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Events for April 23, 2018

  • Spring Explore USC

    Mon, Apr 23, 2018

    Viterbi School of Engineering Undergraduate Admission

    University Calendar


    Spring Explore is a full-day program running from 8:30am-5pm. The day includes a presentation from the Office of Admission, a USC Campus Tour, and visit with us in the Viterbi School of Engineering. During your time with us you will learn what your life will be like as an engineering student at USC, meet some of our current engineering students, see facilities and labs, and get your questions answered about the enrollment process, housing, and your "next steps".


    RSVP

    Location: USC Admission Office

    Audiences: Spring Admits and Their Families

    Contact: Viterbi Admission

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  • SSE Systems Leadership Series

    Mon, Apr 23, 2018 @ 10:00 AM - 12:00 PM

    Systems Architecting and Engineering, USC Viterbi School of Engineering

    Conferences, Lectures, & Seminars


    Speaker: Shane Henderson, Professor and Director - School of Operations Research and Information Engineering, Cornell University

    Talk Title: Citi Bike - Planning through a Combination of Continuous, Discrete, and Simulation Optimization

    Abstract: The Cornell School of Operations Research and Information Engineering has been working with the bike-sharing company Citi Bike since Citi Bike began operations in New York City in 2013. We provide data analysis and advice about strategy and operations, not just to Citi Bike, but also to its parent company Motivate that operates several bike-sharing programs around the USA. I will describe some of our modeling work with Citi Bike, focusing on a suite of models (not just simulation models) that informs the decision about where to position both racks and bikes around the approximately 600 stations in NYC. Joint work with Daniel Freund, Nanjing Jian, Eoin OMahony and David Shmoys.

    The Systems Leadership Series is a series of interactive conversations with leading systems thinkers who explore and examine the nature and complexity of systems that modern society depends upon. The series is an unparalleled learning opportunity as prominent speakers come to share cutting edge ideas, leadership styles and personal philosophies with students and faculty members.

    Biography: Shane G. Henderson is professor and director of the School of Operations Research and Information Engineering at Cornell University. He has previously held positions in the Department of Industrial and Operations Engineering at the University of Michigan and the Department of Engineering Science at the University of Auckland. He is the editor in chief of Stochastic Systems. He has served as chair of the INFORMS Applied Probability Society, and as simulation area editor for Operations Research. He is an INFORMS Fellow. His research interests include discrete-event simulation, simulation optimization, and emergency services planning.

    Host: Stevens Institute of Technology School of Systems and Enterprises

    More Info: https://www.stevens.edu/events/systems-leadership-series-shane-henderson-cornell-university

    Webcast: Register at the event link.

    Location: Online via WebEX

    WebCast Link: Register at the event link.

    Audiences: Everyone Is Invited

    Contact: James Moore II

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  • PhD Defense - Siddharth Jain

    Mon, Apr 23, 2018 @ 11:30 AM - 01:30 PM

    Computer Science

    University Calendar


    PhD Candidate: Siddharth Jain

    Committee: Ron Artstein (Chair), Paul Rosenbloom, Morteza Dehghani, Kallirroi Georgila, Elsi Kaiser

    Title: Identifying Social Roles in Online Contentious Discussions

    Time: Mon, April 23, 2018 @ 11:30 AM - 1:30 PM.

    Room: SOS B45.

    Abstract:
    The main goal of this dissertation is to identify social roles of participants in online contentious discussions, define these roles in terms of behavioral characteristics
    participants show in such discussions, and develop methods to identify these participant roles automatically. As social life becomes increasingly embedded in online systems, the concept of social role becomes increasingly valuable as a tool for
    simplifying patterns of action, recognizing distinct participant types, and cultivating and managing communities. In contentious discussions, the roles may exert a major influence on the course and/or outcome of the discussions. The existing work on social roles mostly focuses on either empirical studies or network based analysis. Whereas this dissertation presents a model of social roles by analyzing the content of the participants' contribution. In the first portion of this dissertation, I present the corpus of participant roles in online discussions from Wikipedia: Articles for Deletion and 4forums.com discussion forums.
    A rich set of annotations of behavioral characteristics such as stubbornness, sensibleness, influence, and ignored-ness, which I believe all contribute in the identification of roles played by participants, is created to analyze the contribution of the participants. Using these behavioral characteristics, Participant roles such as leader,
    follower, rebel, voice in wilderness, idiot etc. are defined which reflect these behavioral
    characteristics. In the second part of this dissertation I present the methods used to identify these participant roles in online discussions automatically using the
    contribution of the participants in the discussion. First, I develop two models to identify leaders in online discussions that quantify the basic leadership qualities of participants. Then, I present the system for analyzing the argumentation structure of comments in discussions. This analysis is divided in three parts: claim detection, claim delimitation, and claim-link detection. Then, the dissertation presents the social roles model to identify the participant roles in discussions. I create classification models and neural network structures for each behavioral characteristic using a set of features based on participants' contribution to the discussion to determine the behavior values for participants. Using these behavioral characteristic values the roles of participants
    are determined based on the rules determined from the annotation scheme. I show that for both, the classification models and neural networks, the rule based methods perform
    better than the model that identifies the participant roles directly. This signifies that the framework of breaking down the problem of identifying social roles to determining
    values of specific behavioral characteristics make it more intuitive in terms of what we expect from participants who assume these roles. Although the neural network methods
    perform worse than their traditional classification method counterparts, when provided with additional training data, neural network structures improve at a much higher rate.

