Anindya De
- Artificial Intelligence top 10%
- Computational Theory and Mathematics top 5%
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering
- Molecular Biology
- Co-authors
- Rocco A. ServedioThomas VidickRenato RennerChristopher PortmannIlias DiakonikolasRyan O’DonnellRajeev GajbhiyeThomas Watson
- Topics
- Machine Learning and Algorithms (13 papers)Complexity and Algorithms in Graphs (12 papers)Markov Chains and Monte Carlo Methods (6 papers)
- Partner nations
- United StatesUnited KingdomSwitzerland
In The Last Decade
Anindya De
32 papers receiving 222 citations
Peers
Comparison fields: 5 of 44
- Artificial Intelligence 177
- Computational Theory and Mathematics 95
- Atomic and Molecular Physics, and Optics 58
- Electrical and Electronic Engineering 32
- Molecular Biology 26
Countries citing papers authored by Anindya De
This map shows the geographic impact of Anindya De's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Anindya De with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anindya De more than expected).
Fields of papers citing papers by Anindya De
This network shows the impact of papers produced by Anindya De. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Anindya De. The network helps show where Anindya De may publish in the future.
Co-authorship network of co-authors of Anindya De
This figure shows the co-authorship network connecting the top 25 collaborators of Anindya De. A scholar is included among the top collaborators of Anindya De based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Anindya De. Anindya De is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | Learning sparse mixtures of permutations from noisy information | 0 |
| 5 | 2 | |
| 6 | 1 | |
| 7 | Is your function low dimensional | 1 |
| 8 | 7 | |
| 9 | Simple and efficient pseudorandom generators from Gaussian processes. | 1 |
| 10 | 19 | |
| 11 | 1 | |
| 12 | 5 | |
| 13 | 2 | |
| 14 | 1 | |
| 15 | 19 | |
| 16 | 15 | |
| 17 | Improved Pseudorandom Generators for Depth 2 Circuits. | 2 |
| 18 | Non-uniform attacks against one-way functions and PRGs. | 2 |
| 19 | 6 | |
| 20 | 17 |
About Anindya De
Anindya De is a scholar working on Computational Theory and Mathematics, Statistics and Probability and Artificial Intelligence, having authored 34 papers that have together received 252 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (13 papers), Complexity and Algorithms in Graphs (12 papers) and Markov Chains and Monte Carlo Methods (6 papers). The work is most often cited by research in Computational Theory and Mathematics (95 citations), Artificial Intelligence (177 citations) and Statistics and Probability (16 citations). Anindya De has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Rocco A. Servedio, Thomas Vidick, Renato Renner, Christopher Portmann, Ilias Diakonikolas, Ryan O’Donnell, Rajeev Gajbhiye, Thomas Watson, S. A. Soman and Xi Chen. Their work appears in journals such as IEEE Transactions on Power Delivery, SIAM Journal on Computing and Games and Economic Behavior.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.