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Events for the 2nd week of May

  • USC AI Futures Symposium on Artificial Intelligence and Data Science

    Mon, May 03, 2021 @ 08:45 AM - 12:15 PM

    Information Sciences Institute, USC Viterbi School of Engineering

    University Calendar

    Profound innovations at the intersection of artificial intelligence and data science are changing our lives. These innovations are transforming how we improve our health, connect with others, sustain our environment, understand complex systems, and enrich our lives. This symposium will present an overview of interdisciplinary research at USC in these critical areas.

    This event is part of the USC AI Futures Symposium Series. A prior event was held in January 2021 with the theme: Will AIs Ever Be One of Us?.

    The sessions will run from 8:45am PST to 12:15 PST on May 3-5, 2021.

    Find out more: https://isi-usc-edu.github.io/USC-AI-DS-Symposium/

    Registration: https://usc.zoom.us/webinar/register/WN_V-mMUlHGQMWnkecKyPUQWA

    Audiences: Everyone Is Invited

    Contact: Yolanda Gil

  • Info Session: ENGR 345 Principle and Practices of Global Innovation

    Mon, May 03, 2021 @ 01:00 PM - 01:30 PM

    USC Viterbi School of Engineering

    Workshops & Infosessions

    Build your global competence and network without leaving campus!

    A global class using the classroom-without-border platform and learning-from-diversity pedagogy. Students will study the dynamic lifecycle of technology innovation in competitive global market with classmates from multiple universities around the world.

    The ENGR 345 course is 3 units and is open to all USC majors, levels, and USC schools. It can be taken as a free elective course or as an approved elective course if approved by your advisor.

    Join our info session in Microsoft Teams to learn more about the course and get your questions answered at tinyurl.com/ipodiainfo.

    Location: tinyurl.com/ipodiainfo

    Audiences: Everyone Is Invited

    Contact: Jenny iPodia Program

  • Ph.D. Dissertation

    Mon, May 03, 2021 @ 03:30 PM - 05:30 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars

    Speaker: Qian Fang, Ph.D. Candidate, Astani Department of Civil and Environmental Engineering

    Talk Title: Optimal Clipped Linear Strategies for Controllable Damping

    Abstract: Please see attached abstract

    More Information: Q. Fang Dissertation-Abstract.pdf

    Location: https://usc.zoom.us/j/97831642691 Meeting ID: 978 3164 2691

    Audiences: Everyone Is Invited

    Contact: Evangeline Reyes

  • MS Awards 2021

    Mon, May 03, 2021 @ 06:00 PM - 08:00 PM

    Viterbi School of Engineering Masters Programs

    Student Activity

    Audiences: Everyone Is Invited

    Contact: Juli Legat

  • Omolola (Lola) Eniola-Adefeso, University of Michigan-Ann Arbor

    Wed, May 05, 2021 @ 09:00 AM - 10:00 AM

    Alfred E. Mann Department of Biomedical Engineering

    Conferences, Lectures, & Seminars

    Speaker: Omolola (Lola) Eniola-Adefeso, University of Michigan-Ann Arbor

    Talk Title: Leveraging the natural cellular & biomolecular interactions in blood to design targeted particle therapeutics for acute and chronic inflammatory diseases

    Host: Kirk Shung

    Audiences: Everyone Is Invited

    Contact: Michele Medina


    Thu, May 06, 2021 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars

    Speaker: Zhou Yu , Columbia University


    Abstract: Reminder Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you're highly encouraged to use your USC account to sign into Zoom. If you're an outside visitor, please inform nlg-seminar-admin2@isi.edu beforehand so we'll be aware of your attendance and let you in.

    Humans routinely learn new concepts using natural language communications, even in scenarios with limited or no labeled examples. Interactions are another key aspect of human learning as well. Learning to ask good questions is a key step towards effective learning. Can machines do the same? In this talk, we will talk about how can a machine learn to ask good natural language questions and plan dynamically what questions to ask next to learn tasks effectively in low-resource settings.

    Biography: Zhou Yu joined the CS department at Columbia University in Jan 2021 as an Assistant Professor. Before that, she was an Assistant Professor at UC Davis. She obtained her Ph.D. from Carnegie Mellon University in 2017. Zhou has built various dialog systems that have a real impact, such as a job interview training system, a depression screening system, and a second language learning system. Her research interest includes dialog systems, language understanding and generation, vision and language, human computer interaction, and social robots. Zhou received an ACL 2019 best paper nomination, featured in Forbes 2018 30 under 30 in Science, and won the 2018 Amazon Alexa Prize.

