2015 - ACM Fellow For contributions to high-dimensional geometric computing, streaming/sketching algorithms, and the Sparse Fourier Transform.
2012 - ACM Paris Kanellakis Theory and Practice Award With Andrei Broder and Moses S Charikar, for their groundbreaking work on Locality-Sensitive Hashing that has had great impact in many fields of computer science including computer vision, databases, information retrieval, machine learning, and signal processing.
2003 - Fellow of Alfred P. Sloan Foundation
Combinatorics, Discrete mathematics, Algorithm, k-nearest neighbors algorithm and Locality-sensitive hashing are his primary areas of study. His Combinatorics study incorporates themes from Upper and lower bounds and Polynomial. Piotr Indyk has researched Discrete mathematics in several fields, including Matrix and Norm.
Piotr Indyk has included themes like Nearest neighbor search and Curse of dimensionality in his k-nearest neighbors algorithm study. His Nearest neighbor search research integrates issues from Ball tree, Fixed-radius near neighbors and Cover tree. His work on Hopscotch hashing as part of general Locality-sensitive hashing study is frequently connected to Artificial intelligence, Dynamic perfect hashing and Universal hashing, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Piotr Indyk mainly focuses on Combinatorics, Discrete mathematics, Algorithm, Nearest neighbor search and Approximation algorithm. His biological study deals with issues like Metric, which deal with fields such as Euclidean distance. In Discrete mathematics, Piotr Indyk works on issues like Embedding, which are connected to Distortion.
His Algorithm study deals with Streaming algorithm intersecting with Data stream. His biological study spans a wide range of topics, including Ball tree, Fixed-radius near neighbors and k-nearest neighbors algorithm. His work in Compressed sensing addresses issues such as Sparse approximation, which are connected to fields such as Sparse matrix.
His scientific interests lie mostly in Combinatorics, Algorithm, Nearest neighbor search, Discrete mathematics and Point. He works on Combinatorics which deals in particular with Binary logarithm. His study in Algorithm is interdisciplinary in nature, drawing from both Measure and Simple.
His work deals with themes such as Data point, Metric space and k-nearest neighbors algorithm, which intersect with Nearest neighbor search. His k-nearest neighbors algorithm research incorporates elements of Nearest-neighbor chain algorithm and Computational problem. In his work, Combinatorial optimization and Element is strongly intertwined with Sequence, which is a subfield of Discrete mathematics.
His primary areas of investigation include Combinatorics, Point, Algorithm, Nearest neighbor search and Metric space. His Combinatorics research focuses on Deterministic algorithm in particular. His Point research is multidisciplinary, relying on both Transmitter, Wireless network and Cellular network.
His work on Time complexity as part of general Algorithm research is frequently linked to Scalability, bridging the gap between disciplines. His Nearest neighbor search study combines topics from a wide range of disciplines, such as Range, Discrete geometry and k-nearest neighbors algorithm. The Metric space study combines topics in areas such as Artificial neural network, Graph, Quantization and Theoretical computer science.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Approximate nearest neighbors: towards removing the curse of dimensionality
Piotr Indyk;Rajeev Motwani.
symposium on the theory of computing (1998)
Similarity Search in High Dimensions via Hashing
Aristides Gionis;Piotr Indyk;Rajeev Motwani.
very large data bases (1999)
Locality-sensitive hashing scheme based on p-stable distributions
Mayur Datar;Nicole Immorlica;Piotr Indyk;Vahab S. Mirrokni.
symposium on computational geometry (2004)
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
Alexandr Andoni;Piotr Indyk.
foundations of computer science (2006)
Enhanced hypertext categorization using hyperlinks
Soumen Chakrabarti;Byron Dom;Piotr Indyk.
international conference on management of data (1998)
Maintaining Stream Statistics over Sliding Windows
Mayur Datar;Aristides Gionis;Piotr Indyk;Rajeev Motwani.
SIAM Journal on Computing (2002)
Locality-Sensitive Hashing Using Stable Distributions
Gregory Shakhnarovich;Trevor Darrell;Piotr Indyk.
Approximate Nearest Neighbor: Towards Removing the Curse of Dimensionality
Sariel Har-Peled;Piotr Indyk;Rajeev Motwani.
Theory of Computing (2012)
Nearest-neighbor methods in learning and vision : theory and practice
Gregory Shakhnarovich;Piotr Indyk;Trevor Darrell.
Finding interesting associations without support pruning
E. Cohen;M. Datar;S. Fujiwara;A. Gionis.
IEEE Transactions on Knowledge and Data Engineering (2001)
Profile was last updated on December 6th, 2021.
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