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Events for October 31, 2019

  • Theory Lunch

    Thu, Oct 31, 2019 @ 12:15 PM - 02:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Federico Echenique, Professor of Economics at CalTech

    Talk Title: The Edgeworth Conjecture with Small Coalitions and Approximate Equilibria in Large Economies

    Abstract: A talk about the paper Federico Echenique worked on with Siddharth Barman entitled "The Edgeworth Conjecture with Small Coalitions and Approximate Equilibria in Large Economies." The paper shows that deciding if an allocation is approximately Walrasian can be done in polynomial time, even if finding the market equilibrium in intractable.
    It will be a fun time, so make sure not to miss out!

    Host: Shaddin Dughmi

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

    Audiences: Everyone Is Invited

    Contact: Cherie Carter

  • Class to Career: Navigating the Process

    Thu, Oct 31, 2019 @ 01:00 PM - 01:50 PM

    USC Viterbi School of Engineering, Viterbi School of Engineering Student Affairs

    Conferences, Lectures, & Seminars

    Abstract: Learn how USC Viterbi School of Engineering Career Connections programs, resources and events help students go from class to career. Whether this is your student's first year or last, professional development is key to career advancement. Discover how to help your student navigate the process.

    Host: USC Viterbi Undergraduate Programs and Women in Engineering

    Audiences: Everyone Is Invited

    Contact: Amanda McCraven

  • CS Colloquium: Masahiro Ono (NASA JPL) - Robots in Space: How AI and Machine Learning are Revolutionizing Space Exploration

    Thu, Oct 31, 2019 @ 02:00 PM - 03:20 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Masahiro Ono, NASA JPL

    Talk Title: Robots in Space: How AI and Machine Learning are Revolutionizing Space Exploration

    Series: Computer Science Colloquium

    Abstract: On the third planet of the Solar System, there is an ongoing revolution caused by a group of technologies collectively called as "AI," including, but not limited to, machine learning, optimal decision making, situation awareness, and autonomous navigation. The same revolution is taking off elsewhere in the Solar System, triggered by an advent of HPSC (high-performance spacecraft computing). This talk introduces ongoing research efforts to adapt and enhance latest AI technologies for future exploration missions to Mars, Europa, and Enceladus, such as resource-aware autonomous rover driving, on-board science interpretation, and autonomous descent into Enceladus's vents, which are believed to be connected to the subsurface ocean that may harbor extraterrestrial life.

    If you would like to take a peek of the talk, take a look at the following YouTube movies:

    This lecture satisfies requirements for CSCI 591: Research Colloquium. *Note: Rescheduled to 10/31/19, 2:00-3:20PM*

    Biography: Dr. Masahiro (Hiro) Ono is a Research Technologist at NASA Jet Propulsion Laboratory, California Institute of Technology. His broad interest is centered around the application of robotic autonomy to space exploration, with an emphasis on machine learning applications to perception, data interpretation, and decision making. Before joining JPL in 2013, he was an assistant professor at Keio University in Japan. He graduated from MIT with PhD in Aeronautics and Astronautics in 2012. Since 2017 he is the PI of a JPL-funded Strategic Research and Development task on the machine learning-based analytics for automated rover systems (MAARS). From 2015 he has led the development of machine learning- based Martian terrain classifier, which is used by MSL and Mars 2020 Rover missions. He was also the PI of a JPL-Caltech joint project on imitation learning-based planning from 2016 to 2018. He was awarded two NIAC Phase I studies: Comet Hitchhiker (2014) and Journey to the Center of Icy Moon (2016). Since 2019, he has been the Autonomy Lead of the EELS (Exobiology Extant Life Surveyor) project.

    Host: Stefanos Nikolaidis

    Location: Henry Salvatori Computer Science Center (SAL) - 101

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

  • Astani Civil and Environmental Engineering Seminar

    Thu, Oct 31, 2019 @ 03:00 PM - 04:00 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars

    Speaker: Dr. Shengqiang Cai, University of California, San Diego

    Talk Title: Soft Materials: from fundamental mechanics to applications

    Abstract: In recent years, soft materials have been extensively explored in a myriad of engineering applications including wearable devices and soft robotics. In the talk, I will discuss some of our recent work on soft materials. The presentation will mainly contain two parts. In the first part, I will talk about recent progress made in our group on a newly emerging polymer: liquid crystal elastomer (LCE). I will show some examples of the applications of liquid crystal elastomer as artificial muscle in the design of soft robots. I will also discuss several very intriguing phenomena associated with the large deformation and soft elasticity of liquid crystal elastomers. In the second part of the talk, I will discuss the challenges of studying the mechanics of soft materials. Electromechanical instabilities of dielectric elastomer balloons and nonlinear instability of an everted tube will serve as two examples.

