What is he best known for?
The fields of study he is best known for:
- Algorithm
- Artificial intelligence
- Statistics
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.
His most cited work include:
- Approximate nearest neighbors: towards removing the curse of dimensionality (3490 citations)
- Similarity Search in High Dimensions via Hashing (2912 citations)
- Locality-sensitive hashing scheme based on p-stable distributions (2270 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Combinatorics (43.55%)
- Discrete mathematics (29.68%)
- Algorithm (23.23%)
What were the highlights of his more recent work (between 2015-2021)?
- Combinatorics (43.55%)
- Algorithm (23.23%)
- Nearest neighbor search (11.61%)
In recent papers he was focusing on the following fields of study:
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.
Between 2015 and 2021, his most popular works were:
- Fast millimeter wave beam alignment (67 citations)
- Which Regular Expression Patterns Are Hard to Match (49 citations)
- Learning-Based Frequency Estimation Algorithms. (43 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Artificial intelligence
- Statistics
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.
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