    Host: Jon May and Mozhdeh Gheini

    More Info: https://nlg.isi.edu/nl-seminar/

    Webcast: https://youtu.be/rNyOspG27Xs

    Location: Information Science Institute (ISI) - Virtual Only

    WebCast Link: https://youtu.be/rNyOspG27Xs

    Audiences: Everyone Is Invited

    Contact: Petet Zamar

    Event Link: https://nlg.isi.edu/nl-seminar/

  • Repeating EventVirtual First-Year Admission Information Session

    Thu, May 06, 2021 @ 04:00 PM - 05:00 PM

    Viterbi School of Engineering Undergraduate Admission

    Workshops & Infosessions

    Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for high school students and their family members to learn more about the USC Viterbi undergraduate experience. Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life. Guests will be able to ask questions and engage in further discussion toward the end of the session.

    Please Register Here!

    Audiences: Everyone Is Invited

    View All Dates

    Contact: Viterbi Undergraduate Admission

  • PhD Defense - Chung Ming Cheung

    Fri, May 07, 2021 @ 09:00 AM - 11:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Candidate: Chung Ming Cheung

    Time: May 7th 2021, 9 - 11 am

    Zoom link:

    Committee: Professor Viktor K Prasanna (Chair)
    Professor C S Raghavendra
    Professor Aiichiro Nakano

    Title: Data-Driven Methods for Increasing Real-Time Observability in Smart Distribution Grids

    Traditional power distribution grids have evolved into smart grids with the development of advanced metering infrastructures and renewable energy based distributed energy resources (DER). This has introduced the following challenges: (1) The stochasity of renewable energy based DERs has increased the volatility of grid frequency; (2) the decentralization of generation into small scaled DERs has reduced grid inertia. To address these challenges, real-time knowledge and understanding of signal measurements of grid assets, called observability, are crucial to make grid operation decisions swiftly. High observability can be obtained through extensive metering of assets in smart grids for data collection, and time series analytics that extract information from the collected time series data. However, the proliferation of DERs has introduced new challenges in these analytics. DERs located behind-the-meters (BTM) are not recorded individually and hidden from real-time observations. This combined with the volatile nature of DER assets greatly reduces observability. As a result, these data-driven models do not have full observability of data and suffer from accuracy losses.

    In this thesis, we develop data-driven approaches to improve observability. We develop unsupervised disaggregation models for separation of signals of BTM DERs hidden from net meter measurements. We focus on the separation of signals from the activity of BTM solar photovoltaics and battery storages. We also propose capturing spatial features using machine learning models such as spatial-temporal graph convolution networks for improving time series analytics in smart grids, e.g. load forecasting and missing data imputation. Moreover, we show that the increase in observability provided by these data-driven models can enhance other time series analytics in smart grids.

    WebCast Link: prasannaseminars.github.io

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

  • PhD Defense - Nazanin Alipourfard

    Fri, May 07, 2021 @ 09:00 AM - 11:00 AM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Candidate: Nazanin Alipourfard

    Date: May 7, 2021

    Time: 9-11am

    Dissertation defense committee:
    Kristina Lerman (chair), Ellis Horowitz, Jose-Luis Ambite, Greg Ver Steeg, Phebe Vayanos

    Emergence and Mitigation of Bias in Data and Networks

    The presence of bias often complicates the quantitative analysis of large-scale heterogeneous or network data. Discovering and mitigating these biases enables a more robust and generalizable analysis of data. This thesis focuses on the 1) discovery, 2) measurement and 3) mitigation of biases in heterogeneous and network data.

    The first part of the thesis focuses on removing biases created by the existence of diverse classes of individuals in the population. I describe a data-driven discovery method that leverages Simpson's paradox to identify subgroups within a population whose behavior deviates significantly from the rest of the population. Next, to address the challenges of multi-dimensional heterogeneous data analysis, I propose a method that discovers latent confounders by simultaneously partitioning the data into fuzzy clusters (disaggregation) and modeling the behavior within them (regression).

    The second part of this thesis is about biases in bi-populated networked data. First, I study the perception bias of individuals about the prevalence of a topic among their friends in the Twitter social network. Second, I show the existence of power-inequality in author citation networks in six different fields of study, due to which authors from one group (e.g., women) receive systematically less recognition for their work than another group (e.g., men). As the last step, I connect these two concepts (perception bias and power-inequality) in bi-populated networks and show that while these two measures are highly correlated, there are some scenarios where there is a disparity between them.

    Zoom Link:

    WebCast Link: https://usc.zoom.us/j/93756467657?pwd=dWxEMHVMYnppZnAyZHRYVEVaTkZSQT09

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon