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.
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.
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. This is a classic supervised learning problem that has drawn lots of attention, including for developing fast algorithms for solving the problem approximately. One of the most widely used algorithms for approximately optimizing the SVM objective is Stochastic Gradient Descent (SGD), which requires Reminder Subject: TALK: THESIS DEFENSE: Sepideh Mahabadi: Sub-linear Algorithms for Massive Data Problems Abstract: The recent availability of massive data sets has had a significant impact on the design of algorithms. This has led to the emergence of new computational models that capture various aspects of massive data computation.
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.
%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, Ilya Razenshteyn, David Woodru , Samson Zhou, Non-Adaptive Adap-tive Sampling on Turnstile Streams. 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.
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. Affiliation: CSAIL MIT 2021-02-04 · Sepideh Mahabadi is a research assistant professor at the Toyota Technological Institute at Chicago (TTIC).
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from MIT, where she was advised by Piotr Indyk. For a year, she was a postdoctoral research scientist at Simons Collaboration on …
Bio: Sepideh Mahabadi is a research assistant professor at the Toyota Technological Institute at Chicago (TTIC).
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Abs. Rel. Abs. Rel. 2007 · Iran · 100 · 40 · 100 · 100 · 64 · 5, 409, 68.17% Sepideh Mahabadi. MathSciNet. 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. OR AND NOT 1.
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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)
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. This is a classic supervised learning problem that has drawn lots of attention, including for developing fast algorithms for solving the problem approximately. One of the most widely used algorithms for approximately optimizing the SVM objective is Stochastic Gradient Descent (SGD), which requires Reminder Subject: TALK: THESIS DEFENSE: Sepideh Mahabadi: Sub-linear Algorithms for Massive Data Problems Abstract: The recent availability of massive data sets has had a significant impact on the design of algorithms. This has led to the emergence of new computational models that capture various aspects of massive data computation. 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 » Searching and summarization are two of the most fundamental tasks in massive data analysis.