    Biography: Professor Shengqiang Cai is currently an associate professor in the Department of Mechanical and Aerospace Engineering and Materials Science and Engineering program at the University of California, San Diego. He obtained his Ph.D. degree from Harvard University in 2011. After graduating from Harvard, he spent one year as postdoc at MIT. His research is mainly focused on mechanics of materials, especially soft materials and active materials. He received NSF CAREER award in 2016.

    Host: Dr. Qiming Wang

    Location: Michelson Center for Convergent Bioscience (MCB) - 102

    Audiences: Everyone Is Invited

    Contact: Evangeline Reyes

  • MASCLE Machine Learning Seminar: Joan Bruna (NYU) - On (Provably) Learning with Large Neural Networks

    Thu, Oct 31, 2019 @ 03:30 PM - 04:50 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Joan Bruna, New York University

    Talk Title: On (Provably) Learning with Large Neural Networks

    Series: Machine Learning Seminar Series hosted by USC Machine Learning Center

    Abstract: Virtually all modern deep learning systems are trained with some form of local descent algorithm over a high-dimensional parameter space. Despite its apparent simplicity, the mathematical picture of the resulting setup contains several mysteries that combine statistics, approximation theory and optimization, all intertwined in a curse of dimensionality.

    In order to make progress, authors have focused in the so-called 'overparametrised' regime, which studies asymptotic properties of the algorithm as the number of neurons grows. In particular, neural networks with a large number of parameters admit a mean-field description, which has recently served as a theoretical explanation for its favorable training properties. In this regime, gradient descent obeys a deterministic partial differential equation (PDE) that converges to a globally optimal solution for networks with a single hidden layer under appropriate assumptions.

    In this talk, we will review recent progress on this problem, and argue that such framework might provide crucial robustness against the curse of dimensionality. First, we will describe a non-local mass transport dynamics that leads to a modified PDE with the same minimizer, that can be implemented as a stochastic neuronal birth-death process, and such that it provably accelerates the rate of convergence in the mean-field limit. Next, such dynamics fit naturally within the framework of total-variation regularization, which following [Bach'17] have fundamental advantages in the high-dimensional regime. We will discuss a unified framework that controls both optimization, approximation and generalisation errors using large deviation principles, and discuss current open problems in this research direction.

    Joint work with G. Rotskoff (NYU), Z. Chen (NYU), S. Jelassi (NYU) and E. Vanden-Eijnden (NYU).

    This lecture satisfies requirements for CSCI 591: Research Colloquium.

    Biography: Joan Bruna is an Assistant Professor at Courant Institute, New York University (NYU), in the Department of Computer Science, Department of Mathematics (affiliated) and the Center for Data Science, since Fall 2016. He belongs to the CILVR group and to the Math and Data groups. From 2015 to 2016, he was Assistant Professor of Statistics at UC Berkeley and part of BAIR (Berkeley AI Research). Before that, he worked at FAIR (Facebook AI Research) in New York. Prior to that, he was a postdoctoral researcher at Courant Institute, NYU. He completed his PhD in 2013 at Ecole Polytechnique, France. Before his PhD he was a Research Engineer at a semi-conductor company, developing real-time video processing algorithms. Even before that, he did a MsC at Ecole Normale Superieure de Cachan in Applied Mathematics (MVA) and a BA and MS at UPC (Universitat Politecnica de Catalunya, Barcelona) in both Mathematics and Telecommunication Engineering. For his research contributions, he has been awarded a Sloan Research Fellowship (2018), a NSF CAREER Award (2019) and a best paper award at ICMLA (2018).

    Host: Yan Liu

    Location: Henry Salvatori Computer Science Center (SAL) - 101

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

  • UBER ATG Info Session

    Thu, Oct 31, 2019 @ 06:00 PM - 07:30 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions

    At Uber Advanced Technologies Group, we are developing safe and reliable self-driving technology that will transform the way the world moves. Our internship program in Pittsburgh, San Francisco, Toronto and Boulder ignites opportunity for students to make a lasting impact on the future of transportation.

    Our software engineers will give an overview of how we're developing self-driving cars and making them into self-driving Ubers!

    We're hiring computer science students from undergraduate to doctoral level.

    We offer visa sponsorship and are able to accept CPT/OPT.

    RSVP for this event on Viterbi Career Gateway

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

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections