What is he best known for?
The fields of study he is best known for:
- Computer network
- Combinatorics
- Algorithm
Anupam Gupta mainly investigates Combinatorics, Approximation algorithm, Discrete mathematics, Mathematical optimization and Algorithm.
His work in the fields of Binary logarithm overlaps with other areas such as Monotone polygon.
He has included themes like Computational complexity theory, Wireless sensor network, Minimax approximation algorithm, Probabilistic logic and Optimization problem in his Approximation algorithm study.
His work deals with themes such as Embedding and Greedy algorithm, which intersect with Discrete mathematics.
His Mathematical optimization research is multidisciplinary, incorporating perspectives in Network planning and design, CURE data clustering algorithm and Cluster analysis.
His Algorithm study incorporates themes from Submodular set function, Open problem and Theoretical computer science.
His most cited work include:
- An elementary proof of a theorem of Johnson and Lindenstrauss (719 citations)
- Near-optimal sensor placements: maximizing information while minimizing communication cost (418 citations)
- Bounded geometries, fractals, and low-distortion embeddings (410 citations)
What are the main themes of his work throughout his whole career to date?
Anupam Gupta mostly deals with Combinatorics, Approximation algorithm, Discrete mathematics, Mathematical optimization and Algorithm.
His research integrates issues of Embedding, Bounded function and Metric space in his study of Combinatorics.
His Embedding research is multidisciplinary, incorporating elements of Distortion and Euclidean space.
His work in Approximation algorithm addresses issues such as Steiner tree problem, which are connected to fields such as Covering problems and Combinatorial optimization.
His biological study spans a wide range of topics, including Linear programming relaxation and Metric.
The Mathematical optimization study combines topics in areas such as Scheduling, Graph and Competitive analysis.
He most often published in these fields:
- Combinatorics (45.45%)
- Approximation algorithm (37.66%)
- Discrete mathematics (27.60%)
What were the highlights of his more recent work (between 2018-2021)?
- Combinatorics (45.45%)
- Algorithm (15.91%)
- Competitive analysis (11.04%)
In recent papers he was focusing on the following fields of study:
Combinatorics, Algorithm, Competitive analysis, Mathematical optimization and Online algorithm are his primary areas of study.
His Combinatorics research includes themes of Bounded function and Metric space.
His work on Randomized algorithm as part of his general Algorithm study is frequently connected to Corruption, thereby bridging the divide between different branches of science.
His studies in Competitive analysis integrate themes in fields like Binary logarithm and Submodular set function.
Anupam Gupta studies Approximation algorithm which is a part of Mathematical optimization.
His studies deal with areas such as Constraint, Markov chain and Cache as well as Approximation algorithm.
Between 2018 and 2021, his most popular works were:
- Better Algorithms for Stochastic Bandits with Adversarial Corruptions (36 citations)
- Scale Steerable Filters for Locally Scale-Invariant Convolutional Neural Networks. (18 citations)
- Chasing convex bodies with linear competitive ratio (15 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Algorithm
- Statistics
His primary areas of investigation include Combinatorics, Algorithm, Competitive analysis, Online algorithm and Treewidth.
His research integrates issues of Bounded function, Metric space and Cluster analysis in his study of Combinatorics.
Anupam Gupta has researched Bounded function in several fields, including Vertex, Induced subgraph, Graph partition, Approximation algorithm and Minimax approximation algorithm.
His studies in Algorithm integrate themes in fields like Adversarial system, Regret, Convolutional neural network and Scale invariance.
His study in Competitive analysis is interdisciplinary in nature, drawing from both Tree and Matching.
His Treewidth research includes elements of Submodular set function, Set cover problem and Linear programming relaxation.
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