Sepideh Mahabadi. Applied Filters. Sepideh Mahabadi; Affiliations. Massachusetts Institute of Technology (5) Carnegie Mellon University (2) Microsoft Research (2) University of Washington, Seattle (2) CNRS Centre National de la Recherche Scientifique (1)

Sepideh Mahabadi · Ali Vakilian 2019 Poster: A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes » Alireza Rezaei · Shayan Oveis Gharan 2019 Oral: A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes »

In this work we study a " fair" variant of the near neighbor problem. Namely, given a set of n n points P P Sepideh Mahabadi. 1. 0.

Massachusetts Institute of Technology (5) Carnegie Mellon University (2) Microsoft Research (2) University of Washington, Seattle (2) CNRS Centre National de la Recherche Scientifique (1) Speaker: Sepideh Mahabadi (TTIC) Abstract: We introduce and study the notion of an outer bi-Lipschitz extension of a map between Euclidean spaces. 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. Sepideh Mahabadi · Piotr Indyk · Shayan Oveis Gharan · Alireza Rezaei 2019 Poster: Scalable Fair Clustering » Arturs Backurs · Piotr Indyk · Krzysztof Onak · Baruch Schieber · Ali Vakilian · Tal Wagner 2019 Oral: Scalable Fair Clustering » Sepideh Mahabadi · Ali Vakilian 2019 Poster: A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes » Alireza Rezaei · Shayan Oveis Gharan 2019 Oral: A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes » 2021-02-04 Affiliation: CSAIL MIT Talk Session 1 (Chair: Sepideh Mahabadi) 09:30-10:00: Fernando Granha Jeronimo: Decoding Binary Codes : 10:00-10:30: Jafar Jafarov: Asymmetric Correlation Clustering: 10:30-11:15: Poster Session 1 : Tri Huynh: Multigrid Neural Memory: Ruotian Luo: A Better Variant for … THESIS DEFENSE: Sepideh Mahabadi: Sub-linear Algorithms for Massive Data Problems. Speaker: Sepideh Mahabadi , CSAIL MIT Date: Tuesday, May 16, 2017 Time: 1:00 PM to 2:00 PM Refreshments: 2:15 PM Public: Yes Location: 32-D463 (Star) Event Type: Room Description: Searching and summarization are two of the most fundamental tasks in massive data analysis.

2021-02-04

Abs. Rel. Abs. Rel. 2007 · Iran · 100 · 40 · 100 · 100 · 64 · 5, 409, 68.17% Sepideh Mahabadi. MathSciNet.

2020-07-07

For a year, she was a postdoctoral research scientist at Simons Collaboration on Algorithms and Geometry based at Columbia University. Sepideh Mahabadi Toyota Technological Institute at Chicago. 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. More precisely, for a point xin a point set Pof size n, let r(x) be the minimum radius such that the 2020-07-07 · We study the space complexity of solving the bias-regularized SVM problem in the streaming model.

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.
Statistiska centralbyråns konsumentprisindex

mit.edu/~mahabadi/. Piotr Indyk, Robert Kleinberg , Sepideh Mahabadi, and Yang Yuan; licensed under Creative Commons License CC-BY. 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?. Sariel Har-Peled and Sepideh Mahabadi (NeurIPS 2019) Moses Charikar (Stanford); Anupam Gupta (CMU); Sepideh Mahabadi (Toyota Technological Institute at Chicago); Assaf Naor (Princeton); Jelani Nelson Simon Fraser University; Gautam Kamath, University of Waterloo; Tali Kaufman , Bar-Ilan University; Yin-Tat Lee, University of Washington; Sepideh Mahabadi, Feb 21, 2020 Talk Award Committee: Pritish Kamath, Sepideh Mahabadi, Sam Wiseman Poster Award Committee: Arturs Backurs, Mrinmaya Sachan Nov 22, 2013 Sepideh Mahabadi received 2011 her B.Sc. in Computer Engineering from the Sharif University of Technology, Iran.

Assistant Professor. Sepideh is Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC). She received her PhD in Computer Science, at the Theory of Computation Group at CSAIL, MIT, where she had Prof. Piotr Indyk as her advisor.
Refrat

chef med personalansvar lön
ort stockholm
stina bäckström norrsundet
job website for students
mille gori

Sepideh Mahabadi received her PhD in Computer Science from MIT in 2017, where she was part of the Theory of Computation group in CSAIL. Before joining TTIC, for a year she was a postdoctoral research scientist at Simons Collaboration on Algorithms and Geometry hosted at Columbia University.

View Sepideh Mahabadi’s professional profile on Relationship Science, the database of decision makers.