantha (Sam) Daly is a Professor in the Department of Mechanical Engineering at the University of California at Santa Barbara. She received her Ph.D. from Caltech in 2007 and subsequently joined the University of Michigan, where she was on the faculty until 2016 prior to her move to UCSB. The Daly group investigates the mechanics of materials, with a focus on fatigue, fracture, creep, composites, multi-functional materials, and new experimental and data-driven approaches for the characterization of processing -“ structure -“ property relationships. Her recognitions include the Experimental Mechanics Best Paper of the Year Award, IJSS Best Paper of the Year Award, DOE Early Career Award, NSF CAREER Award, AFOSR-YIP Award, ASME Eshelby Mechanics Award, Journal of Strain Analysis Young Investigator Award, ASME Orr Award, and Caddell Award. She currently serves on the Executive Board of the Society of Experimental Mechanics, and as an Associate Editor of the journals Applied Mechanics Reviews, Experimental Mechanics, and Strain.

    Host: AME Department

    More Info: https://usc.zoom.us/j/92448962089

    Webcast: https://usc.zoom.us/j/92448962089

    Location: Online event

    WebCast Link: https://usc.zoom.us/j/92448962089

    Audiences: Everyone Is Invited

    Contact: Tessa Yao