BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:IC Colloquium: Diversity and Fairness in Data Summarization Algori
 thms
DTSTART:20210422T140000
DTEND:20210422T150000
DTSTAMP:20260414T175502Z
UID:ce5a97b45cca718f1d18244f44fe843438a46768ac4eced1be0be2e9
CATEGORIES:Conferences - Seminars
DESCRIPTION:By: Sepideh Mahabadi - Toyota Technological Institute at Chica
 go\nIC Faculty candidate\n\nAbstract\nSearching and summarization are two 
 of the most fundamental tasks in massive data analysis. In this talk\, I w
 ill focus on these two tasks from the perspective of diversity and fairnes
 s.\nSearch is often formalized as the (approximate) nearest neighbor probl
 em. Despite an extensive research on this topic\, its basic formulation is
  insufficient for many applications. In this talk\, I will describe such a
 pplications and our approaches to address them. For example\, we show how 
 to incorporate diversity or fairness in the results of a search query.\n\n
 A prominent approach to summarize the data is to compute a small “core-s
 et”: a subset of the data that is sufficient for approximating the solut
 ion of a given task. We introduce the notion of ``composable core-sets" as
  core-sets with the composability property: the union of multiple core-set
 s should form a good summary for the union of the original data sets. This
  composability property enables efficient solutions to a wide variety of m
 assive data processing applications\, including distributed computation (e
 .g. Map-Reduce model)\, streaming algorithms\, and similarity search. We s
 how how to produce such efficient summaries of the data while preserving t
 he diversity in the data set. I will describe several metrics for capturin
 g the notion of diversity\, and present efficient algorithms for construct
 ion of composable core-sets with respect to those metrics.\n\nBio\nSepideh
  Mahabadi is a research assistant professor at the Toyota Technological In
 stitute at Chicago (TTIC).  She received her PhD from MIT\, where she was
  advised by Piotr Indyk. For a year\, she was a postdoctoral research scie
 ntist at Simons Collaboration on Algorithms and Geometry based at Columbia
  University. Her research focuses on Theoretical Foundations of Massive D
 ata including High Dimensional Computational Geometry\, Streaming Algorith
 ms\, and Data Summarization\; as well as Social Aspects of Algorithms for 
 Massive Data including Diversity Maximization and Algorithmic Fairness.\n\
 nMore information
LOCATION:https://epfl.zoom.us/j/85845237967?pwd=emxFTEpkWWdSdjhYTzkvbVQyM2
 V2QT09
STATUS:CONFIRMED
END:VEVENT
END:VCALENDAR