John M. Hitchcock
- Computational Theory and Mathematics top 2%
- Artificial Intelligence top 10%
- Mathematical Physics
- Statistics and Probability
- Computer Networks and Communications
- Co-authors
- Elvira MayordomoJack H. LutzN. V. VinodchandranKrishna B. AthreyaA. PavanHarry BuhrmanAkinori KawachiDan Gutfreund
- Topics
- Computability, Logic, AI Algorithms (25 papers)Complexity and Algorithms in Graphs (20 papers)semigroups and automata theory (7 papers)
- Journals
- SIAM Journal on ComputingTheoretical Computer ScienceJournal of Computer and System Sciences
- Partner nations
- United StatesNetherlandsSpain
In The Last Decade
John M. Hitchcock
28 papers receiving 186 citations
Peers
Comparison fields: 5 of 28
- Computational Theory and Mathematics 196
- Artificial Intelligence 115
- Mathematical Physics 31
- Statistics and Probability 18
- Computer Networks and Communications 8
Countries citing papers authored by John M. Hitchcock
This map shows the geographic impact of John M. Hitchcock'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 John M. Hitchcock with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John M. Hitchcock more than expected).
Fields of papers citing papers by John M. Hitchcock
This network shows the impact of papers produced by John M. Hitchcock. 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 John M. Hitchcock. The network helps show where John M. Hitchcock may publish in the future.
Co-authorship network of co-authors of John M. Hitchcock
This figure shows the co-authorship network connecting the top 25 collaborators of John M. Hitchcock. A scholar is included among the top collaborators of John M. Hitchcock 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 John M. Hitchcock. John M. Hitchcock is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 12 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 8 | |
| 6 | A note on exponential circuit lower bounds from derandomizing Arthur-Merlin games. | 3 |
| 7 | Kolmogorov Complexity in Randomness Extraction. | 2 |
| 8 | NP-Hard Sets are Exponentially Dense Unless NP is contained in coNP/poly. | 1 |
| 9 | 0 | |
| 10 | 0 | |
| 11 | 0 | |
| 12 | 1 | |
| 13 | 14 | |
| 14 | 6 | |
| 15 | 2 | |
| 16 | 14 | |
| 17 | 2 | |
| 18 | 2 | |
| 19 | 25 | |
| 20 | 9 |
About John M. Hitchcock
John M. Hitchcock is a scholar working on Computational Theory and Mathematics, Statistics and Probability and Artificial Intelligence, having authored 33 papers that have together received 208 indexed citations. Recurring topics across this work include Computability, Logic, AI Algorithms (25 papers), Complexity and Algorithms in Graphs (20 papers) and semigroups and automata theory (7 papers). The work is most often cited by research in Computational Theory and Mathematics (196 citations), Artificial Intelligence (115 citations) and Mathematical Physics (31 citations). John M. Hitchcock has collaborated with scholars based in United States, Netherlands and Spain. Frequent co-authors include Elvira Mayordomo, Jack H. Lutz, N. V. Vinodchandran, Krishna B. Athreya, A. Pavan, Harry Buhrman, Akinori Kawachi, Dan Gutfreund, Dieter van Melkebeek and Scott Aaronson. Their work appears in journals such as SIAM Journal on Computing, Theoretical Computer Science and Journal of Computer and System Sciences.
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.