Rajesh Jayaram

92 papers A* 34Journal 55Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Chamfer-Linkage for Hierarchical Agglomerative Clustering. CoRR Kishen N. Gowda, Willem Fletcher, MohammadHossein Bateni, Laxman Dhulipala, D. Ellis Hershkowitz, Rajesh Jayaram, Jakub Lacki
2026	J	`jnl`	JAG: Joint Attribute Graphs for Filtered Nearest Neighbor Search. CoRR Haike Xu, Guy E. Blelloch, Laxman Dhulipala, Lars Gottesbüren, Rajesh Jayaram, Jakub Lacki
2026	J	`jnl`	PiPNN: Ultra-Scalable Graph-Based Nearest Neighbor Indexing. CoRR Tobias Rubel, Richard Wen, Laxman Dhulipala, Lars Gottesbüren, Rajesh Jayaram, Jakub Lacki
2025	A*	`conf`	Approximating High-Dimensional Earth Mover's Distance as Fast as Closest Pair. FOCS Lorenzo Beretta, Vincent Cohen-Addad, Rajesh Jayaram, Erik Waingarten
2025	J	`jnl`	Approximating High-Dimensional Earth Mover's Distance as Fast as Closest Pair. CoRR Lorenzo Beretta, Vincent Cohen-Addad, Rajesh Jayaram, Erik Waingarten
2025	J	`jnl`	CRISP: Clustering Multi-Vector Representations for Denoising and Pruning. CoRR João Veneroso, Rajesh Jayaram, Jinmeng Rao, Gustavo Hernández Ábrego, Majid Hadian, Daniel Cer
2025	J	`jnl`	Hierarchical Retrieval: The Geometry and a Pretrain-Finetune Recipe. CoRR Chong You, Rajesh Jayaram, Ananda Theertha Suresh, Robin Nittka, Felix X. Yu, Sanjiv Kumar
2025	A*	`conf`	Improved Spectral Density Estimation via Explicit and Implicit Deflation. SODA Rajarshi Bhattacharjee, Rajesh Jayaram, Cameron Musco, Christopher Musco, Archan Ray
2025	A*	`conf`	Massively Parallel Minimum Spanning Tree in General Metric Spaces. SODA Amir Azarmehr, Soheil Behnezhad, Rajesh Jayaram, Jakub Lacki, Vahab Mirrokni, Peilin Zhong
2025	A*	`conf`	Metric Embeddings Beyond Bi-Lipschitz Distortion via Sherali-Adams. COLT Ainesh Bakshi, Vincent Cohen-Addad, Rajesh Jayaram, Sam Hopkins, Silvio Lattanzi
2025	J	`jnl`	Parallel Hierarchical Agglomerative Clustering in Low Dimensions. CoRR MohammadHossein Bateni, Laxman Dhulipala, Willem Fletcher, Kishen N. Gowda, D. Ellis Hershkowitz, Rajesh Jayaram, Jakub Lacki
2025	A*	`conf`	Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures. ICML Jie Gao, Rajesh Jayaram, Benedikt Kolbe, Shay Sapir, Chris Schwiegelshohn, Sandeep Silwal, Erik Waingarten
2025	J	`jnl`	Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures. CoRR Jie Gao, Rajesh Jayaram, Benedikt Kolbe, Shay Sapir, Chris Schwiegelshohn, Sandeep Silwal, Erik Waingarten
2025	J	`jnl`	Unleashing Graph Partitioning for Large-Scale Nearest Neighbor Search. Proc. VLDB Endow. Lars Gottesbüren, Laxman Dhulipala, Rajesh Jayaram, Jakub Lacki
2024	A*	`conf`	Data-Dependent LSH for the Earth Mover's Distance. STOC Rajesh Jayaram, Erik Waingarten, Tian Zhang
2024	J	`jnl`	Data-Dependent LSH for the Earth Mover's Distance. CoRR Rajesh Jayaram, Erik Waingarten, Tian Zhang
2024	A*	`conf`	Dynamic PageRank: Algorithms and Lower Bounds. ICALP Rajesh Jayaram, Jakub Lacki, Slobodan Mitrovic, Krzysztof Onak, Piotr Sankowski
2024	J	`jnl`	Dynamic PageRank: Algorithms and Lower Bounds. CoRR Rajesh Jayaram, Jakub Lacki, Slobodan Mitrovic, Krzysztof Onak, Piotr Sankowski
2024	A*	`conf`	Efficient Centroid-Linkage Clustering. NeurIPS Mohammad Hossein Bateni, Laxman Dhulipala, Willem Fletcher, Kishen N. Gowda, D. Ellis Hershkowitz, Rajesh Jayaram, Jakub Lacki
2024	J	`jnl`	Efficient Centroid-Linkage Clustering. CoRR MohammadHossein Bateni, Laxman Dhulipala, Willem Fletcher, Kishen N. Gowda, D. Ellis Hershkowitz, Rajesh Jayaram, Jakub Lacki
