David P. Woodruff Professor Theory Group, Department of Computer Science, Carnegie Mellon University Research interests : algorithms, data streams, machine learning, numerical linear algebra, sketching, and sparse recovery Contact: dwoodruf (at) cs (dot) cmu (dot) edu    I am also part-time in the Google Omega team. For Google-related matters email woodruffd (at) google (dot) com We have a preprint: Accelerating Scientific Research with Gemini: Case Studies and Common Techniques arXiv We ran an LLM-based feedback experimental program for STOC here . Here is a Google Research blog post with the results.

Book

• Sketching as a Tool for Numerical Linear Algebra, Foundations and Trends in Theoretical Computer Science, vol 10, issue 1-2, pp. 1-157, 2014. You can download a free copy (for personal use only) here

Simons Institute Foundations of Data Science: program page

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Teaching at CMU:

I am honored to receive the Herbert Simon Award for teaching in computer science

Fall 2017: 15859 - Algorithms for Big Data
Spring 2018: 15451/651 - Algorithms
Spring 2019: 15451/651 - Algorithms
Fall 2019: 15859 - Algorithms for Big Data
Spring 2020: 15451/651 - Algorithms
Fall 2020: 15859 - Algorithms for Big Data
Spring 2021: 15451/651 - Algorithms
School of Computer Science, Executive Education, Online Course Algorithms and Data Structures
Fall 2021: 15451/651 - Algorithms
Fall 2021: 15859 - Algorithms for Big Data
Fall 2022: 15451/651 - Algorithms
Fall 2022: 15859 - Algorithms for Big Data
Spring 2024: 15451/651 - Algorithms
Spring 2024: 15851 - Algorithms for Big Data
Spring 2025: 15451/651 - Algorithms
Spring 2025: 15851- Algorithms for Big Data
Spring 2026: 15251 - Great Ideas in Theoretical Computer Science

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My Amazing Students and Postdocs

• Students:
  Ainesh Bakshi, co-advised with Pravesh Kothari, Ainesh graduated, was a postdoc at MIT, and is an assistant professor at NYU
  Rajesh Jayaram , Raj has graduated, and has joined Google Research NYC as a Research Scientist
  Praneeth Kacham , Praneeth has graduated, and has joined Google Research NYC as a Resesarch Scientist
  Honghao Lin
  Hoai-An Nguyen co-advised with Yang Liu
  Hai Pham co-advised with Barnabas Poczos. Hai has graduated and joined Reka AI
  Madhusudhan Pittu co-advised with Anupam Gupta
  Kijun Shin
  Taisuke Yasuda , Tai has graduated, and has joined the Voleon Group as a Member of the Research Staff
  Hongyang Zhang, co-advised with Nina Balcan. Hongyang has graduated, was a postdoc at TTIC, and is an assistant professor at the University of Waterloo
  
  
• Postdocs:
  William Swartworth Will will join the Voleon Group as a Member of the Research Staff
  Samson Zhou Samson was a postdoc at UC Berkeley/Rice and will join Texas A & M
  

Publications

2026

• STOC, Adversarial Robustness on Insertion-Deletion Streams
  with Elena Gribelyuk, Honghao Lin, Huacheng Yu, and Samson Zhou
  
• STOC, Combinatorial Optimization using Comparison Queries
  with Vincent Cohen-Addad, Tommaso d'Orsi, Anupam Gupta, Guru Guruganesh, Euiwoong Lee, Renato Paes Leme, Debmalya Panigrahi, Madhusudhan Pittu, and Jon Schneider
  
• ICLR, Distributed Algorithms for Euclidean Clustering
  with Vincent Cohen-Addad, Liudeng Wang, and Samson Zhou
  
• ICLR, Nearly Space-Optimal Graph and Hypergraph Sparsification in Insertion-Only Data Streams
  with Vincent Cohen-Addad, Shenghao Xie, and Samson Zhou
  
• ICLR, SVD Provable Denoises Nearest Neighbor Data
  with Ravi Kannan and Kijun Shin
  
• ICLR, Learning-Augmented Moment Estimation on Time-Decay Models
  with Soham Deepak Nagawanshi, Shalini Panthangi, Chen Wang, and Samson Zhou
  
• ICLR, Better Bounds for the Distributed Experts Problem
  with Samson Zhou
  
• ICLR, Consistent Low Rank Approximation
  with Samson Zhou
  
• SODA, Online Learning with Limited Information in the Sliding Window Model
  with Vladimir Braverman, Sumegha Garg, Chen Wang, and Samson Zhou
  
• SODA, L_p Sampling in Distributed Data Streams with Applications to Adversarial Robustness
  with Honghao Lin, Zhao Song, Shenghao Xie, and Samson Zhou
  
• PODS, Finding Heavy-Hitters with Optimal State Changes
  with William Swartworth
  

2025

• NeurIPS, Nearly-Linear Time and Massively Parallel Algorithms for k-Anonymity
  with Kevin Aydin, Honghao Lin, and Peilin Zhong
  
• NeurIPS, Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graph
  with Gautam Kamath, Alireza F. Pour, and Matthew Regehr
  
• FOCS, Perfect L_p Sampling with Polylogarithmic Update Time
  with William Swartworth and Samson Zhou
  
• FOCS, Root Ridge Leverage Score Sampling for $\ell_p$ Subspace Approximation
  with Taisuke Yasuda
  
• APPROX, Multipass Linear Sketches for Geometric LP-Type Problems
  with Efe Cekirge and William Gay
  
• ICML, On Fine-Grained Distinct Element Estimation
  with Ilias Diakonikolas, Daniel Kane, Jasper C.H. Lee, Thanasis Pittas, and Samson Zhou
  
• ICML, Maximum Coverage in Turnstile Streams
  with Aline Ene, Alessandro Epasto, Vahab Mirrokni, Hoai-An Nguyen, Huy Nguyen, and Peilin Zhong
  
• ICML, On Differential Privacy for Adaptively Solving Search Problems via Sketching
  with Shiyuan Feng, Ying Feng, George Li, Zhao Song, and Lichen Zhang
  Selected for Oral Presentation (top 1% of submissions)
• ICML, Robust Sparsification via Sensitivity
  with Chansophea Wathanak In, Yi Li, and Xuan Wu
  
• ICML, Understanding the Kronecker Matrix-Vector Complexity of Linear Algebra
  with Raphael Meyer and William Swartworth
  
• ICALP, Guessing Efficiently for Constrained Subspace Approximation
  with Aditya Bhaskara, Sepideh Mahabadi, Madhusudhan Pittu, and Ali Vakilian
  Full version on arXiv
• ICALP, Tight Bounds for Heavy-Hitters and Moment Estimation in the Sliding Window Model
  with Shiyuan Feng and William Swartworth
  Full version on arXiv
  
• PODS, Perfect Sampling in Turnstile Streams Beyond Small Moments
  with Shenghao Xie and Samson Zhou
  Full version on arXiv
  
• SoCG, Range Counting Oracles for Geometric Problems
  with Anne Driemel, Morteza Monemizadeh, Eunjin Oh, and Frank Staals
  Full version on arXiv
  
• STOC, Lifting Linear Sketches: Optimal Bounds and Adversarial Robustness
  with Elena Gribelyuk, Honghao Lin, Huacheng Yu, and Samson Zhou
  Full version on arXiv
  
