Logo: University of Southern California

Events Calendar

Select a calendar:

Filter February Events by Event Type:

Events for February 28, 2020

  • IISE Western Regional Conference

    Fri, Feb 28, 2020

    Daniel J. Epstein Department of Industrial and Systems Engineering, USC Viterbi School of Engineering

    Conferences, Lectures, & Seminars

    Speaker: Various, Various

    Talk Title: IISE Western Regional Conference

    Abstract: The Institute of Industrial and Systems Engineers at the University of Southern California is hosting the 2020 IISE Western Regional Conference February 28 - March 1, 2020.

    Please register here: https://ise.usc.edu/iise-student-conference

    This conference is a unique opportunity for students and professionals in the field of Industrial Engineering to network, explore industry trends, and compete for a chance to present their work at the IISE Annual Conference and Expo 2020 in New Orleans, Louisiana.

    The conference will be held on the main campus and includes the technical paper competition, keynote speakers, expert panels, plant tours, and other activities.

    Host: Daniel J. Epstein Department of Industrial and Systems Engineering

    More Info: https://ise.usc.edu/iise-student-conference/

    More Information: ConferenceFlyer.pdf

    Audiences: Everyone Is Invited

    Contact: Greta Harrison

    Event Link: https://ise.usc.edu/iise-student-conference/

    Add to Google CalendarDownload ICS File for OutlookDownload iCal File
  • Repeating EventGrammar Tutoring

    Fri, Feb 28, 2020 @ 10:00 AM - 12:00 PM

    Viterbi School of Engineering Student Affairs

    Workshops & Infosessions

    Need help refining your grammar skills in your academic and professional writing? Meet one-on-one with professors from the Engineering Writing Program, work together on your grammar skills, and take your writing to the next level!

    Sign up here: http://bit.ly/grammaratUSC

    All sessions will be via Zoom.

    Questions? Contact helenhch@usc.edu

    Location: ZOOM

    Audiences: Graduate and Undergraduate Students

    View All Dates

    Contact: Helen Choi

    Add to Google CalendarDownload ICS File for OutlookDownload iCal File
  • Machine Learning for Performance and Power Modeling/Prediction

    Machine Learning for Performance and Power Modeling/Prediction

    Fri, Feb 28, 2020 @ 10:30 AM - 12:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars

    Speaker: Prof. Lizy Kurian John, UT Austin

    Talk Title: Machine Learning for Performance and Power Modeling/Prediction

    Abstract: Estimating the power and thermal characteristics of SoCs is essential for designing its power delivery system, packaging, cooling, and power/thermal management schemes. Power models that estimate the power consumption of each functional unit/hardware component from first principles are slow and tedious to build. Machine learning can be used to create power models that are fast and reasonably accurate. Machine learning can also be used to calibrate analytical models that estimate power. In this talk, I will present some examples of performance and power modeling using machine learning.
    Another application for machine learning has been to create max power stressmarks. Manually developing and tuning so called stressmarks is extremely tedious and time-consuming while requiring an intimate understanding of the processor. In our past research, we created a framework that uses machine learning for the automated generation of stressmarks. In this talk, the methodology of the creation of automatic stressmarks will be explained. Experiments on multiple platforms validating the proposed approach will be described.
    Yet another application for machine learning is in cross-platform performance and power prediction. If one model is slow to run real-world benchmarks/workloads, is it possible to predict/estimate the performance/power by using runs on another platform? Are there correlations that can be exploited using machine learning to make cross-platform performance and power predictions? A methodology to perform cross-platform performance/power predictions will be presented in this talk.

    Biography: Lizy Kurian John is Cullen Trust for Higher Education Endowed Professor in the Electrical and Computer Engineering at the University of Texas at Austin. She received her Ph. D in Computer Engineering from Pennsylvania State University. Her research interests include workload characterization, performance evaluation, memory systems, reconfigurable architectures, and high-performance architectures for emerging workloads. She is a recipient of many awards including The Pennsylvania State University Outstanding Engineering Alumnus 2011, the NSF CAREER award, UT Austin Engineering Foundation Faculty Award, Halliburton, Brown and Root Engineering Foundation Young Faculty Award 2001, University of Texas Alumni Association (Texas Exes) Teaching Award 2004, etc. She has co-authored books on Digital Systems Design using VHDL (Cengage Publishers, 2007, 2017), a book on Digital Systems Design using Verilog (Cengage Publishers, 2014) and has edited 4 books including a book on Computer Performance Evaluation and Benchmarking. In the past, she has served as Associate Editor of IEEE Transactions on Computers, IEEE Transactions on VLSI, IEEE Computer Architecture Letters, ACM Transactions on Architecture and Code Optimization, and IEEE Micro. She is currently the Editor-in-Chief of IEEE Micro. She holds 12 US patents and is an IEEE Fellow (Class of 2009).

    Host: Xuehai Qian, xuehai.qian@usc.edu

    More Information: 200228_Lizy John_CENG.pdf

    Location: Ronald Tutor Hall of Engineering (RTH) - 105

    Audiences: Everyone Is Invited

    Contact: Brienne Moore

    Add to Google CalendarDownload ICS File for OutlookDownload iCal File
  • Virtual Graduate Mixer

    Fri, Feb 28, 2020 @ 01:00 PM - 05:00 PM

    Viterbi School of Engineering Alumni

    Receptions & Special Events

    Don't miss out on this chance to chat with participants from all around the world! Share your experiences, exchange career tips and build your professional network -- all online, from any device.

    Current Viterbi Students, Viterbi Alumni, Engineering Professionals, and Employers hiring Engineers are all encouraged to attend. Don't wait, register now!

    WebCast Link: Please register for more information.

    Audiences: Everyone Is Invited

    Contact: Kristy Ly

    Add to Google CalendarDownload ICS File for OutlookDownload iCal File
  • PhD Defense - Ayush Jaiswal

    Fri, Feb 28, 2020 @ 01:30 PM - 03:30 PM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Candidate: Ayush Jaiswal
    Date: Friday, February 28, 2020
    Time: 1:30 PM - 3:30 PM
    Location: SAL 213
    Committee: Premkumar Natarajan (Chair), Ram Nevatia, Cauligi S. Raghavendra

    Title: Invariant Representation Learning for Robust and Fair Predictions


    Learning representations that are invariant to nuisance factors of data improves robustness of machine learning models, and promotes fairness for factors that represent biasing information. This view of invariance has been adopted for deep neural networks (DNNs) recently as they learn latent representations of data by design. Numerous methods for invariant representation learning for DNNs have emerged in recent literature, but the research problem remains challenging to solve: existing methods achieve partial invariance or fall short of optimal performance on the prediction tasks that the DNNs need to be trained for.

    This thesis presents novel approaches for inducing invariant representations in DNNs by effectively separating predictive factors of data from undesired nuisances and biases. The presented methods improve the predictive performance and the fairness of DNNs through increased invariance to undesired factors. Empirical evaluation on a diverse collection of benchmark datasets shows that the presented methods achieve state-of-the-art performance.

    Application of the invariance methods to real-world problems is also presented, demonstrating their practical utility. Specifically, the presented methods improve nuisance-robustness in presentation attack detection and automated speech recognition, fairness in face-based analytics, and generalization in low-data and semi-supervised learning settings.

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

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

    Add to Google CalendarDownload ICS File for OutlookDownload iCal File