Subhodeep Moitra

1.3k citations
9 papers · 580 indexed · 1 hit paper · h-index 6
Topics
Advanced Bandit Algorithms Research (3 papers)Reinforcement Learning in Robotics (3 papers)Machine Learning and Algorithms (2 papers)

In The Last Decade

Subhodeep Moitra

9 papers receiving 544 citations

Hit Papers

Google Vizier2017202620202023201750100150200250

Peers

Subhodeep Moitra
Comparison fields: 5 of 96
  • Artificial Intelligence 315
  • Computer Vision and Pattern Recognition 99
  • Aerospace Engineering 72
  • Computational Theory and Mathematics 67
  • Information Systems 54
Replace Robert M. Patton with:
Robert M. Patton United States
Youssef Drissi Belgium
Weijie Zheng China
Minjie Wang China
Nabanita Das India
Frédéric Bastien Canada
Subhodeep Moitra relative to Robert M. Patton United States Robert M. Patton's profile →
Citations per field
00.5×4.2×
Robert M. Patton · 1×
Citations per year

Countries citing papers authored by Subhodeep Moitra

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Subhodeep Moitra

This figure shows the co-authorship network connecting the top 25 collaborators of Subhodeep Moitra. A scholar is included among the top collaborators of Subhodeep Moitra 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 Subhodeep Moitra. Subhodeep Moitra is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
#WorkIndexed citations
1
PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair
12
2 164
3 100
4 2
5 1
6
Bayesian Optimization for a Better Dessert
8
7
Google Vizierbreakdown →
286
8 6
9 1

About Subhodeep Moitra

Subhodeep Moitra is a scholar working on Management Science and Operations Research, Software and Artificial Intelligence, having authored 9 papers that have together received 580 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (3 papers), Reinforcement Learning in Robotics (3 papers) and Machine Learning and Algorithms (2 papers). The work is most often cited by research in Artificial Intelligence (315 citations), Computer Vision and Pattern Recognition (99 citations) and Software (17 citations). Subhodeep Moitra has collaborated with scholars based in United States, Sweden and South Korea. Frequent co-authors include D. Sculley, Daniel Golovin, Greg Kochanski, John Karro, Marc G. Bellemare, Pablo Samuel Castro, Jun Gong, Ziyu Wang, Sameera Ponda and Salvatore Candido. Their work appears in journals such as Nature, Artificial Intelligence 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.

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