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					Sepideh Mahabadi 
					  
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				 Research: 
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						My research is mostly focused on 
						Theoretical Foundations of Massive Data  including
						High Dimensional Computational Geometry; Streaming and Sub-linear Time Algorithms; Data Summarization;
						Graph Algorithms; Metric Embeddings; 
						as well as Societal Aspects of Algorithms including Diversity, Fairness,
						and Privacy.
				 
						Our paper Composable Core-sets for Diversity and Coverage Maximizationhas received the ACM PODS Alberto O. Mendelzon Test-of-Time Award 2024.
						Our paper Sampling Near Neighbors in Search for Fairnesshas appeared in Communications of the ACM 2022.
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				 Contact: 
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					 E-mail: [lastname] @ ttic.edu
				
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				 Internship at MSR: 
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				The Algorithms group at MSR hires summer interns each year. The deadline is normally end of November [the link will be posted here].
				If you are a strong TCS student and are interested to work on theoretical problems with me, please contact me.
				
				I have co-hosted the following great interns:
				Yaowei Long, (University of Michigan), 2025
				
				
				 Prasanna Ramakrishnan, (Standford), 2025
				
				
				 Mohammad Roghani, (Stanford), 2024
				
				
				 Sherry Sarkar, (CMU), 2024
				
				
				 Ce Jin, (MIT), 2023
				
				
				 Sandeep Silwal, (MIT), 2023
				
				
				 Shyam Narayanan, (MIT), 2022
				
				
				 Thuy (June) Duong Vuong, (Stanford), 2022 | 
	
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				 Academic Service: 
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				Program Committee:
				 FOCS 2025 ,
				 SODA 2025 ,
				 ICALP 2023 ,
				 STOC 2023 ,
				 WOLA 2022 ,
				 SoCG 2022 ,
				 HALG 2022 ,
				 STOC 2021 ,
				 SODA 2021 ,
				 RANDOM 2020 
				and ITCS 2019 ; 
				and Reviewer:  NeurIPS 2023,
				ICML 2018,
				and  NeurIPS 2017.
				
				Workshop Organization: 
					
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						Workshop on High-Dimensional and Complex Data Algorithms, Venice, May 12-15, 2025.
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						Extroverted Sublinear Algorithms Workshop, Simons Institute, June 17th-21st, 2024 as part of 
							 Sublinear Algorithms Program.
						- Sketching and Algorithm Design
							Workshop, Simons Institute, Oct 9th-13th, 2023 as part of 
							 
							Data Structures and Optimization for Fast Algorithms Program.
						-  WOLA 2020,
							virtual, July 20th-21st, 2020.
						- Recent Trends in TCS workshop, TTIC, January 31st, 2020.
					Member of the Diversity, Equity, and Inclusion Committee at TTIC, 2020-2021. | 
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				 Pre-prints: 
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					  The General Expiration Streaming Model: Diameter, k-Center, Counting, Sampling, and Friends ,
						Lotte Blank, Sergio Cabello, MohammadTaghi Hajiaghayi, Robert Krauthgamer, Sepideh Mahabadi, André Nusser, Jeff M. Phillips, Jonas Sauer 
						 arXiv.
						(pdf)
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				 Publications:  
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						Sublinear Metric Steiner Forest via Maximal Independent Set,
						Sepideh Mahabadi, Mohammad Roghani, Jakub Tarnawski, Ali Vakilian,
						 SODA 2026.
					
						Improved Algorithms for Fair Matroid Submodular Maximization,
						Sepideh Mahabadi, Sherry Sarkar, Jakub Tarnawski,
						 NeurIPS 2025.
					
						Streaming Algorithms for Network Design,
						Chandra Chekuri, Rhea Jain, Sepideh Mahabadi, Ali Vakilian,
						 APPROX 2025.
						(pdf)
					
						Graph-Based Algorithms for Diverse Similarity Search,
						Piyush Anand, Piotr Indyk, Ravishankar Krishnaswamy, Sepideh Mahabadi, Vikas C. Raykar, Kirankumar Shiragur, Haike Xu,
						ICML 2025.
						(pdf)
					  A 0.51-Approximation of Maximum Matching in Sublinear n1.5 Time,
						Sepideh Mahabadi, Mohammad Roghani, Jakub Tarnawski,
						 ICALP 2025.
					(pdf,
					 talk by Jakub)
					  Guessing Efficiently for Constrained Subspace Approximation,
						Aditya Bhaskara, Sepideh Mahabadi, Madhusudhan Pittu, Ali Vakilian, David Woodruff,
						 ICALP 2025.
					(pdf)
					  Sublinear Metric Steiner Tree via Improved Bounds for Set Cover,
						Sepideh Mahabadi, Mohammad Roghani, Jakub Tarnawski, Ali Vakilian,
						 ITCS 2025.
					(pdf,
					slides,
						 talk by Mohammad)	
					  Streaming Algorithms for Connectivity Augmentation,
						Ce Jin, Michael Kapralov, Sepideh Mahabadi, Ali Vakilian,
						 ICALP 2024.
						(pdf)
					   Efficiently Computing Similarities to Private Datasets,
						Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski 
						 ICLR 2024.
						(pdf)
					  Differentially Private Approximate Near Neighbor Counting in High Dimensions,
						Alexandr Andoni, Piotr Indyk, Sepideh Mahabadi, Shyam Narayanan,
						 NeurIPS 2023 (Spotlight).
						(pdf)
					  Core-sets for Fair and Diverse Data Summarization,
						Sepideh Mahabadi, Stojan Trajanovski,
						 NeurIPS 2023.
						(pdf)
					  Tight Bounds for Volumetric Spanners and Applications,
						Aditya Bhaskara, Sepideh Mahabadi, Ali Vakilian,
						 NeurIPS 2023.
						(pdf,
						 talk by Ali)
					  Composable Coresets for Determinant Maximization: Greedy is Almost Optimal,
						Siddharth Gollapudi, Sepideh Mahabadi, Varun Sivashankar
						 NeurIPS 2023.
						(pdf)
					 Improved Diversity Maximization Algorithms for Matching and Pseudoforest,
						Sepideh Mahabadi, Shyam Narayanan,
						 APPROX 2023.
						(pdf)
						
					  Approximation Algorithms for Fair Range Clustering,
						Sedjro Hotegni, Sepideh Mahabadi, Ali Vakilian,
						 ICML 2023.
						(pdf,
						 short talk by Ali)
					  Adaptive Sketches for Robust Regression with Importance Sampling,
						Sepideh Mahabadi, David Woodruff, Samson Zhou,
						 RANDOM 2022.
						(pdf,
						 talk by Samson)
					
						 Sampling a Near Neighbor in High Dimensions--Who is the Fairest of Them All?
						Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri,
						(Extended version, based on our paper in NeurIPS '19 and the paper of Aumüller, Pagh, and Silvestri in PODS '20)
						CACM 2022, 
						TODS 2022,
						SIGMOD Record 2021.
						(pdf,
						slides)
					  Two-sided Kirszbraun Theorem,
						Arturs Backurs, Sepideh Mahabadi, Konstantin Makarychev, Yury Makarychev,
						 SoCG 2021.
						(pdf,
						 slides)
					  Towards Better Approximation of Graph Crossing Number,
						Julia Chuzhoy, Sepideh Mahabadi, Zihan Tan,
						 FOCS 2020.
						(pdf,
						
						talk by Zihan)
					  Streaming Complexity of SVMs,
						Alexandr Andoni, Collin Burns, Yi Li, Sepideh Mahabadi, David Woodruff,
						 APPROX 2020.
						(pdf,
						 talk by Collin)
					  Individual Fairness for k-Clustering,
						Sepideh Mahabadi, Ali Vakilian,
						 ICML 2020.
						(pdf,
						 talk by Ali)
					  Non-Adaptive Adaptive Sampling on Turnstile Streams, 
						Sepideh Mahabadi, Ilya Razenshteyn, David Woodruff, Samson Zhou,
						STOC 2020.
						(pdf,
						slides)
					  Composable Core-sets for Determinant Maximization Problems via Spectral Spanners, 
						Piotr Indyk, Sepideh Mahabadi, Shayan Oveis Gharan, Alireza Rezaei,
						SODA 2020.
						(pdf,
						slides)
					  Near Neighbor: Who is the Fairest of Them All?,
						Sariel Har-Peled, Sepideh Mahabadi, 
						NeurIPS 2019.
						(pdf,
						 slides)
					  Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm,  
						Piotr Indyk, Sepideh Mahabadi, Shayan Oveis Gharan, Alireza Rezaei,
						ICML 2019 (Long Talk).
						(pdf,
						slides)
					  Approximate Sparse Linear Regression, 
						Sariel Har-Peled, Piotr Indyk, Sepideh Mahabadi,
						 ICALP 2018.
						(pdf,
						slides)
					  Nonlinear Dimension Reduction via Outer Bi-Lipschitz Extensions, 
						Sepideh Mahabadi, Konstantin Makarychev, Yury Makarychev, Ilya Razenshteyn,
						 STOC 2018.
						(pdf,
						slides)
					  Set Cover in Sub-linear Time,  
						Piotr Indyk, Sepideh Mahabadi, Ronitt Rubinfeld,  Ali Vakilian, Anak Yodpinyanee,
						 SODA 2018.
						(pdf,
						slides)
					  Fractional Set Cover in the Streaming Model,  
						Piotr Indyk, Sepideh Mahabadi, Ronitt Rubinfeld, Jonathan Ullman,  Ali Vakilian, Anak Yodpinyanee,
						APPROX 2017.
						(pdf,
						slides)
					  Proximity in the Age of Distraction: Robust Approximate Nearest Neighbor Search,
						 Sariel Har-Peled, Sepideh Mahabadi, 
						 SODA 2017.
						(pdf,
						slides)
					  Towards Tight Bounds for the Streaming Set Cover Problem,  
						Sariel Har-Peled, Piotr Indyk, Sepideh Mahabadi, Ali Vakilian,
						PODS 2016.
						(pdf, 
						slides)
					  Simultaneous Nearest Neighbor Search, 
						Piotr Indyk, Robert Kleinberg, Sepideh Mahabadi, Yang Yuan, 
						SoCG 2016.
						(pdf,
						slides)
					  Approximate Nearest Line Search in High Dimensions, 
						 Sepideh Mahabadi, 
						 SODA 2015.
						(pdf,
						short slides,
						long slides)
					  On Streaming and Communication Complexity of the Set Cover Problem,  
						Erik Demaine, Piotr Indyk, Sepideh Mahabadi, Ali Vakilian,
						 DISC 2014.
						(pdf)
					 Composable Core-sets for Diversity and Coverage Maximization, 
						Piotr Indyk, Sepideh Mahabadi, Mohammad Mahdian, Vahab Mirrokni, 
						PODS 2014 (Test-of-Time Award).
						(pdf, slides)
					 Diverse Near Neighbor Problem, 
						Sofiane Abbar, Sihem Amer-Yahia, Piotr Indyk, Sepideh Mahabadi, Kasturi Varadarajan, 
						SoCG 2013.
						(pdf , slides)
					 Real-time Recommendation of Diverse Related Articles, 
						Sofiane Abbar, Sihem Amer-Yahia, Piotr Indyk, Sepideh Mahabadi, 
						WWW 2013.
						(pdf)
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				Undergraduate  Publications:
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					 Construction of Random Perfect Phylogeny Matrix,
					M. Sadeghi, H. Pezeshk, C. Eslahchi, S. Ahmadian, S. Mahabadi,
					Advances and Applications in Bioinformatics and Chemistry 2010. An Algorithm for Construction of all Perfect Phylogeny Matrices, 
					H. Mirzaei, S. Ahmadian, S. Mahabadi, M. Sadeghi M., C. Eslahchi, H. Pezeshk,
					Communications in Mathematical and in Computer Chemistry (MATCH) 2009. | 
	
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				 Master's Thesis:  (MIT, June 2013)
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				Approximate Nearest Neighbor And Its Many Variants (pdf )
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				 PhD Thesis:   (MIT, Sept. 2017)
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				Sub-linear Algorithms for Massive Data Problems (pdf )
				
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						Recent Advances in Diversity Maximization in the Offline and Composable Coreset Models 
						
							Dynamic Graphs and Algorithm Design Workshop at Simons Institute, September 2023
						Sampling a Near Neighbor in High Dimensions — Who is the Fairest of Them All? 
						
							 RAISE (Responsible AI and Software Experiences) at UW, November 2022Sublinear Algorithms Workshop, MIT, August 2022
						Non-Adaptive Adaptive Sampling in Turnstile Streams
						
							IDEAL Workshop on High-Dimensional Geometry and Analysis, May 2022 Michigan-Purdue Theory Seminar, September 2020TCS+ Talk, April 2020
						Two-sided Kirszbraun Theorem
						Workshop on Algorithms for Large Data, August 2021
					
					 Diversity and Fairness in Data Summarization Algorithms
						
							
								ICPCU Alumni Lecture Series, June 2021
							
								IPM Combinatorics and Computing Conference (IPMCCC), May 2021
							
								EPFL, IC School, April 2021
							
								Columbia University, CS Department, April 2021
							
								University of Michigan, CSE Division, March 2021
							
								Google Research, March 2021
							
								CMU, CS Department, March 2021
							
								University of Maryland, CS Department, March 2021
							
								Purdue University, CS Department, February 2021
							
								Georgia Institute of Technology, School of CS, February 2021
							
								MSR Redmond, MLO and Algorithms groups, February 2021
							 University of Waterloo, CS Department, Januray 2021
							
						Determinant Maximization over Large Data Sets
						Sub-linear Reading Group, MIT, September 2020
					
						Composable Core-sets for the Determinant Maximization Problem 
						Rising Stars Talk, TCS Women Spotlight Workshop, June 2020
					
						Composable Core-sets for Determinant Maximization Problems via Spectral Spanners
						
							CMU, November 2019 UIUC, February 2019 Junior Theorists Workshop, Northwestern Univeristy, November 2018
						Diversity Maximization over Large Data Sets
						
							 The Statistics and Data Sciences (SDS) Seminar Series, UT Austin, October 2022 UCSB Theory Colloquium, April 2022 MSR Redmond, October 2019Optimization Methods in Computer Vision and Image Processing workshop, ICERM, May 2019Midwest Theory Day, Purdue University, April 2019 
						Nonlinear Dimension Reduction via Outer Bi-Lipschitz Extension
						
							Metric Embeddings & Dimensionality Reduction workshop, Northwestern Univeristy, May 2019University of Chicago, October 2018 Google New York, July 2018
						Set Cover in Sub-linear Time
						
							Workshop on Local Algorithms (WOLA), MIT, June 2018Rutgers Univeristy/DIMCAS, May 2018 Midwest Theory Day, TTIC, April 2018New York Area Theory Day, NYU, December 2017
						Fractional Set Cover in the Streaming Model
						Simons Collaboration on Algorithms & Geometry, February 2018
					
						Approximate Nearest Neighbor and Its Many Variants
						
							University of Washington,  April 2017IBM Watson, December 2016
						Towards Tight Bounds for the Streaming Set Cover Problem
						University of Toronto,  November 2015
					 
						Approximate Nearest Line Search in High Dimensions
						Brown University,  November 2014
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						 Instructor: Taught a course on 
						 
						Algorithms for Massive Data in Spring 2021 at TTIC.
					
						 Guest Lecturer for Approximation Algorithms taught by Julia Chuzhoy, TTIC, Fall 2019.
					
						 Teaching Assistant
						
						
						Introduction to Algorithms, (Spring 2017, MIT) Advanced Algorithms, (Spring 2013, MIT) Design and Analysis of Algorithms (Spring 2010, Sharif UT) Theory of Languages and Machines (Fall 2010, Sharif UT)Introduction to Programming (Fall 2010,  Sharif UT)
						 Olympiad Camps:  Teaching special topics in Computer Science at the training camps of Iranian National Olympiad
						in Informatics (NIOI), 2008-2009.
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				 Internships & Visits
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						TTIC, Research Internship, Summer 2016, Chicago
						
						- Mentors:  Julia Chuzhoy 
						and  Yury Makarychev  
					
						UIUC, Research Visit, Summer 2015, Urbana Champaign
						- Mentor:  Sariel Har-Peled 
					
						Yahoo Labs!, Research Internship, August 2014, New York 
						- Mentors: Howard Karloff and 
						 Edo Liberty 
					 
						Google, Research Internship, Summer 2013, Mountain View 
						- Mentor: 
						Mohammad Mahdian 
					
						Advanced Digital Sciences Center, Intern, Summer 2010, Singapore 
						- Mentors:   Minh N. Do , Jiangbo Lu, and  Marianne Winslett .
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				 Olympiad and Programming Contests
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						Gold Medal in International Olympiad in Informatics (IOI) 2007. 
						
						
						 Only female contestant to win a gold medal at IOI 2007. First Iranian female to win a gold medal ever at IOI.
						ACM-ICPC World Finals 2011, Ranked 13th.
						
					
						ACM-ICPC World Finals 2013, Ranked 14th.
						
						
						Representing MIT.Only female contestant among all participants from US.
						Member of the Host Scientific Committee in International Olympiad in Informatics (IOI) 2017.
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