Sanjay Jain

2.3k total citations
157 papers, 731 citations indexed

About

Sanjay Jain is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Networks and Communications. According to data from OpenAlex, Sanjay Jain has authored 157 papers receiving a total of 731 indexed citations (citations by other indexed papers that have themselves been cited), including 132 papers in Artificial Intelligence, 128 papers in Computational Theory and Mathematics and 16 papers in Computer Networks and Communications. Recurrent topics in Sanjay Jain's work include Machine Learning and Algorithms (118 papers), Computability, Logic, AI Algorithms (92 papers) and semigroups and automata theory (80 papers). Sanjay Jain is often cited by papers focused on Machine Learning and Algorithms (118 papers), Computability, Logic, AI Algorithms (92 papers) and semigroups and automata theory (80 papers). Sanjay Jain collaborates with scholars based in Singapore, United States and Germany. Sanjay Jain's co-authors include Arun Sharma, John Case, Frank Stephan, Efim Kinber, Bakhadyr Khoussainov, Steffen Lange, Rolf Wiehagen, Cristian S. Calude, Wei Li and Thomas Zeugmann and has published in prestigious journals such as Machine Learning, SIAM Journal on Computing and Theoretical Computer Science.

In The Last Decade

Sanjay Jain

138 papers receiving 686 citations

Peers

Sanjay Jain
Damian Niwiński Poland
J. I. Zucker Canada
Marc Bezem Norway
Rūsiņš Freivalds Latvia
Ferenc Gécseg Hungary
Rahul Santhanam United Kingdom
Zoran Ognjanović Serbia
Gerd Wechsung Germany
B. A. Trakhtenbrot Israel
Denis Thérien Canada
Damian Niwiński Poland
Sanjay Jain
Citations per year, relative to Sanjay Jain Sanjay Jain (= 1×) peers Damian Niwiński

Countries citing papers authored by Sanjay Jain

Since Specialization
Citations

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

Fields of papers citing papers by Sanjay Jain

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sanjay Jain

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

All Works

20 of 20 papers shown
1.
Jain, Sanjay, et al.. (2020). Learners based on transducers. Information and Computation. 283. 104676–104676.
2.
Hölzl, Rupert, Sanjay Jain, & Frank Stephan. (2016). Inductive inference and reverse mathematics. Annals of Pure and Applied Logic. 167(12). 1242–1266. 1 indexed citations
3.
Case, John, et al.. (2012). AUTOMATIC FUNCTIONS, LINEAR TIME AND LEARNING ∗. 8 indexed citations
4.
Jain, Sanjay, et al.. (2010). Uncountable automatic classes and learning. Theoretical Computer Science. 412(19). 1805–1820. 3 indexed citations
5.
Jain, Sanjay & Frank Stephan. (2009). Consistent Partial Identification.. National University of Singapore. 1 indexed citations
6.
Jain, Sanjay & Frank Stephan. (2009). Numberings optimal for learning. Journal of Computer and System Sciences. 76(3-4). 233–250. 2 indexed citations
7.
Case, John, Sanjay Jain, Wolfgang Merkle, & James S. Royer. (2006). Generality’s price: Inescapable deficiencies in machine-learned programs. 1 indexed citations
8.
Jain, Sanjay, et al.. (2005). Algorithmic Learning Theory : 24th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings. DIAL (Catholic University of Leuven). 5 indexed citations
9.
Jain, Sanjay. (2003). The Intrinsic Complexity of Learning: A Survey. Fundamenta Informaticae. 57(1). 17–37. 1 indexed citations
10.
Low, Malcolm Yoke Hean, Wentong Cai, Stephen John Turner, et al.. (2001). The Development of Conservative Superstep Protocols for Shared Memory Multiprocessor Systems. Scalable Computing Practice and Experience. 4(1). 3 indexed citations
11.
Jain, Sanjay & Arun Sharma. (2001). On a Generalized Notion of Mistake Bounds. Information and Computation. 166(2). 156–166. 1 indexed citations
12.
13.
Jain, Sanjay, Efim Kinber, & Rolf Wiehagen. (2000). Language Learning From Texts: Degrees of Instrinsic Complexity and Their Characterizations. Conference on Learning Theory. 47–58. 2 indexed citations
14.
15.
Case, John, et al.. (1995). Language Learning with Some Negative Information. Journal of Computer and System Sciences. 51(2). 273–285. 13 indexed citations
16.
Jain, Sanjay & Arun Sharma. (1995). On aggregating teams of learning machines. Theoretical Computer Science. 137(1). 85–108. 6 indexed citations
17.
Case, John, et al.. (1994). Refinements of inductive inference by Popperian and reliable machines. Kybernetika. 30(1). 23–52. 12 indexed citations
18.
Jain, Sanjay & Arun Sharma. (1990). Finite learning by a “team”. Conference on Learning Theory. 163–177. 18 indexed citations
19.
Jain, Sanjay & Arun Sharma. (1990). Hypothesis formation and language acquisition with an infinitely-often correct teacher. 225–239. 1 indexed citations
20.
Jain, Sanjay, et al.. (1989). Learning in the presence of inaccurate information. Conference on Learning Theory. 175–188. 5 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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