2024	A*	`conf`	Fully Dynamic Consistent SODA Jakub Lacki, Bernhard Haeupler, Christoph Grunau, Rajesh Jayaram, Václav Rozhon
2024	A*	`conf`	HyperAttention: Long-context Attention in Near-Linear Time. ICLR Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David P. Woodruff, Amir Zandieh
2024	J	`jnl`	Improved Spectral Density Estimation via Explicit and Implicit Deflation. CoRR Rajarshi Bhattacharjee, Rajesh Jayaram, Cameron Musco, Christopher Musco, Archan Ray
2024	A*	`conf`	It's Hard to HAC Average Linkage! ICALP MohammadHossein Bateni, Laxman Dhulipala, Kishen N. Gowda, D. Ellis Hershkowitz, Rajesh Jayaram, Jakub Lacki
2024	J	`jnl`	It's Hard to HAC with Average Linkage! CoRR MohammadHossein Bateni, Laxman Dhulipala, Kishen N. Gowda, D. Ellis Hershkowitz, Rajesh Jayaram, Jakub Lacki
2024	A*	`conf`	MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encoding. NeurIPS Laxman Dhulipala, Majid Hadian, Rajesh Jayaram, Jason Lee, Vahab Mirrokni
2024	J	`jnl`	MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encodings. CoRR Laxman Dhulipala, Majid Hadian, Rajesh Jayaram, Jason Lee, Vahab Mirrokni
2024	A*	`conf`	Massively Parallel Algorithms for High-Dimensional Euclidean Minimum Spanning Tree. SODA Rajesh Jayaram, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong
2024	J	`jnl`	Massively Parallel Minimum Spanning Tree in General Metric Spaces. CoRR Amir Azarmehr, Soheil Behnezhad, Rajesh Jayaram, Jakub Lacki, Vahab Mirrokni, Peilin Zhong
2024	A*	`conf`	Metric Clustering and MST with Strong and Weak Distance Oracles. COLT MohammadHossein Bateni, Prathamesh Dharangutte, Rajesh Jayaram, Chen Wang
2024	J	`jnl`	Streaming Algorithms with Few State Changes. CoRR Rajesh Jayaram, David P. Woodruff, Samson Zhou
2024	J	`jnl`	Streaming Algorithms with Few State Changes. Proc. ACM Manag. Data Rajesh Jayaram, David P. Woodruff, Samson Zhou
2024	J	`jnl`	Unleashing Graph Partitioning for Large-Scale Nearest Neighbor Search. CoRR Lars Gottesbüren, Laxman Dhulipala, Rajesh Jayaram, Jakub Lacki
2023	J	`jnl`	A Near-Linear Time Algorithm for the Chamfer Distance. CoRR Ainesh Bakshi, Piotr Indyk, Rajesh Jayaram, Sandeep Silwal, Erik Waingarten
2023	J	`jnl`	A quasi-polynomial time algorithm for Multi-Dimensional Scaling via LP hierarchies. CoRR Ainesh Bakshi, Vincent Cohen-Addad, Samuel B. Hopkins, Rajesh Jayaram, Silvio Lattanzi
2023	J	`jnl`	Fully Dynamic Consistent k-Center Clustering. CoRR Jakub Lacki, Bernhard Haeupler, Christoph Grunau, Václav Rozhon, Rajesh Jayaram
2023	J	`jnl`	HyperAttention: Long-context Attention in Near-Linear Time. CoRR Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David P. Woodruff, Amir Zandieh
2023	J	`jnl`	Massively Parallel Algorithms for High-Dimensional Euclidean Minimum Spanning Tree. CoRR Rajesh Jayaram, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong
2023	J	`jnl`	Metric Clustering and MST with Strong and Weak Distance Oracles. CoRR MohammadHossein Bateni, Prathamesh Dharangutte, Rajesh Jayaram, Chen Wang
2023	A*	`conf`	Near-Linear Time Algorithm for the Chamfer Distance. NeurIPS Ainesh Bakshi, Piotr Indyk, Rajesh Jayaram, Sandeep Silwal, Erik Waingarten
2023	A*	`conf`	Optimal Fully Dynamic SODA MohammadHossein Bateni, Hossein Esfandiari, Hendrik Fichtenberger, Monika Henzinger, Rajesh Jayaram, Vahab Mirrokni, Andreas Wiese
2023	J	`jnl`	Optimal Fully Dynamic k-Center Clustering for Adaptive and Oblivious Adversaries. CoRR MohammadHossein Bateni, Hossein Esfandiari, Hendrik Fichtenberger, Monika Henzinger, Rajesh Jayaram, Vahab Mirrokni, Andreas Wiese
