Sooraj Bhat

504 total citations
13 papers, 188 citations indexed

About

Sooraj Bhat is a scholar working on Artificial Intelligence, Computer Networks and Communications and Signal Processing. According to data from OpenAlex, Sooraj Bhat has authored 13 papers receiving a total of 188 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 6 papers in Computer Networks and Communications and 3 papers in Signal Processing. Recurrent topics in Sooraj Bhat's work include Bayesian Modeling and Causal Inference (5 papers), Reinforcement Learning in Robotics (4 papers) and Peer-to-Peer Network Technologies (3 papers). Sooraj Bhat is often cited by papers focused on Bayesian Modeling and Causal Inference (5 papers), Reinforcement Learning in Robotics (4 papers) and Peer-to-Peer Network Technologies (3 papers). Sooraj Bhat collaborates with scholars based in United States, Sweden and India. Sooraj Bhat's co-authors include Charles L. Isbell, Dejan Kostić, Charles Killian, Adolfo Rodriguez, Amin Vahdat, Michael Mateas, Ashish Agarwal, Alexander Gray, Richard Vuduc and David L. Roberts and has published in prestigious journals such as ACM SIGPLAN Notices, Logical Methods in Computer Science and Infoscience (Ecole Polytechnique Fédérale de Lausanne).

In The Last Decade

Sooraj Bhat

13 papers receiving 180 citations

Peers

Sooraj Bhat

AI

CNC

CVPR

SPS

IS

Nobuhiro Inuzuka Japan

Karim Tabia France

Toshiaki Tanaka Japan

J. Girão Portugal

Hatem Bettahar France

Michael Malkin United States

Gregory M. Zaverucha Canada

HyungChul Kang South Korea

Chunxiang Gu China

Deepesh Data United States

Nobuhiro Inuzuka Japan

Sooraj Bhat

36 ×0.4

AI

48 ×0.5

CNC

10 ×0.4

CVPR

7 ×0.3

SPS

9 ×0.6

IS

Citations per year, relative to Sooraj Bhat Sooraj Bhat (= 1×) peers Nobuhiro Inuzuka

Countries citing papers authored by Sooraj Bhat

Since Specialization

Citations

This map shows the geographic impact of Sooraj Bhat'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 Sooraj Bhat with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sooraj Bhat more than expected).

Fields of papers citing papers by Sooraj Bhat

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sooraj Bhat. 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 Sooraj Bhat. The network helps show where Sooraj Bhat may publish in the future.

Co-authorship network of co-authors of Sooraj Bhat

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

All Works

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13 of 13 papers shown

1.

Asha, C S, et al.. (2019). Auto Removal of Bright Spot from Images Captured Against Flashing Light Source. 1–6. 21 indexed citations

2.

Bhat, Sooraj, Johannes Borgström, Andrew D. Gordon, & Claudio Russo. (2017). Deriving Probability Density Functions from Probabilistic Functional Programs. Logical Methods in Computer Science. Volume 13, Issue 2. 1 indexed citations

3.

Bhat, Sooraj, Johannes Borgström, Andrew D. Gordon, & Claudio Russo. (2017). Deriving Probability Density Functions from Probabilistic Functional Programs. Logical Methods in Computer Science. 13(2). 1–32. 2 indexed citations

4.

Bhat, Sooraj, Ashish Agarwal, Richard Vuduc, & Alexander Gray. (2012). A type theory for probability density functions. 545–556. 16 indexed citations

5.

Bhat, Sooraj, Ashish Agarwal, Richard Vuduc, & Alexander Gray. (2012). A type theory for probability density functions. ACM SIGPLAN Notices. 47(1). 545–556. 12 indexed citations

6.

Bhat, Sooraj, et al.. (2008). Towards adaptive programming. ACM SIGPLAN Notices. 43(10). 603–614. 5 indexed citations

7.

Bhat, Sooraj, et al.. (2008). Towards adaptive programming. 603–614. 18 indexed citations

8.

Roberts, David L., et al.. (2007). Authorial idioms for target distributions in TTD-MDPs. National Conference on Artificial Intelligence. 852–857. 13 indexed citations

9.

Bhat, Sooraj, David L. Roberts, Mark Nelson, Charles L. Isbell, & Michael Mateas. (2007). A globally optimal algorithm for TTD-MDPs. 1–8. 11 indexed citations

10.

Bhat, Sooraj, Charles L. Isbell, & Michael Mateas. (2006). On the difficulty of modular reinforcement learning for real-world partial programming. National Conference on Artificial Intelligence. 318–323. 15 indexed citations

11.

Cooper, Brian F., Charles L. Isbell, Jeffrey S. Pierce, David L. Roberts, & Sooraj Bhat. (2006). Accord: Middleware Support for Contextual, Ubiquitous Data Management on User Devices. SMARTech Repository (Georgia Institute of Technology). 1 indexed citations

12.

Roberts, David L., Sooraj Bhat, Charles L. Isbell, Brian F. Cooper, & Jeffrey S. Pierce. (2006). A decision-theoretic approach to file consistency in constrained peer-to-peer device networks. 338–340. 2 indexed citations

13.

Rodriguez, Adolfo, Charles Killian, Sooraj Bhat, Dejan Kostić, & Amin Vahdat. (2004). MACEDON: methodology for automatically creating, evaluating, and designing overlay networks. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 20–20. 71 indexed citations

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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