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Events for April 03, 2018

  • Series of Lectures

    Tue, Apr 03, 2018 @ 10:00 AM - 12:00 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Thanasis Fokas,

    Talk Title: New development in unified transform (Fokas Method, www.wikipedia.org/wiki/Fokas_method).

    Abstract: Prof Fokas will give a series of lectures on new development in unified transform (Fokas Method, www.wikipedia.org/wiki/Fokas_method) including applications in water waves with moving boundaries, elliptic PDEs in curved domains and PDEs with variable coefficients. The first two lectures will be in KAP 209 Tuesday, April 3, 2018.

    Session #1: from 10am to Noon
    Session #2: from 3pm to 5pm


    Location: Kaprielian Hall (KAP) - 209

    Audiences: Everyone Is Invited

    Contact: Evangeline Reyes

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  • Epstein Institute Seminar, ISE 651

    Tue, Apr 03, 2018 @ 11:00 AM - 12:00 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Sham Kakade, Associate Professor, University of Washington

    Talk Title: Accelerating Stochastic Gradient Descent for Convex and Non-Convex Optimization

    Host: Dr. Meisam Razaviyayn

    More Information: April 3, 2018_Kakade.pdf

    Location: Ethel Percy Andrus Gerontology Center (GER) - 206

    Audiences: Everyone Is Invited

    Contact: Grace Owh

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  • CS Colloquium: Nicolas Papernot (Pennsylvania State University) - Characterizing the Space of Adversarial Examples in Machine Learning

    Tue, Apr 03, 2018 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Nicolas Papernot, Pennsylvania State University

    Talk Title: Characterizing the Space of Adversarial Examples in Machine Learning

    Series: CS Colloquium

    Abstract: There is growing recognition that machine learning (ML) exposes new security and privacy vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited but expanding. In this talk, I explore the threat model space of ML algorithms, and systematically explore the vulnerabilities resulting from the poor generalization of ML models when they are presented with inputs manipulated by adversaries. This characterization of the threat space prompts an investigation of defenses that exploit the lack of reliable confidence estimates for predictions made. In particular, we introduce a promising new approach to defensive measures tailored to the structure of deep learning. Through this research, we expose connections between the resilience of ML to adversaries, model interpretability, and training data privacy.

    This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity, seats will be first come first serve.


    Biography: Nicolas Papernot is a PhD student in Computer Science and Engineering working with Professor Patrick McDaniel at the Pennsylvania State University. His research interests lie at the intersection of computer security, privacy and machine learning. He is supported by a Google PhD Fellowship in Security and received a best paper award at ICLR 2017. He is also the co-author of CleverHans, an open-source library widely adopted in the technical community to benchmark machine learning in adversarial settings. In 2016, he received his M.S. in Computer Science and Engineering from the Pennsylvania State University and his M.S. in Engineering Sciences from the Ecole Centrale de Lyon.

    Host: Aleksandra Korolova

    Location: Olin Hall of Engineering (OHE) - 100D

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

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  • EE Seminar - Embracing Uncertainty: from Differential Privacy to Generative Adversarial Privacy

    Tue, Apr 03, 2018 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Peter Kairouz, Postdoctoral Scholar, Stanford University

    Talk Title: Embracing Uncertainty: from Differential Privacy to Generative Adversarial Privacy

    Abstract: The explosive growth in connectivity and data collection is accelerating the use of machine learning to guide consumers through a myriad of choices and decisions. While this vision is expected to generate many disruptive businesses and social opportunities, it presents one of the biggest threats to privacy in recent history. In response to this threat, differential privacy (DP) has recently surfaced as a context-free, robust, and mathematically rigorous notion of privacy.

    The first part of my talk will focus on understanding the fundamental tradeoff between DP and utility for a variety of learning applications. Surprisingly, our results show the universal optimality of a family of extremal privacy mechanisms called staircase mechanisms. While the vast majority of early works on DP have focused on using the Laplace mechanism, our results indicate that it is often strictly suboptimal and can be replaced by a staircase mechanism to improve utility. Our results also show that the strong privacy guarantees of DP often come at a significant loss in utility.

    The second part of my talk is motivated by the following question: can we exploit data statistics to achieve a better privacy-utility tradeoff? To address this question, I will present a novel context-aware notion of privacy called generative adversarial privacy (GAP). GAP leverages recent advancements in generative adversarial networks (GANs) to arrive to a unified framework for data-driven privacy that has deep game-theoretic and information-theoretic roots. I will conclude my talk by showcasing the performance of GAP on real life datasets.


    Biography: Peter Kairouz is a postdoctoral scholar at Stanford University. He received his PhD in ECE from the University of Illinois at Urbana-Champaign (UIUC). He interned twice at Qualcomm and more recently at Google where he designed privacy-aware machine learning algorithms. He is the recipient of the 2015 ACM SIGMETRICS Best Paper Award, the 2012 Roberto Padovani Scholarship from Qualcomm's Research Center, and the 2016 Harold L. Olesen Award for Excellence in Undergraduate Teaching from UIUC. His research interests are interdisciplinary and span the areas of data and network sciences, privacy-preserving data analysis, machine learning, and information theory.

    Host: Keith Chugg, chugg@usc.edu

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

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • Epstein Institute Seminar, ISE 651

    Tue, Apr 03, 2018 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Christopher Williams, Associate Professor, Virginia Tech

    Talk Title: Additive Manufacturing of Multifunctional Products via Tailored Materials and Topologies

    Host: Dr. Yong Chen

    More Information: April 3, 2018_Williams.pdf

    Location: Ethel Percy Andrus Gerontology Center (GER) - 206

    Audiences: Everyone Is Invited

    Contact: Grace Owh

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