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Events for March 09, 2022

  • Amazon Student Programs SDE Career Fair Series (Virtual)

    Wed, Mar 09, 2022 @ 10:00 AM - 12:00 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions


    https://app.brazenconnect.com/a/amazon-student-programs/e/xB0Ox
    For people who like to invent, there's no better place to explore opportunities than at Amazon!
    Amazon Student Programs is currently looking for interns and full-time software developers (SDEs) to come help build the future with us in 2022. Join us at the Amazon Student Programs SDE Career Fair to learn more.
    Can't wait to meet you!
    - Amazon Student Programs
    External employer-hosted events and activities are not affiliated with the USC Career Center. They are posted on Viterbi Career Connections because they may be of interest to members of the Viterbi community. Inclusion of any activity does not indicate USC sponsorship or endorsement of that activity or event. It is the participant's responsibility to apply due diligence, exercise caution when participating, and report concerns to vcareers@usc.edu

    Location: RSVP in Viterbi Career Gateway

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

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  • CS Colloquium: Christoforos Mavrogiannis (University of Washington) - Building Robots that Humans Accept

    Wed, Mar 09, 2022 @ 10:00 AM - 11:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Christoforos Mavrogiannis, University of Washington

    Talk Title: Building Robots that Humans Accept

    Series: CS Colloquium

    Abstract: Robotics has transformed sectors like manufacturing and fulfillment which now rely on robots to meet their goals. Conventionally, these robots operate in isolation from humans due to safety and efficiency considerations. Lately, there have been efforts towards bringing robots closer to humans to assist in everyday-life tasks, enhance productivity, and augment human capabilities. Despite these efforts, robotic technology has not reached widespread acceptance outside of factories; robot autonomy is often not robust, producing new problems that outweigh its benefits for users. Inspired by theories of technology acceptance, my research strives to develop highly functional, safe, and comfortable robots that humans accept. In this talk, I argue that the path towards acceptance requires imbuing robots with a deeper understanding of how users perceive and react to them. To motivate this perspective, I will share insights on robot navigation in dynamic environments, a fundamental task with many crucial applications ranging from collaborative manufacturing to warehouse automation and healthcare. I will describe a human-inspired algorithmic framework for crowd navigation, highlighting how mathematical abstractions of multiagent behavior enable safe, efficient, and positively perceived robot motion across a series of extensive empirical studies involving real robots and human subjects. Inspired by field-deployment challenges, I will then present a data-driven framework that enables robots to recover from failure via bystander help without overloading users. I will conclude with future directions on the development of shared and full robot autonomy that explicitly reasons about human perceptions to produce safe, trustworthy, and comfortable robot behavior.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Christoforos Mavrogiannis is a postdoctoral Research Associate in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, working with Prof. Siddhartha Srinivasa. His interests lie at the intersection of robotics, human-robot interaction, and artificial intelligence. His research often draws insights from algebraic topology and dynamical systems, tools from machine learning, planning and control, and inspiration from social sciences. He is a full-stack roboticist, passionate about real-world deployment of robot systems, and extensive benchmarking with users. He has been a best-paper award finalist at the ACM/IEEE International Conference on Human-Robot Interaction (HRI), and selected as a Pioneer at the HRI and RSS conferences. He has also led open-source initiatives (Openbionics, MuSHR), for which he has been a finalist for the Hackaday Prize and a winner of the Robotdalen International Innovation Award. His work has received coverage from many media outlets including Wired, IEEE Spectrum, GeekWire, RoboHub, and the Hellenic Broadcasting Corporation. Christoforos holds M.S. and Ph.D. degrees from Cornell University, and a Diploma in mechanical engineering from the National Technical University of Athens.


