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Events for March 06, 2019

  • Repeating EventMeet USC: Admission Presentation, Campus Tour, and Engineering Talk

    Wed, Mar 06, 2019

    Viterbi School of Engineering Undergraduate Admission

    Workshops & Infosessions


    This half day program is designed for prospective freshmen (HS juniors and younger) and family members. Meet USC includes an information session on the University and the Admission process, a student led walking tour of campus, and a meeting with us in the Viterbi School. During the engineering session we will discuss the curriculum, research opportunities, hands-on projects, entrepreneurial support programs, and other aspects of the engineering school. Meet USC is designed to answer all of your questions about USC, the application process, and financial aid.

    Reservations are required for Meet USC. This program occurs twice, once at 8:30 a.m. and again at 12:30 p.m.

    Please make sure to check availability and register online for the session you wish to attend. Also, remember to list an Engineering major as your "intended major" on the webform!

    RSVP

    Location: Ronald Tutor Campus Center (TCC) - USC Admission Office

    Audiences: Everyone Is Invited

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    Posted By: Viterbi Admission

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

    Wed, Mar 06, 2019 @ 03:00 AM - 04:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dorsa Sadigh, Computer Science and Electrical Engineering at Stanford University

    Talk Title: Interactive Autonomy: A human-centered approach to learning and control

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

    Abstract: Today's society is rapidly advancing towards robotics systems that interact and collaborate with humans, e.g., semi-autonomous vehicles interacting with drivers and pedestrians, medical robots used in collaboration with doctors, or service robots interacting with their users in smart homes. Formalizing interaction is a crucial component in seamless collaboration and coordination between humans and today's robotics systems. In this talk, I will first discuss our recent results on efficient and active learning of predictive models of humans' preferences by eliciting comparisons from humans. I will then formalize interactive autonomy, and our approach in design of learning and control algorithms that influence humans in interactive settings. I will further analyze the global implications of human-robot interaction and its societal impacts in the setting of autonomous driving.

    Biography: Dorsa Sadigh is an assistant professor in Computer Science and Electrical Engineering at Stanford University. Her research interests lie in the intersection of robotics, learning and control theory, and algorithmic human-robot interaction. Specifically, she works on developing efficient algorithms for autonomous systems that safely and reliably interact with people. Dorsa has received her doctoral degree in Electrical Engineering and Computer Sciences (EECS) at UC Berkeley in 2017, and has received her bachelor's degree in EECS at UC Berkeley in 2012. She is awarded the Amazon Faculty Research Award, the NSF and NDSEG graduate research fellowships as well as the Leon O. Chua departmental award departmental award.

    Host: Paul Bogdan

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132

    Audiences: Everyone Is Invited

    Posted By: Talyia White

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  • CS Colloquium: Behnam Neyshabur (New York University) - Why Do Neural Networks Learn?

    Wed, Mar 06, 2019 @ 09:00 AM - 10:00 AM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Behnam Neyshabur, New York University

    Talk Title: Why Do Neural Networks Learn?

    Series: CS Colloquium

    Abstract: Neural networks used in practice have millions of parameters and yet they generalize well even when they are trained on small datasets. While there exists networks with zero training error and a large test error, the optimization algorithms used in practice magically find the networks that generalizes well to test data. How can we characterize such networks? What are the properties of networks that generalize well? How do these properties ensure generalization?
    In this talk, we will develop techniques to understand generalization in neural networks. Towards the end, I will show how this understanding can help us design architectures and optimization algorithms with better generalization performance.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.

    Biography: Behnam Neyshabur is a postdoctoral researcher in Yann LeCun's group at New York University. Before that, he was a member of Theoretical Machine Learning program lead by Sanjeev Arora at the Institute for Advanced Study (IAS) in Princeton. In summer 2017, he received a PhD in computer science at TTI-Chicago where Nati Srebro was his advisor. He is interested in machine learning and optimization and his primary research is on optimization and generalization in deep learning.

    Host: Haipeng Luo

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

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • CS Colloquium:Sida Wang (Princeton University) - Learning Adaptive Language Interfaces Through Interaction

    Wed, Mar 06, 2019 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Sida Wang, Princeton University

    Talk Title: Learning Adaptive Language Interfaces Through Interaction

    Series: CS Colloquium

    Abstract: The interactivity and adaptivity of natural language have the potential to allow people to better communicate with increasingly AI-driven computer systems. However, current natural language interfaces are mostly static and fall short of their potential. In this talk, I will cover two systems that can quickly learn from interactions, adapt to users, and simultaneously give feedback so that users can adapt to the system. The first system learns from scratch from users in real time. The second starts with a programming language and then learns to naturalize the programming language by interacting with users. Finally, I will present how these ideas can be combined to build a natural language interface for data visualization and discuss my work on modeling interactive language learning more rigorously.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.

