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Searching and summarization are two of the most fundamental tasks in massive data analysis. In this talk, I will focus on these two tasks from the perspective of diversity and fairness. Search is often formalized as the (approximate) nearest neighbor problem. Sepideh Mahabadi Research Assistant Professor Toyota Technological Institute at Chicago (TTIC) WHEN: September 18, 2020 @ 10:00 am - 11:00 am Sepideh Mahabadi Ali Vakiliany Abstract We give a local search based algorithm for k-median and k-means (and more generally for any k-clustering with ‘ p norm cost function) from the perspective of individual fairness.

Massachusetts Institute of Technology (5) Carnegie Mellon University (2) Microsoft Research (2) University of Washington, Seattle (2) 2021-02-04 · Sepideh Mahabadi is a research assistant professor at the Toyota Technological Institute at Chicago (TTIC). She received her Ph.D. from MIT, where she was advised by Piotr Indyk. For a year, she was a postdoctoral research scientist at Simons Collaboration on Algorithms and Geometry based at Columbia University. Filter by Year.

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Piotr Indyk, Sepideh Mahabadi, Shayan Oveis Gharan, Alireza Rezaei, Composable Core-sets for Determinant Maximization Problems via Spectral Spanners. Sepideh Mahabadi is a postdoctoral research scientist with an appointment at the Simons Collaboration on Algorithms and Geometry based at Columbia University, working with Alexandr Andoni. She received her Ph.D. in computer science from the Massachusetts Institute of Technology in 2017, under the supervision of Piotr Indyk.

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Join Facebook to connect with Sepideh Mahabadi and others you may know. Facebook gives people the power to share and makes the world more open and connected. 2021-02-04 Title: Non-Adaptive Adaptive Sampling in Turnstile Streams Abstract: Adaptive sampling is a useful algorithmic tool for data summarization problems in the cl 2019-05-24 Sepideh Mahabadi is a research assistant professor at the Toyota Technological Institute at Chicago (TTIC). She received her Ph.D.

Apr 23, 2020 Non-Adaptive Adaptive Sampling on Turnstile Streams. Authors:Sepideh Mahabadi, Ilya Razenshteyn, David P. Woodruff, Samson Zhou. Individual Fairness for k-Clustering. Sepideh Mahabadi · Ali Vakilian. Tue Jul 14 07:00 AM -- 07:45 AM & Tue Jul 14 06:00 PM -- 06:45 PM (PDT) @ Virtual Sampling a Near Neighbor in High Dimensions--Who is the Fairest of Them All? ( pdf); Piotr Indyk, Sepideh Mahabadi, Shayan Oveis Gharan, Alireza Rezaei, Research Assistant Professor at TTIC. PhD in (Theoretical) Computer Science from MIT. Chicago. mit.edu/~mahabadi/.
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32nd International Symposium on @InProceedings{indyk_et_al:LIPIcs:2017:7561, author = {Piotr Indyk and Sepideh Mahabadi and Ronitt Rubinfeld and Jonathan Ullman and Ali Vakilian and Near Neighbor: Who is the Fairest of Them All?.

Sepideh Mahabadi Ali Vakiliany Abstract We give a local search based algorithm for k-median and k-means (and more generally for any k-clustering with ‘ p norm cost function) from the perspective of individual fairness. More precisely, for a point xin a point set Pof size n, … Sepideh Mahabadi Research Assistant Professor Toyota Technological Institute at Chicago (TTIC) WHEN: September 18, 2020 @ 10:00 am - 11:00 am This event is free and open to the public Add to Google Calendar. WEB: Event Website. SHARE: (Click “Event Website” above to access the zoom link.) 2020-07-07 Talk: Sepideh Mahabadi: Composable Core-sets for Diversity and Coverage Maximization, and Its Application in Diverse Near Neighbor Problem.
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In the 52nd ACM Symposium on Theory of Computing (STOC), 2020. Piotr Indyk, Sepideh Mahabadi, Shayan Oveis Gharan, Alireza Rezaei, Composable Core-sets for Determinant Maximization Problems via Spectral Spanners.