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Events for September 26, 2023

  • Viterbi Career & Internship Expo

    Tue, Sep 26, 2023 @ 10:00 AM - 04:00 PM

    Viterbi School of Engineering Career Connections

    Receptions & Special Events

    Viterbi Career Connections is excited to announce the Fall 2023 Career & Internship Expo will be hosted September 25th-28th, 2023.
    70% of employers recruit in the fall semester for their summer roles. Meet recruiters actively looking for full-time roles, internships, and co-ops.

    Join additional activities such as Employer Info Sessions and Company Demos to learn more about an organization and have your questions answered. You can share your resume at any Expo activity.
    The Viterbi Career & Internship Expo is free and open to all students in the USC Viterbi School of Engineering.

    For more information and to register visit: https://viterbicareers.usc.edu/careerexpo/

    Location: Michelson Center for Convergent Bioscience (MCB) -

    Audiences: All Viterbi

    Contact: RTH 218 Viterbi Career Connections

    Event Link: https://viterbicareers.usc.edu/careerexpo/

  • ECE Seminar: Verifiable Control of Learning-Enabled Autonomous Systems

    Tue, Sep 26, 2023 @ 12:00 PM - 01:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars

    Speaker: Dr. Lars Lindemann, Assistant Professor, USC Thomas Lord Department of Computer Science

    Talk Title: Verifiable Control of Learning-Enabled Autonomous Systems

    Abstract: Autonomous systems research shows great promise to enable many future technologies such as autonomous driving, intelligent transportation, and robotics. Accelerated by the computational advances in machine learning and AI, there has been tremendous success in the development of learning-enabled autonomous systems over the past years. At the same time, however, new fundamental questions arise regarding the safety and reliability of these increasingly complex systems that operate in dynamic and unknown environments. In this talk, I will provide new insights and discuss exciting opportunities to address these challenges.

    In the first part of the talk, we focus on reasoning about uncertainty of learning-enabled components in an autonomy stack. Existing model-based techniques are usually too conservative or do not scale. I will instead advocate for conformal prediction as an accurate and computationally lightweight alternative. We will first use conformal prediction to design predictive runtime verification algorithms that quantify uncertainty of learning-enabled systems. These algorithms can effectively compute the probability of a task violation during the execution of the system. I will then show how to design probabilistically safe motion planning algorithms in dynamic environments using such uncertainty quantification. While existing data-driven approaches quantify uncertainty heuristically, we quantify uncertainty in a distribution-free manner. Using ideas from adaptive conformal prediction, we can even deal with distribution shifts, i.e., when test and training distributions are different. We illustrate the method on a self-driving car and a drone that avoids a flying frisbee. In the second part of the talk, I present an optimization framework to learn safe control laws from expert demonstrations. In most safety-critical systems, expert demonstrations in the form of system trajectories that showcase safe system behavior are readily available or can easily be collected. I will propose a constrained optimization problem with constraints on the expert demonstrations and the system model to learn control barrier functions for safe control. Formal guarantees are provided in terms of the density of the data and the smoothness of the system model. We then discuss how we can account for model uncertainty and hybrid system models, and how we can learn safe control laws from high-dimensional sensor data. Two case studies on a self-driving car and a bipedal robot illustrate the method.

    Biography: Lars Lindemann is currently an Assistant Professor at the Department of Computer Science at the University of Southern California where he leads the Safe Autonomy and Intelligent Distributed Systems (SAIDS) lab. Prior to joining USC, he was a Postdoctoral Fellow in the Department of Electrical and Systems Engineering at the University of Pennsylvania from 2020 and 2022. He received the Ph.D. degree in Electrical Engineering from KTH Royal Institute of Technology in 2020. Prior to that, he received the M.Sc. degree in Systems, Control and Robotics from KTH in 2016 and two B.Sc. degrees in Electrical and Information Engineering and in Engineering Management from the Christian-Albrecht University of Kiel in 2014. His current research interests include systems and control theory, formal methods, and autonomous systems. Lars received the Outstanding Student Paper Award at the 58th IEEE Conference on Decision and Control and the Student Best Paper Award (as a co-author) at the 60th IEEE Conference on Decision and Control. He was a finalist for the Best Paper Award at the 2022 Conference on Hybrid Systems: Computation and Control and for the Best Student Paper Award at the 2018 American Control Conference.

    Host: Dr. Rahul Jain, rahul.jain@usc.edu

    Webcast: Webcast: https://usc.zoom.us/j/99747592573?pwd=YmNGYkJCK1V5SEQwcU1jVllwQVFwZz09

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    WebCast Link: Webcast: https://usc.zoom.us/j/99747592573?pwd=YmNGYkJCK1V5SEQwcU1jVllwQVFwZz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

  • PhD Dissertation Defense - Setareh Nasihati Gilani

    Tue, Sep 26, 2023 @ 03:00 PM - 05:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Dissertation Defense - Setareh Nasihati Gilani

    Committee Members: David Traum (Chair), Maja Mataric, Peter Kim, Kallirroi Georgila, Mohammad Soleymani

    Title: Understanding and Generating Multimodal Feedback in Human Machine Story Telling

    Abstract: People use feedback, verbal or nonverbal, from their interlocutors to guide their own behavior and alter the flow of conversation. In this thesis, we focus on human machine interactions that involve storytelling and investigate the role of understanding and providing feedback from the machines perspective. We explored the characteristics of stories that machines should use to increase rapport. We developed machine storytellers and listeners that can provide feedback and adapt their stories based on perceived multimodal feedback from their users. Finally, we investigated how machines can use real time predictions based on user feedback to further adapt the dialogue management policies of the system for better overall performance.

    Audiences: Everyone Is Invited

    Contact: Melissa Ochoa

    Event Link: https://usc.zoom.us/j/93206733633

  • Epstein Institute, ISE 651 Seminar Class

    Tue, Sep 26, 2023 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars

    Speaker: Dr. Robert Schuler, Senior Computer Scientist, Research Lead, USC Viterbi Information Sciences Institute (ISI)

    Talk Title: Schema Evolution for Scientific Asset Management

    Host: Prof. Carl Kesselman

    More Information: September 26, 2023.pdf

    Location: Social Sciences Building (SOS) - SOS Building, B2

    Audiences: Everyone Is Invited

    Contact: Grace Owh

  • PhD Thesis Proposal - Taoan Huang

    Tue, Sep 26, 2023 @ 03:30 PM - 05:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Thesis Proposal - Taoan Huang

    Committee Members: Sven Koenig (co chair), Bistra Dilkina (co chair), Jyotirmoy Deshmukh, Stefanos Nikolaidis, John Carlsson, Peter Stuckey from Monash University

    Title: Improving Decision Makings in Search Algorithms with Machine Learning for Combinatorial Optimizations

    Abstract: Designing algorithms for combinatorial optimization problems (COP) are important and challenging tasks since it concerns a wide range of real world problems, such as vehicle routing, path planning and resource allocation problems. Most COPs are NP hard to solve and many research algorithms have been developed for them in the past few decades. Decision makings such as partitioning or pruning the search space and prioritizing exploration in the search space, are crucial to the efficiency and effectiveness of the search algorithms. Many of those heavily rely on domain expertise and human designed strategies.

    In this thesis, we hypothesize that one can leverage machine learning to improve human designed decision making strategies in different categories of search algorithms for combinatorial optimization problems. We validate the hypothesis on the problems of multiagent path finding and solving mixed integer linear programs, introducing different machine learning techniques to advance a few state of the art optimal and heuristic search algorithms for the two problems.

    Audiences: Everyone Is Invited

    Contact: Melissa Ochoa

    Event Link: https://usc.zoom.us/j/92825821724?pwd=a2RFY0x0QzV0S3hqYmkxakJvQUpYZz09

  • DEN@Viterbi - Online Graduate Engineering Virtual Information Session

    Tue, Sep 26, 2023 @ 05:00 PM - 06:00 PM

    DEN@Viterbi, Viterbi School of Engineering Graduate Admission

    Workshops & Infosessions

    Join USC Viterbi School of Engineering for a virtual information session via WebEx, providing an introduction to DEN@Viterbi, our top-ranked online delivery system. Discover the 40+ graduate engineering and computer science programs available entirely online.

    Attendees will have the opportunity to connect directly with USC Viterbi representatives during the session to discuss the admission process, program details, and the benefits of online delivery.

    Register Today!

    WebCast Link: https://uscviterbi.webex.com/weblink/register/red26cec1e1940dd049913913b038e92e

    Audiences: Everyone Is Invited

    Contact: Corporate & Professional Programs

    Event Link: https://uscviterbi.webex.com/weblink/register/red26cec1e1940dd049913913b038e92e