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Events for September 09, 2020
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Internship/Job Search Open Forum
Wed, Sep 09, 2020 @ 08:30 AM - 09:30 AM
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.
To access this workshop, log into Viterbi Career Gateway>> Events>>Workshops: https://shibboleth-viterbi-usc-csm.symplicity.com/sso/
For more information about Labs & Open Forums, please visit viterbicareers.usc.edu/workshopsLocation: Zoom
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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M.S. Group AdvisementWed, Sep 09, 2020 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Workshops & Infosessions
This optional group advisement session is for new and continuing M.S. Computer Science students and M.S. students in our Data Science Programs. Access instructions will be emailed to students prior to the session.
Location: Online - Zoom
WebCast Link: https://usc.zoom.us/j/92220006953
Audiences: Graduate
Contact: Ryan Rozan
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar
Wed, Sep 09, 2020 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Alan Mishchenko, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
Talk Title: Circuit-Based Intrinsic Methods to Detect Overfitting
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: The focus of this talk is on intrinsic methods to detect overfitting. By intrinsic methods, we mean methods that rely only on the model and the training data, as opposed to traditional methods that rely on performance on a test set or on bounds from model complexity. We propose a family of intrinsic methods, called Counterfactual Simulation (CFS), which analyze the flow of training examples through the model by identifying and perturbing rare patterns. By applying CFS to logic circuits we get a method that has no hyper-parameters and works uniformly across different types of models such as neural networks, random forests and lookup tables. Experimentally, CFS can separate models with different levels of overfit using only their logic circuit representations without any access to the high level structure. By comparing lookup tables, neural networks, and random forests using CFS, we get insight into why neural networks generalize. The paper appeared at ICML 2020: https://people.eecs.berkeley.edu/~alanmi/publications/2020/icml20_cfs.pdf
Biography: Alan graduated with M.S. from Moscow Institute of Physics and Technology (Moscow, Russia) in 1993 and received his Ph.D. from Glushkov Institute of Cybernetics (Kiev, Ukraine) in 1997. In 2002, Alan joined the EECS Department at the University of California, Berkeley, where he is currently a full researcher. His research is in computationally efficient logic synthesis and formal verification.
Host: Pierluigi Nuzzo, nuzzo@usc.edu
Webcast: https://usc.zoom.us/webinar/register/WN_YSl0DRVOQJetWGNAACPOYQLocation: Online
WebCast Link: https://usc.zoom.us/webinar/register/WN_YSl0DRVOQJetWGNAACPOYQ
Audiences: Everyone Is Invited
Contact: Talyia White
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AME Seminar
Wed, Sep 09, 2020 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Adrian Lozano-Duran, Stanford
Talk Title: Prediction of real-world external aerodynamics using numerical simulations
Abstract: The use of computational fluid dynamics for external aerodynamic applications has been a key tool for aircraft design in the modern aerospace industry. In the last decades, large-eddy simulation with near-wall modeling (wall-modeled LES) has gained momentum as a cost-effective approach for both scientific research and industrial applications. In this talk, we discuss current challenges of wall-modeled LES to become a design tool for the aerospace industry. Our focus is on the working principles and performance of wall-modeled LES for external aerodynamic applications, with emphasis on realistic commercial aircrafts. We examine the computational cost to predict mean flow features and forces for a given degree of accuracy using theory and numerical simulations of the NASA Juncture Flow and the JAXA Standard Model. The vision presented here is motivated by discussions in previous AIAA workshops and the experience acquired at the Center for Turbulence Research during the last years.
Biography: Dr. Adrian Lozano-Duran is a Postdoctoral Research Fellow at the Center for Turbulence Research at Stanford University hosted by Prof. Moin. He received his PhD in Aerospace Engineering from the Technical University of Madrid in 2015 at the Fluid Mechanics Lab. advised by Prof. Jiménez. The overarching theme of his research is physics and modeling of wall-bounded turbulence via theory and computational fluid mechanics. His work covers a wide range of topics, such as turbulence theory and modeling by machine learning, large-eddy simulation for external aerodynamics, geophysical and multiphase flows, among others.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/99375525323Location: https://usc.zoom.us/j/99375525323
WebCast Link: https://usc.zoom.us/j/99375525323
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