    Host: Jesse Thomason

    Location: Ronald Tutor Hall of Engineering (RTH) - 109

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • ECE Seminar: Algebraic Neural Networks: Stability to Deformations

    Wed, Mar 09, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Alejandro Parada-Mayorga, Postdoctoral Researcher, Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia

    Talk Title: Algebraic Neural Networks: Stability to Deformations

    Abstract: Convolutional architectures play a central role on countless scenarios in machine learning, and the numerical evidence that proves the advantages of using them is overwhelming. Theoretical insights have provided solid explanations about why such architectures work well. These analysis apparently different in nature, have been performed considering signals defined on different domains and with different notions of convolution, but with remarkable similarities in the final results, posing then the question of whether there exists an explanation for this at a more structural level. In this talk we provide an affirmative answer to this question with a first principles analysis introducing algebraic neural networks (AlgNNs), which rely on algebraic signal processing and algebraic signal models. In particular, we study the stability properties of algebraic neural networks showing that stability results for traditional CNNs, graph neural networks (GNNs), group neural networks, graphon neural networks, or any formal convolutional architecture, can be derived as particular cases of our results. This shows that stability is a universal property - at an algebraic level - of convolutional architectures, and this also explains why the remarkable similarities we find when analyzing stability for each particular type of architecture.

    Biography: Alejandro Parada-Mayorga (alejopm@seas.upenn.edu) received his B.Sc. and M.Sc. degrees in electrical engineering from Universidad Industrial de Santander, Colombia, in 2009 and 2012, respectively, and his Ph.D. degree in electrical engineering from the University of Delaware, Newark, 2019. Currently, he is a postdoctoral researcher at the University of Pennsylvania, Philadelphia, under the supervision of Prof. Alejandro Ribeiro. His research interests include algebraic signal processing, algebraic neural networks, graph neural networks, graph signal processing, and applications of representation theory of algebras and category theory.

    Host: Dr. Shri Narayanan, shri@ee.usc.edu

    Webcast: https://usc.zoom.us/j/92088625170?pwd=enhYNUpicEYvS0R5SEViVVBobjQ1dz09

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

    WebCast Link: https://usc.zoom.us/j/92088625170?pwd=enhYNUpicEYvS0R5SEViVVBobjQ1dz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • Amazon Student Programs SDE Career Fair (Virtual)

    Wed, Mar 09, 2022 @ 11:00 AM - 12:00 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions


    Amazon Student Programs SDE Career Fair (Virtual)

    Thursday, March 9, 2022 10:00 AM - 12:00 PM PST

    Register here: https://app.brazenconnect.com/a/amazon-student-programs/e/xB0Ox

    For people who like to invent, there's no better place to explore opportunities than at Amazon!

    Amazon Student Programs is currently looking for interns and full-time software developers (SDEs) to come help build the future with us in 2022. Join us at the Amazon Student Programs SDE Career Fair to learn more.
    Can't wait to meet you! - Amazon Student Programs

    External employer-hosted events and activities are not affiliated with the USC Career Center. They are posted on Viterbi Career Connections because they may be of interest to members of the Viterbi community. Inclusion of any activity does not indicate USC sponsorship or endorsement of that activity or event. It is the participants' responsibility to apply due diligence, exercise caution when participating, and report concerns to vcareers@usc.edu

    Location: Virtual

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

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  • Computer Science General Faculty Meeting

    Wed, Mar 09, 2022 @ 12:00 PM - 02:00 PM

    Computer Science

    Receptions & Special Events


    Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.

    Location: TBD

    Audiences: Invited Faculty Only

    Contact: Assistant to CS chair

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  • Robinhood - Resume Workshop (Virtual)

    Wed, Mar 09, 2022 @ 12:00 PM - 01:00 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions


    Robinhood is on a mission to democratize finance for all. In order to make that mission a reality, we are hiring top student talent across the world! We are dedicated to building a company that represents a variety of backgrounds, perspectives, and skills.
    The University Recruiting team at Robinhood is excited to host a virtual Resume Workshop. We'll have a few of our Robinhoodies share their tips and tricks on finding the right role, preparing for interviews, and landing the job! The latter portion of the session will be focused on resume tips from one of our University Recruiters!
    sign up link: https://ripplematch.com/t/79c715c4

    The event will take place on Zoom; you'll be able to chat and ask any questions you have with the speakers and other attendees using Zoom's chat and Q+A features. Please RSVP and you will receive the Zoom link 15 minutes prior to the event. As a perk for attending, we will be raffling off Robinhood water bottles!
    We're looking forward to seeing you then!
    External employer-hosted events and activities are not affiliated with the USC Viterbi Career Connections Office. They are posted on Viterbi Career Connections because they may be of interest to members of the Viterbi community. Inclusion of any activity does not indicate USC sponsorship or endorsement of that activity or event. It is the participant's responsibility to apply due diligence, exercise caution when participating, and report concerns to vcareers@usc.edu

    Location: Virtual. Sign-up Link in the event description.

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

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  • Navigating the Internship/Job Search Workshop (ON-CAMPUS)

    Wed, Mar 09, 2022 @ 01:00 PM - 01:30 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions


    THIS EVENT WILL BE HOSTED IN-PERSON, ON-CAMPUS

    Increase your knowledge on the job/internship search by attending this professional development Q&A moderated by Viterbi Career Connections staff or Viterbi employer partners.

    For more information about workshops, please visit viterbicareers.usc.edu/workshops.

    Attendance is limited to room capacity

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

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

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  • Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series

    Wed, Mar 09, 2022 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Swarat Chaudhuri, Computer Science Department, The University of Texas at Austin

    Talk Title: Neurosymbolic Programming

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

    Abstract: I will speak about Neurosymbolic programming, an emerging research area that bridges the fields of deep learning and program synthesis. Like in classic machine learning, the goal here is to learn functions from data. However, these functions are represented as programs that can use neural modules in addition to symbolic primitives and are induced using a combination of symbolic search and gradient-based optimization. Neurosymbolic programming can offer multiple advantages over end-to-end deep learning. Programs can sometimes naturally represent long-horizon, procedural tasks that are difficult to perform using deep networks. Neurosymbolic representations are also, commonly, easier to interpret and formally verify than neural networks. The restrictions of a programming language can serve as a form of regularization and lead to more generalizable and data-efficient learning. Compositional programming abstractions can also be a natural way of reusing learned modules across learning tasks.

    In the talk, I will illustrate some of the potential benefits of research in this area. I will also categorize the main ways in which symbolic and neural learning techniques come together here. I will conclude with a discussion of the open technical challenges in the field.


    Biography: Swarat Chaudhuri (http://www.cs.utexas.edu/~swarat) is an Associate Professor of Computer Science and the director of the Trishul laboratory at UT Austin. His research lies at the interface of programming languages, logic, and machine learning. Through a synthesis of ideas from these areas, he seeks to develop a new generation of intelligent systems that are designed to be reliable, transparent, secure, and that can solve complex procedural tasks beyond the scope of contemporary AI.

    Host: Pierluigi Nuzzo

    Webcast: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw

    Location: Online

    WebCast Link: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw

    Audiences: Everyone Is Invited

    Contact: Talyia White

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  • AME Seminar

    Wed, Mar 09, 2022 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Talk Title: Data-driven discovery of governing equations with deep learning and sparse identification techniques

    Abstract: Machine learning techniques promise to offer the ultimate form of automation, particularly when applied to computational modeling and simulation. As a consequence, the computational scientist's narrative now revolves around discovering physics directly from data, with as little assumptions about the underlying physical system as possible. I briefly go over the latest attempts to accomplish this goal and focus on my recent work in combining deep learning with sparse identification of differential equations. First, I show how probability distribution function (PDF) equations can be inferred from Monte Carlo simulations for coarse-graining and closure approximations. Second, I present our latest results on discovering dimensionless groups from data, using the Buckingham Pi theorem as a constraint. And third, I go over the deep delay autoencoder algorithm that reconstructs high dimensional models from partial measurements as motivated by Takens' embedding theorem. I finally highlight the limitations of these methods and propose a few directions for future research.

    Biography: Joseph Bakarji is currently a postdoctoral fellow in the department of mechanical engineering at the University of Washington, working with Steven Brunton and Nathan Kutz. He received his PhD in 2020 from Stanford University where he developed multiscale stochastic models for granular materials and data-driven closure models for uncertainty quantification. Joseph received the Henry J. Ramey, Jr. and the Frank G. Miller fellowship awards in 2018 and 2020 respectively. His current research focuses on combining deep learning and sparse identification methods, to discover interpretable physical models in complex systems from data.

    More Info: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Webcast: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Location: James H. Zumberge Hall Of Science (ZHS) - 252

    WebCast Link: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Audiences: Everyone Is Invited

    Contact: Tessa Yao

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