    Biography: Sida Wang is a research instructor at Princeton University and Institute for Advanced Study working in the areas of natural language processing and machine learning. He holds a Ph.D. in computer science from Stanford University and a B.A.Sc. from the University of Toronto. He received an outstanding paper award at ACL 2016 and the NSERC Postgraduate Scholarship.

    Host: Joseph Lim

    Location: Ronald Tutor Hall of Engineering (RTH) - 115

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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

    Wed, Mar 06, 2019 @ 11:30 AM - 12:30 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Vijay G. Subramanian, Electrical Engineering and Computer Science, University of Michigan

    Talk Title: One If By Land and Two If By Sea: A Glimpse into the Value of Information in Strategic Interactions

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

    Abstract: This work studies sequential social learning (also known as Bayesian observational learning), and how private communication can enable agents to avoid herding to the wrong action/state. Starting from the seminal BHW (Bikhchandani, Hirshleifer, and Welch, 1992) model where asymptotic learning does not occur, we allow agents to ask private and finite questions to a bounded subset of their predecessors. While retaining the publicly observed history of the agents and their Bayes rationality from the BHW model, we further assume that both the ability to ask questions and the questions themselves are common knowledge. Then interpreting asking questions as partitioning information sets, we study whether asymptotic learning can be achieved with finite capacity questions. Restricting our attention to the network where every agent is only allowed to query her immediate predecessor, an explicit construction shows that a 1-bit question from each agent is enough to enable asymptotic learning.

    This is joint work with Shih-Tang Su and Grant Schoenebeck at the University of Michigan. Details of the work can be found at https://arxiv.org/abs/1811.00226


    Biography: I am an Associate Professor in the EECS Department at the University of Michigan. My main research interests are in stochastic modeling, communications, information theory, and applied mathematics. A large portion of my past work has been on probabilistic analysis of communication networks, especially analysis of scheduling and routing algorithms. In the past, I have also done some work with applications in immunology and coding of stochastic processes. My current research interests are on game-theoretic and economic modeling of socio-technological systems and networks, and the analysis of associated stochastic processes.

    Host: Ashutosh Nayyar

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132

    Audiences: Everyone Is Invited

    Posted By: Talyia White

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  • Astani Civil and Environmental Engineering Seminar

    Wed, Mar 06, 2019 @ 11:30 AM - 12:30 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Lorenzo Valdevit, Ph.D., University of California, Irvine

    Talk Title: Deformation and Damage Mechanisms in Ceramic Nano-Architected Metamaterials

    Abstract: See attached

    Host: Dr. Qiming Wang

    More Information: Seminar Annoucement_Lorenzo Valdevit.docx

    Location: Ray R. Irani Hall (RRI) - 101

    Audiences: Everyone Is Invited

    Posted By: Evangeline Reyes

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  • Internship/Job Search Open Forum

    Wed, Mar 06, 2019 @ 01:00 PM - 02:00 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions


    Increase your career and internship 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 Labs & Open Forums, please visit viterbicareers.usc.edu/workshops.

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

    Audiences: All Viterbi Students

    Posted By: RTH 218 Viterbi Career Connections

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  • The Role of Advanced Experimental and Numerical Simulations in the Management of Deteriorated Infrastructure

    Wed, Mar 06, 2019 @ 03:00 PM - 04:00 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Hussam Mahmoud, PhD, Colorado State University

    Talk Title: The Role of Advanced Experimental and Numerical Simulations in the Management of Deteriorated Infrastructure

    Host: Civil and Environmental Engineering

    Location: Ray R. Irani Hall (RRI) - 101

    Audiences: Everyone Is Invited

    Posted By: Salina Palacios

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  • AME Seminar

    Wed, Mar 06, 2019 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Krishna Garikipati, University of Michigan

    Talk Title: Mechano-Chemical Phase Transformations: Computational Framework, Machine Learning Studies and Graph Theoretic Analysis

    Abstract: Phase transformations in a wide range of materials-”for energy, electronics, structural and other applications-”are driven by mechanics in interaction with chemistry. We have developed a general theoretical and computational framework for large scale simulations of these mechano-chemical phenomena. I will begin by presenting our recent work in this sphere, while highlighting some of its more insightful results. In addition to being a platform for investigating mechanically driven phenomena in materials physics, this work is a foundation to explore the potential of recent advances in data-driven modeling. Of interest to us are machine learning advances that may enhance our approaches to solve computational materials physics problems. I will outline the first of several recent studies that we have launched in this spirit. Such combinations of classical high-performance scientific computing and modern data-driven modeling now allow us to access large numbers of states of physical systems. They also motivate the study of mathematical structures for representation, exploration and analysis of systems by using these collections of states. With this perspective, I will offer a view of graph theory that places it in nearly perfect correspondence with properties of stationary and dynamical systems. This has opened up new insights to our earlier, large-scale computational investigations of mechano-chemically phase transforming materials systems. This treatment has potential for eventual decision-making for physical systems that builds on high-fidelity computations.

    Krishna Garikipati is a computational scientist whose work draws upon nonlinear physics, applied mathematics and numerical methods. A very recent interest of his is the development of methods for data-driven computational science. He has worked for quite a few years in mathematical biology, biophysics and materials physics. Some specific problems he has been thinking about recently are: (1) mathematical models of patterning and morphogenesis in developmental biology, (2) mathematical and physical modeling of tumor growth, and (3) mechano-chemically driven phenomena in materials, such as phase transformations and stress-influenced mass transport.

    Host: AME Department

    More Info: https://ame.usc.edu/seminars/

    Location: Seaver Science Library (SSL) - 150

    Audiences: Everyone Is Invited

    Posted By: Tessa Yao

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  • CAIS Seminar: Lindsay Young (University of Chicago) - Social Network Analysis and Artificial Intelligence: Methodological Partners in the Study of HIV Prevention and Risk Online

    Wed, Mar 06, 2019 @ 04:00 PM - 05:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Dr. Lindsay Young, University of Chicago

    Talk Title: Social Network Analysis and Artificial Intelligence: Methodological Partners in the Study of HIV Prevention and Risk Online

    Series: USC Center for Artificial Intelligence in Society (CAIS) Seminar Series

    Abstract: As transmitters of information and progenitors of behavioral norms, social networks are critical mechanisms of HIV prevention and risk in impacted populations like men who have sex with men (MSM), people who inject drugs (PWID), and homeless youth. Today, widespread use of online social networking technologies (e.g., Facebook, Instagram, Twitter) yield unprecedented amounts of relational and communication data far richer than anything previously collected in offline (physical) network settings. However, parsing these complex data into tractable insights and solutions requires an innovative and flexible computational toolkit that extends beyond traditional approaches. In this talk, Dr. Young will discuss her ongoing efforts to unpack how HIV prevention and risk manifest in the Facebook networks of young MSM using a hybrid of computational methods that include social and semantic network analysis and machine learning approaches for textual analysis and predictive modeling. She will conclude with a discussion of the practical implications of this work and outstanding challenges that require further exploration.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: Dr. Lindsay Young is a NIH Pathway to Independence Award Postdoctoral Fellow at the University of Chicago Department of Medicine and Chicago Center for HIV Elimination (CCHE). Trained as a social scientist and network methodologist, she now applies those perspectives to understand the social and communicative contexts of HIV risk and prevention among young sexual minorities and other vulnerable populations. She is particularly interested in how online social network data can be leveraged for behavioral research and interventions.


    Host: Milind Tambe

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

    Audiences: Everyone Is Invited

    Posted By: Computer Science Department

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  • Trojan Talk with Disney Imagineering

    Wed, Mar 06, 2019 @ 05:30 PM - 07:00 PM

    Viterbi School of Engineering Career Connections

    University Calendar


    Hear from Viterbi ISE alumnus Justin Newton about his career path, how he landed a job working at the ultimate dream company, and what it takes to be a Disney Imagineer!

    Justin Newton is an executive at Walt Disney Imagineering. He is responsible for process improvement, risk management, talent development and supporting Walt Disney Imagineering projects.

    Location: Seeley G. Mudd Building (SGM) - 101

    Audiences: Everyone Is Invited

    Posted By: RTH 218 Viterbi Career Connections

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  • Intro to Deep Learning with AAAI and GRIDS

    Wed, Mar 06, 2019 @ 07:30 PM - 09:00 PM

    Viterbi School of Engineering Student Organizations

    University Calendar


    Join AAAI as we collaborate with USC's GRIDS (Graduates Rising in Informatics and Data Science) to bring you a presentation on Intro to Deep Learning.

    Deep learning has been the focus of much attention (and hype) in recent years: it has not only revolutionized consumer technologies ranging from image understanding to language processing to speech recognition, but has also found its way into domains like genomics, robotics, and particle physics. In this talk, we take a grounded look at what deep learning really is, what it is good for, how it is being used all around you, what you need to know to get started with it, and how you can do so on a budget.

    RSVP Here

    Location: Seeley G. Mudd Building (SGM) - 124

    Audiences: Everyone Is Invited

    Posted By: USC AAAI

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