Subhodeep Moitra
Impact in
- Artificial Intelligence top 5%
- Reinforcement Learning in Robotics
- Machine Learning and Data Classification
- Artificial Intelligence in Games
- Data Stream Mining Techniques
- Machine Learning and Algorithms
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- Advanced Neural Network Applications
Papers in
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- Reinforcement Learning in Robotics 3
- Machine Learning and Algorithms 2
- Artificial Intelligence in Games 1
- Topic Modeling 1
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- Advanced Bandit Algorithms Research 3
- Co-authors
- D. Sculley (2 shared papers)John Karro (2 shared papers)Greg Kochanski (2 shared papers)Daniel Golovin (2 shared papers)Marc G. Bellemare (3 shared papers)Sameera Ponda (1 shared paper)Ziyu Wang (1 shared paper)Salvatore Candido (1 shared paper)
- Journals
- Nature (1 paper)Artificial Intelligence (1 paper)PubMed (1 paper)arXiv (Cornell University) (1 paper)Figshare (1 paper)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Subhodeep Moitra
9 papers receiving 606 citations
Subhodeep Moitra's Hit Papers
Peers
Comparison fields: 5 of 95
- Artificial Intelligence 355
- Computer Vision and Pattern Recognition 107
- Computational Theory and Mathematics 80
- Software 19
- Management Science and Operations Research 49
Countries citing papers authored by Subhodeep Moitra
This map shows the geographic impact of Subhodeep Moitra'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 Subhodeep Moitra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Subhodeep Moitra more than expected).
Fields of papers citing papers by Subhodeep Moitra
This network shows the impact of papers produced by Subhodeep Moitra. 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 Subhodeep Moitra. The network helps show where Subhodeep Moitra may publish in the future.
Co-authors
The 25 scholars most cited alongside Subhodeep Moitra, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Google Vizier Hit paper breakdown → | 2017 | 314 |
| 2 | 2020 | 175 | |
| 3 | 2019 | 123 | |
| 4 | PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair | 2021 | 12 |
| 5 | Bayesian Optimization for a Better Dessert | 2017 | 8 |
| 6 | 2012 | 6 | |
| 7 | 2019 | 2 | |
| 8 | 2018 | 1 | |
| 9 | 2010 | 1 |
About Subhodeep Moitra
Subhodeep Moitra is a scholar working on Artificial Intelligence, Management Science and Operations Research, Molecular Biology, Information Systems and Management and Hardware and Architecture, having authored 9 papers that have together received 642 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Advanced Bandit Algorithms Research (3 papers), Machine Learning and Algorithms (2 papers), Artificial Intelligence in Games (1 paper), Bioinformatics and Genomic Networks (1 paper), Parallel Computing and Optimization Techniques (1 paper), Topic Modeling (1 paper) and Spreadsheets and End-User Computing (1 paper). The work is most often cited by research in Artificial Intelligence (355 citations), Computer Vision and Pattern Recognition (107 citations), Computational Theory and Mathematics (80 citations), Software (19 citations) and Management Science and Operations Research (49 citations). Subhodeep Moitra has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include D. Sculley, John Karro, Greg Kochanski, Daniel Golovin, Marc G. Bellemare, Sameera Ponda, Ziyu Wang, Salvatore Candido, Pablo Samuel Castro and Jun Gong. Their work appears in journals such as Nature, Artificial Intelligence, PubMed, arXiv (Cornell University) and Figshare.
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.