2023	A*	`conf`	Streaming Euclidean MST to a Constant Factor. STOC Xi Chen, Vincent Cohen-Addad, Rajesh Jayaram, Amit Levi, Erik Waingarten
2023	J	`jnl`	Technical Perspective: Optimal Algorithms for Multiway Search on Partial Orders. SIGMOD Rec. Rajesh Jayaram
2023	J	`jnl`	Towards Optimal Moment Estimation in Streaming and Distributed Models. ACM Trans. Algorithms Rajesh Jayaram, David P. Woodruff
2022	J	`jnl`	A Framework for Adversarially Robust Streaming Algorithms. J. ACM Omri Ben-Eliezer, Rajesh Jayaram, David P. Woodruff, Eylon Yogev
2022	J	`jnl`	Adore: Differentially Oblivious Relational Database Operators. CoRR Lianke Qin, Rajesh Jayaram, Elaine Shi, Zhao Song, Danyang Zhuo, Shumo Chu
2022	J	`jnl`	Counting the Answers to a Query. SIGMOD Rec. Marcelo Arenas, Luis Alberto Croquevielle, Rajesh Jayaram, Cristian Riveros
2022	J	`jnl`	Differentially Oblivious Relational Database Operators. Proc. VLDB Endow. Lianke Qin, Rajesh Jayaram, Elaine Shi, Zhao Song, Danyang Zhuo, Shumo Chu
2022	A*	`conf`	New streaming algorithms for high dimensional EMD and MST. STOC Xi Chen, Rajesh Jayaram, Amit Levi, Erik Waingarten
2022	J	`jnl`	Stars: Tera-Scale Graph Building for Clustering and Graph Learning. CoRR CJ Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong
2022	A*	`conf`	Stars: Tera-Scale Graph Building for Clustering and Learning. NeurIPS CJ Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong
2022	J	`jnl`	Streaming Euclidean MST to a Constant Factor. CoRR Vincent Cohen-Addad, Xi Chen, Rajesh Jayaram, Amit Levi, Erik Waingarten
2022	A*	`conf`	Truly Perfect Samplers for Data Streams and Sliding Windows. PODS Rajesh Jayaram, David P. Woodruff, Samson Zhou
2021	J	`jnl`	#NFA Admits an FPRAS: Efficient Enumeration, Counting, and Uniform Generation for Logspace Classes. J. ACM Marcelo Arenas, Luis Alberto Croquevielle, Rajesh Jayaram, Cristian Riveros
2021	J	`jnl`	A Framework for Adversarially Robust Streaming Algorithms. SIGMOD Rec. Omri Ben-Eliezer, Rajesh Jayaram, David P. Woodruff, Eylon Yogev
2021	A*	`conf`	A polynomial-time approximation algorithm for counting words accepted by an NFA (invited paper). STOC Marcelo Arenas, Luis Alberto Croquevielle, Rajesh Jayaram, Cristian Riveros
2021	—	`conf`	An Optimal Algorithm for Triangle Counting in the Stream. APPROX-RANDOM Rajesh Jayaram, John Kallaugher
2021	J	`jnl`	An Optimal Algorithm for Triangle Counting. CoRR Rajesh Jayaram, John Kallaugher
2021	A*	`conf`	In-Database Regression in Input Sparsity Time. ICML Rajesh Jayaram, Alireza Samadian, David P. Woodruff, Peng Ye
2021	J	`jnl`	In-Database Regression in Input Sparsity Time. CoRR Rajesh Jayaram, Alireza Samadian, David P. Woodruff, Peng Ye
2021	A*	`conf`	Learning and testing junta distributions with sub cube conditioning. COLT Xi Chen, Rajesh Jayaram, Amit Levi, Erik Waingarten
2021	J	`jnl`	New Streaming Algorithms for High Dimensional EMD and MST. CoRR Xi Chen, Rajesh Jayaram, Amit Levi, Erik Waingarten
2021	J	`jnl`	Optimal Fully Dynamic k-Centers Clustering. CoRR MohammadHossein Bateni, Hossein Esfandiari, Rajesh Jayaram, Vahab S. Mirrokni
2021	J	`jnl`	Perfect L SIAM J. Comput. Rajesh Jayaram, David P. Woodruff
2021	—	`—`	Sketching and Sampling Algorithms for High-Dimensional Data. Rajesh Jayaram
2021	J	`jnl`	Truly Perfect Samplers for Data Streams and Sliding Windows. CoRR Rajesh Jayaram, David P. Woodruff, Samson Zhou
2021	A*	`conf`	When is approximate counting for conjunctive queries tractable? STOC Marcelo Arenas, Luis Alberto Croquevielle, Rajesh Jayaram, Cristian Riveros
2020	A*	`conf`	A Framework for Adversarially Robust Streaming Algorithms. PODS Omri Ben-Eliezer, Rajesh Jayaram, David P. Woodruff, Eylon Yogev
2020	J	`jnl`	A Framework for Adversarially Robust Streaming Algorithms. CoRR Omri Ben-Eliezer, Rajesh Jayaram, David P. Woodruff, Eylon Yogev
2020	J	`jnl`	An FPRAS and Polynomial-Time Uniform Sampler for Tree Automata. CoRR Marcelo Arenas, Luis Alberto Croquevielle, Rajesh Jayaram, Cristian Riveros
2020	J	`jnl`	Efficient Logspace Classes for Enumeration, Counting, and Uniform Generation. SIGMOD Rec. Marcelo Arenas, Luis Alberto Croquevielle, Rajesh Jayaram, Cristian Riveros
2020	J	`jnl`	Learning and Testing Junta Distributions with Subcube Conditioning. CoRR Xi Chen, Rajesh Jayaram, Amit Levi, Erik Waingarten
2020	A*	`conf`	Span Recovery for Deep Neural Networks with Applications to Input Obfuscation. ICLR Rajesh Jayaram, David P. Woodruff, Qiuyi Zhang
2020	J	`jnl`	Span Recovery for Deep Neural Networks with Applications to Input Obfuscation. CoRR Rajesh Jayaram, David P. Woodruff, Qiuyi Zhang
2020	A*	`conf`	Testing Positive Semi-Definiteness via Random Submatrices. FOCS Ainesh Bakshi, Nadiia Chepurko, Rajesh Jayaram
2020	J	`jnl`	Testing Positive Semi-Definiteness via Random Submatrices. CoRR Ainesh Bakshi, Nadiia Chepurko, Rajesh Jayaram
2019	A*	`conf`	Efficient Logspace Classes for Enumeration, Counting, and Uniform Generation. PODS Marcelo Arenas, Luis Alberto Croquevielle, Rajesh Jayaram, Cristian Riveros
2019	J	`jnl`	Efficient Logspace Classes for Enumeration, Counting, and Uniform Generation. CoRR Marcelo Arenas, Luis Alberto Croquevielle, Rajesh Jayaram, Cristian Riveros
2019	A*	`conf`	Learning Two Layer Rectified Neural Networks in Polynomial Time. COLT Ainesh Bakshi, Rajesh Jayaram, David P. Woodruff
2019	A*	`conf`	Optimal Sketching for Kronecker Product Regression and Low Rank Approximation. NeurIPS Huaian Diao, Rajesh Jayaram, Zhao Song, Wen Sun, David P. Woodruff
2019	J	`jnl`	Optimal Sketching for Kronecker Product Regression and Low Rank Approximation. CoRR Huaian Diao, Rajesh Jayaram, Zhao Song, Wen Sun, David P. Woodruff
2019	—	`conf`	Towards Optimal Moment Estimation in Streaming and Distributed Models. APPROX-RANDOM Rajesh Jayaram, David P. Woodruff
2019	J	`jnl`	Towards Optimal Moment Estimation in Streaming and Distributed Models. CoRR Rajesh Jayaram, David P. Woodruff
2019	A*	`conf`	Weighted Reservoir Sampling from Distributed Streams. PODS Rajesh Jayaram, Gokarna Sharma, Srikanta Tirthapura, David P. Woodruff
2019	J	`jnl`	Weighted Reservoir Sampling from Distributed Streams. CoRR Rajesh Jayaram, Gokarna Sharma, Srikanta Tirthapura, David P. Woodruff
2018	A*	`conf`	Data Streams with Bounded Deletions. PODS Rajesh Jayaram, David P. Woodruff
2018	J	`jnl`	Data Streams with Bounded Deletions. CoRR Rajesh Jayaram, David P. Woodruff
2018	J	`jnl`	Learning Two Layer Rectified Neural Networks in Polynomial Time. CoRR Ainesh Bakshi, Rajesh Jayaram, David P. Woodruff
2018	J	`jnl`	Perfect L CoRR Rajesh Jayaram, David P. Woodruff
2018	A*	`conf`	Perfect Lp Sampling in a Data Stream. FOCS Rajesh Jayaram, David P. Woodruff
2017	A*	`conf`	Approximating Language Edit Distance Beyond Fast Matrix Multiplication: Ultralinear Grammars Are Where Parsing Becomes Hard!. ICALP Rajesh Jayaram, Barna Saha

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.