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Speaker: Sepideh Mahabadi , MIT Date: Wednesday, May 07, 2014 Time: 4:00 PM to 5:00 PM Public: Yes Location: 32-G575 Event Type: Sepideh Mahabadi (Toyota Technological Institute at Chicago) Ruta Mehta (University of Illinois, Urbana-Champaign) Raghu Meka (University of California, Los Angeles) Dor Minzer (Massachusetts Institute of Technology) Jesper Nederlof (Utrecht University) Jelani Nelson (University of California, Berkeley) Sasho Nikolov (University of Toronto) Sepideh Mahabadi Submitted to the Department of Electrical Engineering and Computer Science on May 22, 2013, in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering Abstract This thesis investigates two variants of the approximate nearest neighbor problem. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We introduce a new variant of the nearest neighbor search problem, which allows for some coordinates of the dataset to be arbitrarily corrupted or unknown. Formally, given a dataset of n points P = {x1,,xn} in high-dimensions, and a parameter k, the goal is to preprocess the dataset, such that given a query point q CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We consider the classic SetCover problem in the data stream model. For n elements and m sets we give a O(1/δ)-pass algorithm with a strongly sub-linear Õ(mn δ) space and logarithmic approximation factor1. This yields a significant improvement over the earlier algorithm of Demaine et al.

For a year, she was a postdoctoral research scientist at Simons Collaboration on Algorithms and Geometry based at Columbia University. %0 Conference Paper %T Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm %A Sepideh Mahabadi %A Piotr Indyk %A Shayan Oveis Gharan %A Alireza Rezaei %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97-mahabadi19a %I PMLR %P 4254 In Spring Quarter 2021, Sepideh Mahabadi will be teaching a new course, Special Topics: Algorithms for Massive Data. This course will cover the theoretical aspects of computation over massive data. While classical algorithms can be too slow, or require too much space on big data, in this course students will focus on designing algorithms that are specifically tailored for large datasets. Sepideh Mahabadi* 1 Ali Vakilian* 2 Abstract We give a local search based algorithm for k-median and k-means (and more generally for any k-clustering with ‘ pnorm cost function) from the perspective of individual fairness. More precisely, for a point xin a point set Pof size n, let r(x) be the minimum radius such that the ball of radius Sepideh Mahabadi is on Facebook. Join Facebook to connect with Sepideh Mahabadi and others you may know.

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Sepideh Mahabadi. Toyota Technological Institute at Chicago (TTIC) Verified email at ttic.edu - Homepage.

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We show that for every map f there exists an outer bi-Lipschitz extension f’ whose distortion is greater than that of f by at most a constant factor. Bio: Sepideh Mahabadi is a research assistant professor at the Toyota Technological Institute at Chicago (TTIC). She received her PhD 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. Title: Non-Adaptive Adaptive Sampling in Turnstile Streams Abstract: Adaptive sampling is a useful algorithmic tool for data summarization problems in the cl Sepideh Mahabadi; Affiliations. 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).

View Sepideh Mahabadi’s professional profile on Relationship Science, the database of decision makers. Sepideh Mahabadi's 26 research works with 187 citations and 461 reads, including: Sampling a Near Neighbor in High Dimensions -- Who is the Fairest of Them All? Sepideh Mahabadi's 24 research works with 180 citations and 395 reads, including: Streaming Complexity of SVMs Affiliation: CSAIL MIT Sepideh Mahabadi is a postdoctoral research scientist with an appointment at the Simons Collaboration on Algorithms and Geometry based at Columbia University. More articles on Iranian successes in science competitions Sources: Press TV, MIT, Simons Foundation, stats.ioiinformatics.org (IOI 2007) Sepideh Mahabadi is a research assistant professor at the Toyota Technological Institute at Chicago (TTIC). She received her PhD from MIT, where she was advised by Piotr Indyk. She received her PhD from MIT, where she was advised by Piotr Indyk. Sepideh Mahabadi: Composable Core-sets for Diversity and Coverage Maximization, and Its Application in Diverse Near Neighbor Problem Wednesday, May 7, 2014 - 4:00pm to 5:00pm Location:
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Ebba Brahes Väg 5 C, 192 69 Sollentuna. Malak Haji Karami Mahabadi 66 år. Sörgården 153, 1002 18638 VALLENTUNA Sepideh Karami 44 år. Slalomvägen 17, 1202 12949 HÄGERSTEN. 070-851 Sepideh Gholian, journalist. Narges Mohammadi, människorättsaktivist.