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Events for May 07, 2021

  • PhD Defense - Chung Ming Cheung

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

    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

    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