• ICLR, Streaming Algorithms for lp Flows and lp Regression
  with Amit Chakrabarti, Jeffrey Jiang, and Taisuke Yasuda
  Full version on OpenReview
  Seleted for Spotlight Presentation
  
• ICLR, Time, Space, and Streaming Efficient Algorithm for Heavy Attentions
  with Ravindran Kannan, Chiranjib Bhattacharyya, and Praneeth Kacham
  Full version on arXiv
  
• ICLR, Beyond Worst-Case Dimensionality Reduction for Sparse Vectors
  with Sandeep Silwal and Qiuyi Zhang
  Full version on arXiv
  
• ITCS, Space Complexity of Minimum Cut Problems in Single-Pass Streams
  with Matthew Ding, Alexandro Garces, Jason Li, Honghao Lin, Jelani Nelson, and Vihan Shah
  Full version on arXiv
  
• SODA, Tight Sampling Bounds for Eigenvalue Approximation
  with William Swartworth
  Full version on arXiv
  
• iScience, Learning-Augmented Sketching Offers Improved Performance for Privacy Preserving and Secure GWAS
  with Junyan Xu, Kaiyuan Zhu, Jieling Cai, Can Kockan, Natnatee Dokmai, Hyunghoon Cho, and Cenk Sahinalp
  Full version on biorxiv
  

2024

• NeurIPS, Communication Bounds for the Distributed Experts Problem
  with Zhihao Jia, Qi Pang, Trung Tran, Zhihao Zhang, and Wenting Zheng
  Full version on arXiv
  
• NeurIPS, Approximating the Top Eigenvector in Random Order Streams
  with Praneeth Kacham
  Selected for Spotlight Presentation
  Full version on arXiv
  
• NeurIPS, Even Sparser Graph Transformers
  with Hamed Shirzad, Honghao Lin, Balaji Venkatachalam, Ameya Velingker, and Danica J. Sutherland
  Full version on arXiv
  
• NeurIPS, On Socially Fair Low-Rank Approximation and Column Subset Selection
  with Zhao Song, Ali Vakilian, and Samson Zhou
  Full verison on arXiv
  
• NeurIPS, John Ellipsoids via Lazy Updates
  with Taisuke Yasuda
  Full version on arXiv
  
• NeurIPS, Adversarially Robust Dense-Sparse Tradeoffs via Heavy-Hitters
  with Samson Zhou Full version on arXiv
  
• EMNLP, GRASS: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients
  with Aashiq Muhamed, Oscar Li, Mona T. Diab, and Virginia Smith
  Full version on arXiv
  
• FOCS, A Strong Separation for Adversarially Robust $\ell_0$ Estimation for Linear Sketches
  with Elena Gribelyuk, Honghao Lin, Huacheng Yu, and Samson Zhou
  Full version on arXiv
  
• RANDOM, Faster Algorithms for Schatten-p Low Rank Approximation
  Full version on arXiv
  with Praneeth Kacham
  
• ICML, Sensitivity Sampling for Coreset-Based Data Selection
  Full version on arXiv
  with Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome, Vahab Mirrokni, David Saulpic, and Michael Wunder
  
• ICML, High-Dimensional Geometric Streaming for Nearly Low Rank Data
  Full version on arXiv
  with Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, and Peilin Zhong
  
• ICML, Fast White-Box Adversarial Streaming Without a Random Oracle
  Full version on arXiv
  with Ying Feng and Aayush Jain
  
• ICML, Learning Multiple Secrets in Mastermind
  Full version on arXiv
  with Milind Prabhu
  
• ICML, Fast Sampling-Based Sketches for Tensors
  Full version on arXiv
  with William Swartworth
  Selected for Spotlight Presentation
  
• ICML, Dimension-Free Coresets for Multiple $\ell_p$ Regression
  Full version on arXiv
  with Taisuke Yasuda
  
• ICML, Reweighted Solutions for Weighted Low Rank Approximation
  Full version on arXiv
  with Taisuke Yasuda
  
• STOC, A New Information Complexity Measure for Multi-Pass Streaming with Applications
  Full version on arXiv
  with Mark Braverman, Sumegha Garg, Qian Li, Shuo Wang, and Jiapeng Zhang
  
• STOC, Optimal Communication Bounds for Classic Functions in the Coordinator Model and Beyond
  Full version on arXiv
  with Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, and Peilin Zhong
  
• STOC, Improving the Bit Complexity of Communication for Distributed Convex Optimization
  Full version on arXiv
  with Mehrdad Ghadiri, Yin Tat Lee, Swati Padmanabhan, William Swartworth, and Guanghao Ye
  
• ICLR, HyperAttention: Long-context Attention in Near-Linear Time
  Full version on arXiv
  with Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, and Amir Zandieh
  
• ICLR, Optimal Sketching for Residual Error Estimation for Matrix and Vector Norms
  Available here
  with Yi Li and Honghao Lin
• ICLR, Adaptive Regret for Bandits Made Possible: Two Queries Suffice
  Available here
  with Zhou Lu, Qiuyi Zhang, Xinyi Chen, Fred Zhang, and Elad Hazan
  
• ITCS, Universal Matrix Sparsifiers and Fast Deterministic Algorithms and Fast Deterministic Algorithms for Linear Algebra
  Full version on arXiv
  with Rajarshi Bhattacharjee, Gregory Dexter, Cameron Musco, Archan Ray, and Sushant Sachdeva
  
• ITCS, Space-Optimal Profile Estimation in Data Streams with Applications to Symmetric Functions
  Full version on arXiv
  with Justin Y. Chen and Piotr Indyk
  
• ITCS, Near-Linear Time and Fixed-Parameter Tractable Algorithms for Tensor Decompositions
  Full version on arXiv
  with Arvind V. Mahankali and Ziyu Zhang
  
• PODS, Tight Lower Bounds for Directed Cut Sparsification and Distributed Min-Cut
  Full version on arXiv
  with Yu Cheng, Max Li, Honghao Lin, Zi-Yi Tai, and Jason Zhang
  
• PODS, Streaming Algorithms with Few State Changes
  Full version on arXiv
  with Rajesh Jayaram and Samson Zhou
  

2023

• NeurIPS, Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming
  Full version on arXiv
  with Gregory Dexter, Petros Drineas, and Taisuke Yasuda
  
• NeurIPS, Lower Bounds on Adaptive Sensing for Matrix Recovery
  Full version on arXiv
  with Praneeth Kacham
  
• NeurIPS, Computing Approximate Sensitivities
  Full version on arXiv
  with Swati Padmanabhan and Qiuyi Zhang
  
• NeurIPS, Hardness of Low Rank Approximation of Entrywise Transformed Matrix Products
  Full version on arXiv
  with Tamas Sarlos, Xingyou Song, and Qiuyi Zhang
  
• NeurIPS, On Robust Streaming for Learning with Experts: Algorithms and Lower Bounds
  Full version on arXiv
  with Fred Zhang and Samson Zhou
  
• NeurIPS, Near-Optimal k-Clustering in the Sliding Window Model
  Full version on arXiv
  with Peilin Zhong and Samson Zhou
  
• FOCS, Streaming Euclidean $k$-median and $k$-means with $o(\log n)$ Space
  Full version on arXiv
  with Vincent Cohen-Addad and Samson Zhou
  
• FOCS, Pseudorandom Hashing for Space-bounded Computation with Applications in Streaming
  Full version on arXiv
  with Praneeth Kacham, Rasmus Pagh, and Mikkel Thorup
  
• COLT, $\ell_p$-Regression in the Arbitrary Partition Model of Communication
  Full version on arXiv
  with Yi Li and Honghao Lin
  
• ICML, Improved Algorithms for White-Box Adversarial Streams
  Full version on arXiv
  with Ying Feng
  
• ICML, Fast $(1+\varepsilon)$-Approximation Algorithms for Binary Matrix Factorization
  Full version on arXiv
  with Ameya Velingker, Maximilian Votsch, and Samson Zhou
  
• ICML, Sharper Bounds for $\ell_p$ Sensitivity Sampling
  with Taisuke Yasuda
  Full version on arXiv
  Selected for short live presentation
  
• STOC, Optimal Eigenvalue Approximation via Sketching
  Full version on arXiv
  with William Swartworth
  
• STOC, New Subset Selection Algorithms for Low Rank Approximation: Offline and Online
  Full version on arXiv
  with Taisuke Yasuda
  
• Eurocrypt, On Differential Privacy and Adaptive Data Analysis with Bounded Space
  Full version on arXiv
  with Itai Dinur, Uri Stemmer, and Samson Zhou
  
• ICLR, Learning the Positions in CountSketch
  Full version on openReview
  with Yi Li, Honghao Lin, Simin Liu, and Ali Vakilian
  Selected as a Notable-top-25% paper
  
• ICLR, Robust Algorithms on Adaptive Inputs from Bounded Adversaries
  Full version on arXiv
  with Yeshwanth Cherapanamjeri, Sandeep Silwal, Fred Zhang, Qiuyi Zhang, and Samson Zhou
  
• ICLR, Almost Linear Constant-Factor Sketching for l1 and Logistic Regression
  Full version on arXiv
  with Alexander Munteanu and Simon Omlor
  
• AISTATS, Optimal Sketching Bounds for Sparse Linear Regression
  Full version on arXiv
  with Tung Mai, Alexander Munteanu, Cameron Musco, Anup Rao, and Chris Schwiegelshohn
  
• ITCS, Recovery from Non-Decomposable Distance Oracles
  Full version on arXiv
  with Zhuangfei Hu, Xinda Li, Hongyang Zhang, and Shufan Zhang
  
• SODA, Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time
  Full version on arXiv
  with Yeshwanth Cherapanamjeri, Sandeep Silwal, and Samson Zhou
  
• SODA, The $\ell_p$-Subspace Sketch Problem in Small Dimensions with Applications to Support Vector Machines
  Full version on arXiv
  with Yi Li and Honghao Lin
  
• SODA, Near-Linear Sample Complexity for $L_p$ Polynomial Regression
  Full version on arXiv
  with Raphael A. Meyer, Cameron Musco, Christopher Musco, and Samson Zhou
  
• SODA, Online Lewis Weight Sampling
  Full version on arXiv
  with Taisuke Yasuda
  Invited to the special issue for SODA, 2023

2022

• NeurIPS, Optimal Query Complexities for Dynamic Trace Estimation
  Full version on arXiv
  with Fred Zhang and Qiuyi Zhang
  
• FOCS, Active Linear Regression for lp Norms and Beyond
  Full version on arXiv
  with Cameron Musco, Christopher Musco, and Taisuke Yasuda
  
• FOCS, Testing Positive Semidefiniteness Using Linear Measurements
  Full version on arXiv
  with Deanna Needell and William Swartworth
  
• FOCS, High-Dimensional Geometric Streaming in Polynomial Space
  Full version on arXiv
  with Taisuke Yasuda
  
• RANDOM, Streaming Algorithms with Large Approximation Factors
  Full version on arXiv
  with Yi Li, Honghao Lin, and Yuheng Zhang
  
• RANDOM, Adaptive Sketches for Robust Regression with Importance Sampling
  Full version on arXiv
  with Sepideh Mahabadi and Samson Zhou
  
• ICML, Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra (to appear)
  Full version on arXiv
  with Nadiia Chepurko, Ken Clarkson, Lior Horesh, and Honghao Lin
  
• ICML, Sketching Algorithms and Lower Bounds for Ridge Regression (to appear)
  Full version on arXiv
  with Praneeth Kacham
  
• ICML, Learning Augmented Binary Search Trees (to appear)
  Full version on arXiv
  with Honghao Lin and Tian Luo
  
• ICML, Bounding the Width of Neural Networks via Coupled Initialization - A Worst Case Analysis (to appear)
  Full version on arXiv
  with Alexander Munteanu, Simon Omlor, and Zhao Song
  
• ICML, Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time (to appear)
  Full version on arXiv
  with Amir Zandieh
  
• PODS, The White-Box Adversarial Data Stream Model
  Full version on arXiv
  with Miklos Ajtai, Vladimir Braverman, T.S. Jayram, Sandeep Silwal, Alec Sun, and Samson Zhou
• STOC, Low-Rank Approximation with $1/\epsilon^{1/3}$ Matrix-Vector Products
  Full version on arXiv
  with Ainesh Bakshi and Ken Clarkson
  
• STOC, Memory Bounds for the Experts Problem
  with Vaidehi Srinivas, Ziyu Xu, and Samson Zhou
  Full version on arXiv
  
• ICLR, Triangle and Four Cycle Counting with Predictions in Graph Streams
  Full version on arXiv
  with Justin Y Chen, Talya Eden, Piotr Indyk, Honghao Lin, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner, and Michael Zhang
  
• ICLR, Learning-Augmented $k$-Means Clustering
  Full version on arXiv
  with Jon Ergun, Zhili Feng, Sandeep Silwal, and Samson Zhou
  Selected for Spotlight Presentation
  
• ICLR, Fast Regression for Structured Inputs
  Full version on arXiv
  with Raphael A. Meyer, Cameron Musco, Christopher Musco, and Samson Zhou
  
• RECOMB, A Fast, Provably Accurate Approximation Algorithm for Sparse Principal Component Analysis Reveals Human Genetic Variation Across the World
  Full version on bioRxiv
  with Agniva Chowdhury, Aritra Bose, Samson Zhou, and Petros Drineas
  
• ITCS, An Efficient Semi-Streaming PTAS for Tournament Feedback Arc Set with Few Passes
  Full version on arXiv
  with Anubhav Baweja and Justin Jia
  
• ITCS, Noisy Boolean Hidden Matching with Applications
  Full version on arXiv
  with Michael Kapralov, Amulya Musiplata, Jakab Tardos, and Samson Zhou
  
• SODA, Near-Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time
  Full version on arXiv
  with Nadiia Chepurko, Ken Clarkson, and Praneeth Kacham
  
• SODA, Frequency Estimation with One-Sided Error
  Full version on arXiv
  with Piotr Indyk and Shyam Narayanan
  
• SODA, Improved Algorithms for Low Rank Approximation from Sparsity
  Full version on arXiv
  with Taisuke Yasuda
  
• PODS, Truly Perfect Samplers for Data Streams and Sliding Windows
  Full version on arXiv
  with Rajesh Jayaram and Samson Zhou
  

2021

• NeurIPS, Few-Shot Data-Driven Algorithms for Low Rank Approximation
  with Piotr Indyk and Tal Wagner
  Full version on OpenReview
  
• NeurIPS, Optimal Sketching for Trace Estimation
  Full version on arXiv
  with Shuli Jiang, Hai Pham, and Qiuyi Zhang
  Selected for Spotlight presentation
  
• NeurIPS, Linear and Kernel Classification in the Streaming Model: Improved Bounds for Heavy Hitters
  Full version on OpenReview
  with Arvind Mahankali
  
• FOCS, Tight Bounds for Adversarially Robust Streams and Sliding Windows via Difference Estimators
  Full version on arXiv
  with Samson Zhou
  
• IEEE Transactions on Information Theory, How to reduce dimension with PCA and random projections?
  Full version on arXiv
  with Fan Yang, Sifan Liu, and Edgar Dobriban
  
• Computing Community Consortium Research Horizons report, Visions in Theoretical Computer Science: A Report on the TCS Visioning Workshop, 2020
  Full version on arXiv
  with Shuchi Chawla, Jelani Nelson, and Chris Umans
  
• RANDOM, The Product of Gaussian Matrices is Close to Gaussian
  Available here
  with Yi Li
  
• COLT, Reduced-Rank Regression with Operator Norm Error
  with Praneeth Kacham
  Full version on arXiv
  
• COLT, Exponentially Improved Dimensionality Reduction for l1: Subspace Embeddings and Independence Testing
  Full version on arXiv
  with Yi Li and Taisuke Yasuda
  
• COLT, Average-Case Communication Complexity of Statistical Problems
  Full version on arXiv
  with Cyrus Rashtchian, Peng Ye, and Hanlin Zhu
  
• UAI, Non-PSD Matrix Sketching with Applications to Regression and Optimization
  Full version on arXiv
  with Zhili Feng and Fred Roosta-Khorasani
  
• ICML, Dimensionality Reduction for the Sum-of-Distances Metric
  Full version on arXiv
  with Zhili Feng and Praneeth Kacham
  Selected for long talk
  
• ICML, In-Database Regression in Input Sparsity Time
  Full version on arXiv
  with Rajesh Jayaram, Alireza Samadian, and Peng Ye
  
• ICML, Streaming and Distributed Algorithms for Robust Column Subset Selection
  Available here
  with Shuli Jiang, Dennis Li, Irene Mengze Li, and Arvind Mahankali
  
• ICML, Single Pass Entrywise-Transformed Low Rank Approximation
  Available here
  with Yifei Jiang, Yi Li, Yiming Sun, and Jiaxin Wang
  
• ICML, Oblivious Sketching for Logistic Regression
  Full version on arXiv
  with Alexander Munteanu and Simon Omlor
  
• ICML, Fast Sketching of Polynomial Kernels of Polynomial Degree
  Available here
  with Zhao Song, Zheng Yu, and Lichen Zhang
  
• CCC, A Simple Proof of a New Set Disjointness with Applications to Data Streams
  Full version: pdf
  with Akshay Kamath and Eric Price
  
• ICALP, Separations for Estimating Large Frequency Moments on Data Streams
  with Samson Zhou
  Full version on arXiv
  
• ICALP (invited paper), A Very Sketchy Talk
  pdf
  
• ICLR, Learning a Latent Simplex in Input Sparsity Time
  with Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan, and Samson Zhou
  Version on OpenReview
  Selected for Spotlight presentation.
• ITCS, Simple Heuristics Yield Provable Algorithms for Masked Low Rank Approximation
  with Cameron Musco and Christopher Musco
  Earlier version on arXiv
  
• SOSA, Hutch++: Optimal Stochastic Trace Estimation
  with Raphael Meyer, Cameron Musco, and Christopher Musco
  Full version on arXiv
  
• SODA, Optimal $\ell_1$ Column Subset Selection and a Fast PTAS for Low Rank Approximation
  with Arvind Mahankali
  Full version on arXiv
  
• PODS, Subspace exploration: Bounds on Projected Frequency Estimation
  with Graham Cormode and Charlie Dickens
  Full version on arXiv
  

• NeurIPS, WOR and $p$'s: Sketches for $\ell_p$-Sampling Without Replacement
  with Edith Cohen and Rasmus Pagh
  Full version on arXiv
  
• NeurIPS, Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes
  with Minh Hoang, Nghia Hoang, and Hai Pham
  Full version on arXiv
  
• FOCS, Robust and Sample Optimal Algorithms for PSD Low-Rank Approximation
  with Ainesh Bakshi and Nadiia Chepurko
  Full version on arXiv
  
• FOCS, The Coin Problem with Applications to Data Streams
  with Mark Braverman and Sumegha Garg
  Full version here
  
• FOCS, Near Optimal Linear Algebra in the Online and Sliding Window Models
  with Vladimir Braverman, Petros Drineas, Cameron Musco, Christopher Musco, Jalaj Upadhyay, and Samson Zhou
  Full version on arXiv
  
• APPROX, Streaming Complexity of SVMs
  with Alexandr Andoni, Collin Burns, Yi Li, and Sepideh Mahabadi
  Full version on arXiv
  
• APPROX, Weighted Maximum Independent Set of Geometric Objects in Turnstile Streams
  with Ainesh Bakshi and Nadiia Chepurko
  Full version on arXiv
  
• RANDOM, Vector-Matrix-Vector Queries for Solving Linear Algebra, Statistics, and Graph Problems
  with Cyrus Rashtchian and Hanlin Zhu
  Full version on arXiv
  
• ICML, Input-Sparsity Low Rank Approximation in Schatten Norm
  with Yi Li
  Full version on arXiv
  
• ICML, Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling
  with Amir Zandieh
  Full version on arXiv
  
• EC, Optimal Communication-Distortion Tradeoff in Voting
  with Debmalya Mandal and Nisarg Shah
  Full version here
  
• PODS, A Framework for Adversarially Robust Streaming Algorithms
  with Omri Ben-Eliezer, Rajesh Jayaram, and Eylon Yogev
  Full version on arXiv
  PODS Best Paper Award, 2020
  Invited to the Journal of the ACM
  2021 ACM SIGMOD Research Highlight Award
  Invited to Highlights of Algorithms (HALG) 2021
  
• STOC, Non-Adaptive Adaptive Sampling on Turnstile Streams
  with Sepideh Mahabadi, Ilya Razenshteyn, and Samson Zhou
  Full version on arXiv
  
• WWW (The Web Conference), LSF-Join: Locality Sensitive Filtering for Distributed All-Pairs Similarity Under Skew
  with Cyrus Rashtchian and Aneesh Sharma
  Full version on arXiv
  
• AISTATS, Optimal Deterministic Coresets for Ridge Regression
  with Praneeth Kacham
  Main, Supplementary
  
• AISTATS, Automatic Differentiation of Sketched Regression
  with Hang Liao, Barak A. Pearlmutter, and Vamsi k. Potluru
  Main, Supplementary
  
• ICLR, Span Recovery for Deep Neural Networks with Applications to Input Obfuscation
  with Rajesh Jayaram and Richard Zhang
  Version on OpenReview
  
• ICLR, Learning-Augmented Data Stream Algorithms
  with Tanqiu Jiang, Yi Li, Honghao Lin, and Yisong Ruan
  Version on OpenReview
  
• ITCS, Graph Spanners in the Message-Passing Model
  with Manuel Fernandez and Taisuke Yasuda
  Full version on arXiv
  
• ITCS, Pseudo-deterministic Streaming
  with Shafi Goldwasser, Ofer Grossman, and Sidhanth Mohanty
  Full version on arXiv
  
• SODA, Oblivious Sketching of High-Degree Polynomial Kernels
  with Thomas D. Ahle, Michael Kapralov, Jakob B. T. Knudsen, Rasmus Pagh, Ameya Velingker, and Amir Zandieh
  Initial version on arXiv
  Merged conference version here
  
• SODA, Tight Bounds for the Subspace Sketch Problem
  with Yi Li and Ruosong Wang
  Full version on arXiv
  
• SODA, The Communication Complexity of Optimization
  with Santosh Vempala and Ruosong Wang
  Full version on arXiv
  Invited to Highlights of Algorithms (HALG), 2020

• NeurIPS, Efficient and Thrifty Voting by Any Means Necessary
  with Debmalya Mandal, Ariel Procaccia, and Nisarg Shah
  Selected for Oral Presentation (36 out of 1428 accepted papers)
  here
  
• NeurIPS, Regularized Weighted Low Rank Approximation
  with Frank Ban and Richard Zhang
  here
  Full version on arXiv
  
• NeurIPS, Optimal Sketching for Kronecker Product Regression and Low Rank Approximation
  with Huaian Diao, Rajesh Jayaram, Zhao Song, and Wen Sun
  Full version on arXiv
  
• NeurIPS, Total Least Squares Regression in Input Sparsity Time
  with Huaian Diao, Zhao Song, and Xin Yang
  Full version on arXiv
  
• NeurIPS, Tight Dimensionality Reduction for Sketching Low Degree Polynomials
  with Michela Meister and Tamas Sarlos
  here
  
• NeurIPS, Average Case Column Subset Selection for Entrywise $\ell_1$-Norm Loss
  with Zhao Song and Peilin Zhong
  Full version is second half of this on arXiv
  
• NeurIPS, Towards a Zero-One Law for Column Subset Selection
  with Zhao Song and Peilin Zhong
  Full version is first half of this on arXiv
  
• APPROX, The Query Complexity of Mastermind with Lp Distances
  with Manuel Fernandez and Taisuke Yasuda
  Full version on arXiv
  
• APPROX, Towards Optimal Moment Estimation in Streaming and Distributed Models
  with Rajesh Jayaram
  Full version on arXiv
  
• ICML, Dimensionality Reduction for Tukey Regresion
  with Ken Clarkson and Ruosong Wang
  Full version on arXiv
  
• ICML, Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$-Means Clustering
  with Manuel Fernandez and Taisuke Yasuda
  Full version on arXiv
  Selected for a long talk
  
• ICML, Faster Algorithms for Boolean Matrix Factorization
  with Ravi Kumar, Rina Panigrahy, and Ali Rahimi
  Full version here
  slides
  Selected for a long talk
  
• ICALP, Robust Communication-Optimal Distributed Clustering
  with Pranjal Awasthi, Ainesh Bakshi, Nina Balcan, and Colin White
  Full version on arXiv
  
• ICALP, Querying a Matrix through Matrix-Vector Products
  with Xiaoming Sun, Guang Yang, and Jialin Zhang
  Full version on arXiv
  
• ICALP, Separating k-Player from t-Player One-Way Communication
  with Guang Yang
  Full version on arXiv
  
• COLT, Learning Two Layer Rectified Neural Networks in Polynomial Time
  with Ainesh Bakshi and Rajesh Jayaram
  Full version on arXiv
  
• COLT, Faster Algorithms for High Dimensional Robust Covariance Estimation
  with Yu Cheng, Ilias Diakonikolas, and Rong Ge
  Full version on arXiv
  
• COLT, Sample-Optimal Low-Rank Approximation of Distance Matrices
  with Piotr Indyk, Ali Vakilian, and Tal Wagner
  Full version on arXiv
  
• PODS, Weighted Reservoir Sampling from Distributed Streams
  with Rajesh Jayaram, Gokarna Sharma, and Srikanta Tirthapura
  Full version on arXiv
  
• SOCG, The One-Way Communication Complexity of Dynamic Time Warping Distance
  with Vladimir Braverman, Moses Charikar, William Kuszmaul, and Lin F. Yang
  Full version on arXiv
  Invited to the special issue for SOCG, 2019
• AISTATS, Conditional Sparse L_p-norm Regression with Optimal Probability
  with John Hainline, Brendan Juba, and Hai S. Le
  Full version on arXiv
  
• RECOMB, Sketching Algorithms for Genomic Data Analysis and Querying in a Secure Enclave
  Preliminary full version on biorXiv
  with Can Kockan, Kaiyuan Zhu, Natnatee Dokmai, Nikolai Karpov, M. Oguzhan Kulekci, and S. Cenk Sahinalp
  
• AAAI, Sublinear Time Numerical Linear Algebra for Structured Matrices
  with Xiaofei Shi
  Full version on arXiv
  
• SODA, Testing Matrix Rank, Optimally
  with Nina Balcan, Yi Li, and Hongyang Zhang
  Full version on arXiv
  
• SODA, A PTAS for $\ell_p$ Low Rank Approximation
  with Frank Ban, Vijay Bhattiprolu, Karl Bringmann, Pavel Kolev, and Euiwoong Lee
  Full version on arXiv
  
• SODA, Relative Error Tensor Low Rank Approximation
  with Zhao Song and Peilin Zhong
  Full version on arXiv
  
• SODA, Tight Bounds for $\ell_p$ Oblivious Subspace Embeddings
  with Ruosong Wang
  Full version on arXiv
  slides
  Invited to the special issue for SODA, 2019

2018

• NeurIPS, Sublinear Time Low-Rank Approximation of Distance Matrices
  with Ainesh Bakshi
  Full version on arXiv
  Selected for Spotlight presentation.
• NeurIPS, On Coresets for Logistic Regression
  with Alexander Munteanu, Chris Schwiegelshohn, and Christian Sohler
  Full version on arXiv
  Selected for Spotlight presentation.
• NeurIPS, Robust Subspace Approximation in a Stream
  with Roie Levin and Anish Sevekari
  pdf
  Full version pdf
  Selected for Spotlight presentation.
• FOCS, Strong Coresets for k-Median and Subspace Approximation, Goodbye Dimension
  with Christian Sohler
  pdf
  long slides
  
• FOCS, Perfect $L_p$ Sampling in a Data Stream
  with Rajesh Jayaram
  Full version on arXiv
  
• APPROX, On Sketching the q to p Norms pdf
  with Aditya Krishnan and Sidhanth Mohanty
  
• APPROX, On Low-Risk Heavy Hitters and Sparse Recovery Schemes
  Full version on arXiv
  with Yi Li and Vasileios Nakos
  
• APPROX, Nearly Optimal Distinct Elements and Heavy Hitters on Sliding Windows pdf
  with Vladimir Braverman, Elena Grigorescu, Harry Lang, and Samson Zhou
  
• ICML, Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski $p$-Norms pdf and supplementary pdf
  with Graham Cormode and Charlie Dickens
  Selected for a long talk
  
• ICML, Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order
  Full version on arXiv
  with Vladimir Braverman, Stephen R. Chestnut, Robert Krauthgamer, Yi Li, and Lin Yang
  
• KDD, An Empirical Evaluation of Sketching for Numerical Linear Algebra pdf
  with Yogesh Dahiya and Dimitris Konomis
  
• ICALP, Revisiting Frequency Moment Estimation in Random Order Streams
  Full version on arXiv
  with Vladimir Braverman, Emanuele Viola, and Lin Yang
  
• ICALP, High Probability Frequency Moment Sketches
  Full version on arXiv
  with Sumit Ganguly
• ICALP, Improved Algorithms for Adaptive Compressed Sensing
  Full version on arXiv
  with Vasileios Nakos, Xiaofei Shi, and Hongyang Zhang
• PODS, Data Streams with Bounded Deletions
  Full version on arXiv
  with Rajesh Jayaram
• PODS, Distributed Statistical Estimation of Matrix Products with Appliations pdf
  with Qin Zhang
• AISTATS, Sketching for Kronecker Product Regression and P-splines
  Full version on arXiv
  with Huaian Diao, Zhao Song, and Wen Sun
  Selected for oral presentation
• ITCS, Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness
  Full version on arXiv
  with Cameron Musco, Praneeth Netrapalli, Aaron Sidford, and Shashanka Ubaru
• ITCS, Matrix Completion and Related Problems via Strong Duality
  Full version on arXiv
  with Nina Balcan, Yingyu Liang, and Hongyang Zhang

2017

• NIPS, Approximation Algorithms for $\ell_0$-Low Rank Approximation
  Full version on arXiv
  with Karl Bringmann and Pavel Kolev
• NIPS, Near Optimal Sketching of Low-Rank Tensor Regression
  Full version on arXiv
  with Jarvis Haupt and Xingguo Li
• NIPS, Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? pdf
  with Cameron Musco
• FOCS, Sublinear Time Low-Rank Approximation of Positive Semidefinite Matrices
  Full version on arXiv
  with Cameron Musco
  Long talk Short talk
• FOCS, Optimal Lower Bounds for Universal Relation, and for Samplers and Finding Duplicates in Streams
  Full version on arXiv
  with Michael Kapralov, Jelani Nelson, Jakub Pachocki, Zhengyu Wang and Mobin Yahyazadeh
• RANDOM, Sharper Bounds for Regularized Data Fitting
  Full version on arXiv
  with Haim Avron and Ken Clarkson
• ICML, Algorithms for $\ell_p$ Low-Rank Approximation pdf
  Full version on arXiv
  with Flavio Chierichetti, Sreenivas Gollapudi, Ravi Kumar, Silvio Lattanzi, and Rina Panigrahy
• ICALP, Embeddings of Schatten Norms with Applications to Data Streams
  Full version on arXiv
  with Yi Li
• ICALP, Fast Regression with an $\ell_\infty$ Guarantee pdf
  with Eric Price and Zhao Song
• STOC, Low Rank Approximation with Entrywise $\ell_1$-Norm Error
  Full version on arXiv
  with Zhao Song and Peilin Zhong
  talk
• PODS, BPTree: an L2 Heavy Hitters Algorithm Using Constant Memory
  Full version on arXiv
  with Vladimir Braverman, Stephen R. Chestnut, Nikita Ivkin, Jelani Nelson, and Zhengyu Wang
• SODA, Low-Rank PSD Approximation in Input-Sparsity Time pdf
  with Ken Clarkson
  talk
• SODA, Adaptive Matrix Vector Product pdf
  with Santosh Vempala
  talk

2016

• Scientific Reports, True Randomness from Big Data
  with Periklis Papakonstantinou and Guang Yang
  Full version here
• NIPS, Communication-Optimal Distributed Clustering pdf
  with Jiecao Chen, He Sun, and Qin Zhang
• NIPS, Sublinear Time Orthogonal Tensor Decomposition pdf
  with Zhao Song and Huan Zhang
• SPAA, Brief Announcement: Applications of Uniform Sampling: Densest Subgraph and Beyond pdf
  with Hossein Esfandiari and Mohammad Taghi Hajiaghayi
  Full version on arXiv
• RANDOM, Tight Bounds for Sketching the Operator Norm, Schatten Norms, and Subspace Embeddings pdf
  with Yi Li
  See Sicomp version for improved bounds
• ESA, Stochastic Streams: Sample Complexity vs. Space Complexity pdf
  with Michael Crouch, Andrew McGregor, and Gregory Valiant
  Talk
• KDD, Communication Efficient Distributed Kernel Principal Component Analysis pdf
  with Maria-Florina Balcan, Yingyu Liang, Le Song, and Bo Xie
  Full version on arXiv
• ICML, How to Fake Multiply by a Gaussian Matrix
  Full version on arXiv
  with Michael Kapralov and Vamsi k. Potluru
Note: the original version claimed a stronger upper bound, which was incorrect - we have shown tight matching bounds in the corrected version

• ICALP, Optimal Approximate Matrix Product in Terms of Stable Rank pdf
  with Michael B. Cohen and Jelani Nelson
  Full version on arXiv
• ICDT (invited paper), New Algorithms for Heavy Hitters in Data Streams
  Full version on arXiv
  Talk
• PODS, An Optimal Algorithm for l1-Heavy Hitters in Insertion Streams and Related Problems pdf
  Full version on arXiv
  with Arnab Bhattacharyya and Palash Dey
• PODS, Streaming Space Complexity of Nearly All Functions of One Variable on Frequency Vectors pdf
  Full version on arXiv
  with Vladimir Braverman, Stephen R. Chestnut, and Lin F. Yang
• CCC, New Characterizations in Turnstile Streams with Applications pdf
  with Yuqing Ai, Wei Hu, and Yi Li
• STOC, Beating CountSketch for Heavy Hitters in Insertion Streams pdf 
  with Vladimir Braverman, Stephen R. Chestnut, and Nikita Ivkin
  Full version on arXiv
  Talk
• STOC, On Approximating Functions of the Singular Values in a Stream pdf
  with Yi Li
  Full version on arXiv
  Talk
• STOC, Communication Lower Bounds for Statistical Estimation Problems via a Distributed Data Processing Inequality pdf
  Full version on arXiv
  with Mark Braverman, Ankit Garg, Tengyu Ma, and Huy L. Nguyen
• STOC, Optimal Principal Component Analysis in Distributed and Streaming Models pdf
  Full version on arXiv
  with Christos Boutsidis and Peilin Zhong
  Talk
• STOC, Weighted Low Rank Approximations with Provable Guarantees pdf
  with Ilya Razenshteyn and Zhao Song
  Talk
• ICDE, Distributed Low Rank Approximation of Implicit Functions of a Matrix pdf
  with Peilin Zhong
• SODA, Nearly-optimal bounds for sparse recovery in generic norms, with applications to k-median sketching pdf
  with Arturs Backurs, Piotr Indyk and Ilya Razenshteyn
• ITCS, On Sketching Quadratic Forms pdf
  Merging of full versions of papers arXiv and arXiv
  with Alexandr Andoni, Jiecao Chen, Bo Qin, Robert Krauthgamer, Qin Zhang

2015

• FOCS, Input Sparsity and Hardness for Robust Subspace Approximation pdf
  with Ken Clarkson
  talk

• APPROX, Tight Bounds for Graph Problems in Insertion Streams pdf
  with Xiaoming Sun
  talk
• ICALP, The Simultaneous Communication of Disjointness with Applications to Data Streams pdf
  with Omri Weinstein
• ICALP, Amplification of One-Way Information Complexity via Codes and Noise Sensitivity
  Full version on ECCC
  with Marco Molinaro and Grigory Yaroslavtsev
• PODS, The Communication Complexity of Distributed Set-Joins with Applications to Matrix Multiplication pdf
  with Dirk Van Gucht, Ryan Williams, and Qin Zhang
  talk
• SODA, Sketching for M-Estimators: A Unified Approach to Robust Regression pdf
  with Ken Clarkson
  talk

2014

• Bulletin of EATCS, Data Streams and Applications pdf
• NIPS, Subspace Embeddings for the Polynomial Kernel pdf
  with Haim Avron and Huy L. Nguyen
• NIPS, Low Rank Approximation Lower Bounds in Row-Update Streams pdf
• NIPS, Improved Distributed Principal Component Analysis pdf
  with Nina Balcan, Vandana Kanchanapally, and Yingyu Liang
• DISC, On the Communication Complexity of Linear Algebraic Problems in the Message Passing Model
  Full version on arXiv
  with Yi Li, Xiaoming Sun, and Chengu Wang
• RANDOM, Certifying Equality with Limited Interaction pdf
  with Joshua Brody, Amit Chakrabarti, Ranganath Kondapally, and Grigory Yaroslavtsev
• KDD, Improved Testing of Low Rank Matrices pdf
  Invited to the special issue for KDD, 2014 in Transactions on Knowledge Discovery from Data
  with Yi Li and Zhengyu Wang
  One of nine best papers in KDD.
• COLT, Principal Component Analysis and Higher Correlations for Distributed Data
  Full version: pdf
  with Ravi Kannan and Santosh Vempala
• PODC, Spanners and Sparsifiers for Dynamic Streams pdf
  Invited to the special issue for PODC, 2014 in Distributed Computing
  with Michael Kapralov
• PODC, Beyond Set Disjointness: The Communication Complexity of Finding the Intersection pdf
  with Joshua Brody, Amit Chakrabarti, Ranganath Kondapally, and Grigory Yaroslavtsev
• PODS, Is Min-Wise Hashing Optimal for Summarizing Set Intersection? pdf
  with Rasmus Pagh and Morten Stockel
• STOC, Optimal CUR Matrix Decompositions
  Full version on arXiv
  with Christos Boutsidis
  Talk
• STOC, Turnstile Streaming Algorithms Might as Well Be Linear Sketches pdf
  with Yi Li and Huy L. Nguyen
  Talk
• SODA, On Sketching Matrix Norms and the Top Singular Vector
  pdf
  with Yi Li and Huy L. Nguyen
  See Sicomp version for improved bounds
• SODA, An Optimal Lower Bound for Distinct Elements in the Message Passing Model
  pdf
  with Qin Zhang
• VLDB, Multi-Tuple Deletion Propagation: Approximations and Complexity (in PVLDB 2013 proceedings, presented in VLDB 2014)
  pdf
  with Benny Kimelfeld and Jan Vondrak

2013

• NIPS, Sketching Structured Matrices for Faster Nonlinear Regression
  pdf
  with Haim Avron and Vikas Sindhwani
• DISC, When Distributed Computation is Communication Expensive
  Full version on arXiv
  Invited to the special issue for DISC, 2013, in Distributed Computing.
  with Qin Zhang
• RANDOM, A Tight Lower Bound for High Frequency Moment Estimation with Small Error (draft of full version)
  pdf
  with Yi Li
• COLT, Subspace Embeddings and L_p Regression Using Exponential Random Variables
  Full version on arXiv
  with Qin Zhang
• STOC, Low Rank Approximation and Regression in Input Sparsity Time
  Full version on arXiv
  Co-Winner of STOC Best Paper Award
  STOC 2023 Test of Time Award
  Pat Goldberg Best Paper Award, 2013
  Invited to the Journal of the ACM
  with Ken Clarkson
  Talk
• STOC, How Robust Are Linear Sketches to Adaptive Inputs?
  Full version on arXiv
  with Moritz Hardt
  Talk
• SODA, Beating the Direct Sum Theorem in Communication Complexity with Implications for Sketching pdf
  with Marco Molinaro and Grigory Yaroslavtsev
• SODA, Lower Bounds for Adaptive Sparse Recovery pdf
  Full version on arXiv
  with Eric Price
• SODA, Faster Robust Linear Regression pdf
  Full version on arXiv
  with K.L. Clarkson, P. Drineas, M. Magdon-Ismail, M.W. Mahoney, and X. Meng

2012

• RANDOM, On Deterministic Sketching and Streaming for Sparse Recovery and Norm Estimation pdf
  with Jelani Nelson and Huy L. Nguyen
• ICML, Fast approximation of matrix coherence and statistical leverage. pdf . Full version on arXiv
  with Petros Drineas, Malik Magdon-Ismail, and Michael Mahoney
• SSP, Reusable Low-Error Compressive Sampling Schemes Through Privacy pdf
  with Anna Gilbert, Brett Hemenway, Martin Strauss, and Mary Wootters
• ISIT, Applications of the Shannon-Hartley Theorem to Data Streams and Sparse Recovery pdf
  with Eric Price
• PODS, Rectangle-Efficient Aggregation in Spatial Data Streams pdf
  with Srikanta Tirthapura
  Talk: ppt
  Note: some of the claimed time bounds should be exponential in d, that is,
  the O*() notation should suppress bounds exponential in d rather than polynomial as claimed.
  The space bounds in the paper are not affected. This will be updated on arxiv shortly.
• PODS, Space-Efficient Estimation of Statistics over Sub-Sampled Streams pdf
  with Andrew McGregor, A. Pavan, and Srikanta Tirthapura
  Talk: pptx
• STOC, Tight Bounds for Distributed Functional Monitoring pdf . Earlier full version on arXiv
  with Qin Zhang
  Talk: .ppt
  More recent talk: .ppt
• ICDE, A General Method for Estimating Correlated Aggregates over a Data Stream pdf
  with Srikanta Tirthapura

2011

• FOCS, (1+eps)-Approximate Sparse Recovery pdf
  with Eric Price
  Talk: .ppt
• FOCS, On the Power of Adaptivity in Sparse Recovery pdf
  with Piotr Indyk and Eric Price
• DISC, Optimal Random Sampling from Distributed Streams Revisited. Full version on arXiv
  with Srikanta Tirthapura
• ESA, Tolerant Algorithms pdf
  with Rolf Klein, Rainer Penninger, and Christian Sohler
• RANDOM, Streaming Algorithms with One-Sided Estimation pdf
  with Joshua Brody
• ICALP, Linear Programming and Combinatorial Bounds on Steiner Transitive-Closure Spanners pdf . Full version on arXiv
  with Piotr Berman, Arnab Bhattacharyya, Elena Grigorescu, Sofya Raskhodnikova, and Grigory Yaroslavtsev
• STOC, Near-Optimal Private Approximation Protocols via a Black Box Transformation pdf
  Talk: .ppt
• STOC, Subspace Embeddings for the L_1-norm with Applications pdf
  with Christian Sohler
  Talk: .ppt
• STOC, Fast Moment Estimation in Data Streams in Optimal Space pdf , Full version on ArXiv
  with Daniel M. Kane, Jelani Nelson, and Ely Porat
  Talk: .ppt
• ICS, The Complexity of Linear Dependence Problems in Vector Spaces pdf
  with Arnab Bhattacharyya, Piotr Indyk, and Ning Xie
  Talk: .ppt
• SODA, Optimal Bounds for Johnson-Lindenstrauss Transforms and Streaming Problems with Low Error pdf
  Full version in the special issue for Soda, 2011 in Transactions on Algorithms pdf
  with T.S. Jayram
  Talk: .ppt

2010

• FOCS, Sublinear Optimization for Machine Learning. Short version: pdf , Full version on ArXiv
  Full version in Journal of the ACM, 2012.
  Pat Goldberg Best Paper Award, 2012
  with Ken Clarkson and Elad Hazan
  Talk: .ppt
• RANDOM, A Quadratic Lower Bound for Three-Query Linear Locally Decodable Codes over Any Field pdf
  Talk: .ppt
• RANDOM, Lower Bounds for Local Monotonicity Reconstruction from Transitive-Closure Spanners Conference version: pdf
  Draft of Full version: pdf
  with Arnab Bhattacharyya, Elena Grigorescu, Madhav Jha, Kyomin Jung, and Sofya Raskhodnikova
• ICALP, Additive Spanners in Nearly Quadratic Time pdf
  Talk: .ppt
• PODS, An Optimal Algorithm for the Distinct Elements Problem pdf
  PODS Best Paper Award, 2010
  Pat Goldberg Best Paper Award, 2010
  Invited to Journal of the ACM, preliminary full version: pdf
  with Daniel M. Kane and Jelani Nelson
  Long Talk: .ppt Short Talk: .ppt
• PODS, Fast Manhattan Sketches in Data Streams pdf
  with Jelani Nelson
• SODA, Coresets and Sketches for High Dimensional Subspace Approximation Problems pdf
  with Dan Feldman, Morteza Monemizadeh, and Christian Sohler
• SODA, Lower Bounds for Sparse Recovery pdf
  with Khanh Do Ba, Piotr Indyk, and Eric Price
• SODA, On the Exact Space Complexity of Sketching and Streaming Small Norms pdf
  with Daniel M. Kane and Jelani Nelson
• SODA, 1-pass Relative-Error L_p-Sampling with Applications pdf
  with Morteza Monemizadeh
  Talk: .ppt

2009

• FOCS, The Data Stream Space Complexity of Cascaded Norms pdf
  with T.S. Jayram
  Talk: .ppt
• FOCS, Efficient Sketches for Earth-Mover Distances, with Applications pdf
  with Alexandr Andoni, Khanh Do Ba, and Piotr Indyk
  Talk: .ppt
• STOC, Numerical Linear Algebra in the Streaming Model (full version) pdf
  with Ken Clarkson
  Talk: .ppt Longer .ppt
• ICDT, The Average Case Complexity of Counting Distinct Elements pdf
  Talk .ppt
• SODA, Transitive-Closure Spanners. Full version on ArXiv
  with Arnab Bhattacharyya, Elena Grigorescu, Kyomin Jung, and Sofya Raskhodnikova
  Talk: .ppt

2008

• PODS, Epistemic Privacy ps, pdf ,
  Full version in the special issue for PODS, 2010 in Journal of the ACM: pdf
  with Alexandre Evfimievski and Ron Fagin
• RANDOM, Corruption and Recovery-Efficient Locally Decodable Codes (extended abstract) pdf
  Talk: .ppt

2007

• Ph.D. Thesis, Efficient and Private Distance Approximation in the Communication and Streaming Models pdf
  This thesis contains results from the papers "Optimal Approximations of the Frequency Moments", "Private Polylogarithmic Approximations and Efficient Matching", "Optimal Space Lower Bounds for all Frequency Moments", and "Tight Lower Bounds for the Distinct Elements Problem". It serves as a better and more thorough exposition of these papers.
• New Lower Bounds for General Locally Decodable Codes ECCC link
• Invited article in the Encyclopedia of Database Systems, Frequency Moments pdf
• Eurocrypt, Revisiting the Efficiency of Malicious Two-Party Computation eprint link
  Talk: .ppt
• SODA, The Communication and Streaming Complexity of Computing the Longest Common and Increasing Subsequences ps, pdf
  with Xiaoming Sun
  Talk: .ppt

2006

• FOCS, Lower Bounds for Additive Spanners, Emulators, and More ps, pdf
  Long talk: .ppt , FOCS talk: ppt
• FOCS, Explicit Exclusive Set Systems with Applications to Broadcast Encryption ps, pdf
  with Craig Gentry, Zulfikar Ramzan
  Long Talk: .ppt Crypto rump session Talk: .ppt , FOCS talk: ppt
• Crypto, Fast Algorithms for the Free Riders Problem in Broadcast Encryption ps, pdf Full version on eprint eprint
  with Zulfikar Ramzan
  Talk: .ppt
• APPROX, Better Approximations for the Minimum Common Integer Partition Problem ps, pdf
  Talk: .ppt
• TCC, Private Polylogarithmic Approximations and Efficient Matching. Also, ECCC ps, pdf Conference version (with some edits) ps, pdf
  with Piotr Indyk
  Talk: long .ppt and short .ppt
  Also see my Ph.D. thesis.

2005

• STOC, Optimal Approximations of the Frequency Moments of Data Streams (some typos fixed) ps, pdf
  with Piotr Indyk
  Talk: .ppt
  Also see my Ph.D. thesis.
• Eurocrypt, Practical Cryptography in High Dimensional Tori. Available: eprint
  with Marten van Dijk, Robert Granger, Dan Page, Karl Rubin, Alice Silverberg, Martijn Stam
  Talk: .ppt
• CCC, also in SICOMP, A Geometric Approach to Information-theoretic Private Information Retrieval. Also, ECCC ps, pdf
  Conference version: pdf
  with Sergey Yekhanin

2004

• SODA, Optimal Space Lower Bounds for all Frequency Moments, revised ps, conf pdf
  Talk: short .ppt and long .ppt
  Also see my Ph.D. thesis.
• PODS, Clustering via Matrix Powering, ps, pdf
  Also, a note clarifying the algorithm txt
  Thanks to Ding Bolin for pointing this out.
  with Hanson Zhou
• Crypto, Asymptotically Optimal Communication for Torus-Based Cryptography, ps, pdf
  with Marten van Dijk
  Talk: .ppt
• CCS, Private Inference Control. Conference version: ps, Long version available: IACR eprint
  with Jessica Staddon
  Talk: Dimacs/Portia Privacy-Preserving workshop: .ppt, conf: .ppt

2003

• FOCS, Tight Lower Bounds for the Distinct Elements Problem, revised ps, conf pdf
  with Piotr Indyk
  Talk: (Also DIMACS Embeddings workshop) .ppt
  Also see my Ph.D. thesis.

2002

• Eurocrypt, Cryptography in an Unbounded Computational Model, ps, pdf
  with Marten van Dijk
  Talk: .pdf

DBLP

DBLP

Miscellaneous Papers

• Master's thesis (based on Eurocrypt 2002) ps, pdf
• Survey on stream-computing F_0 ps, pdf
  with Johnny Chen, Naveen Sunkavalley

2017 Course on Sketching as a Tool for Numerical Linear Algebra

2016 Summer School Course Slides (Sketching as a Tool for Numerical Linear Algebra)

Lecture Notes (MADALGO and BASICS)

Here are three lectures, slight variants of which were given at the MADALGO summer school on streaming 2015 as well as the BASICS summer school on commun\ ication complexity 2015. The first lecture is an introduction to information theory for data streams, the second contains direct sum theorems for data strea\ ms, and the third covers multiplayer communication complexity.