    In the last part of the dissertation, I use the social roles model as a tool to analyze participants' behavior in large corpus. Social roles are automatically tagged in
    Wikipedia corpus containing -26000 discussions. This allows determining participants' roles over time in order to identify whether they assume different roles in different discussions, and what factors may affect an individual's role in such discussions. I investigate three factors: topic of the discussion, amount of contention in the discussion, and other participants in the discussion. The results show that participants behave similarly in most situations. However, the social roles model is able to identify instances where participants' behavior patterns are different than their own typical behavior. In doing so, the model provides useful context regarding the reason behind these behavioral patterns by identifying specific behaviors affected by the situation.

    Location: Social Sciences Building (SOS) - B45

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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  • PhD Academic Career Mentoring Panel Series

    Mon, Apr 23, 2018 @ 12:00 PM - 01:30 PM

    Viterbi School of Engineering Doctoral Programs

    Conferences, Lectures, & Seminars


    Speaker: Panel moderated by Timothy Pinkston, Vice Dean of Academic Affairs,

    Talk Title: Preparing for and Landing a Faculty Position

    Abstract: A panel of graduating Ph.D. students and a postdoc, will discuss "Preparing For, and Landing a Faculty Position." Moderated by Vice Dean Timothy Pinkston, the panelists will discuss key strategies for the early, middle, and latter stages of your PhD and postdoc that will help you prepare for landing a faculty position. A Q&A will be included in the panel discussion. A boxed lunch will be provided to all registered participants

    More Info: https://viterbigrad.usc.edu/instructional-support/events-workshops/phd-academic-career-mentoring-panel-series/

    More Information: USC Panel Flyer.pdf

    Location: 102

    Audiences: Ph.D. and Postdoctoral

    Contact: Tracy Charles

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  • Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering

    Mon, Apr 23, 2018 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Steven Brunton, University of Washington

    Talk Title: Data-Driven Discovery and Control of Nonlinear Systems

    Series: Joint CSC@USC/CommNetS-MHI Seminar Series

    Abstract: The ability to discover physical laws and governing equations from data is one of humankind's greatest intellectual achievements. A quantitative understanding of dynamic constraints and balances in nature has facilitated rapid development of knowledge and enabled advanced technology, including aircraft, combustion engines, satellites, and electrical power. There are many more critical data-driven problems, such as understanding cognition from neural recordings, inferring patterns in climate, determining stability of financial markets, predicting and suppressing the spread of disease, and controlling turbulence for greener transportation and energy. With abundant data and elusive laws, data-driven discovery of dynamics will continue to play an increasingly important role in these efforts.

    This work develops a general framework to discover the governing equations underlying a dynamical system simply from data measurements, leveraging advances in sparsity-promoting techniques and machine learning. The resulting models are parsimonious, balancing model complexity with descriptive ability while avoiding overfitting. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions. This perspective, combining dynamical systems with machine learning and sparse sensing, is explored with the overarching goal of real-time closed-loop feedback control of complex systems. Connections to modern Koopman operator theory are also discussed.

    Biography: Steven L. Brunton is an Assistant Professor of Mechanical Engineering and a Data Science Fellow at the eScience Institute at the University of Washington in Seattle. He received a B.S. in Mathematics with a minor in Control and Dynamical Systems from Caltech in 2006, and received a Ph.D. in Mechanical and Aerospace Engineering from Princeton in 2012. His research interests include data-driven modeling and control, dynamical systems, sparse sensing and machine learning applied to complex systems in fluid dynamics, optics, neuroscience, bio-locomotion, and renewable energy.

    Host: Eva Kanso, kanso@usc.edu

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

    Audiences: Everyone Is Invited

    Contact: Gerrielyn Ramos

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