Jack H. Lutz
- Computational Theory and Mathematics top 0.5%
- Artificial Intelligence top 5%
- Molecular Biology
- Statistics and Probability top 5%
- Mathematical Physics top 10%
- Co-authors
- Elvira MayordomoDavid JuedesJames I. LathropScott M. SummersJohn M. HitchcockKrishna B. AthreyaRonald V. BookMatthew J. Patitz
- Topics
- Computability, Logic, AI Algorithms (63 papers)semigroups and automata theory (38 papers)Algorithms and Data Compression (15 papers)
- Journals
- IEEE Transactions on Information TheorySIAM Journal on ComputingTheoretical Computer Science
- Partner nations
- United StatesSpainGermany
In The Last Decade
Jack H. Lutz
77 papers receiving 892 citations
Peers
Comparison fields: 5 of 50
- Computational Theory and Mathematics 871
- Artificial Intelligence 470
- Molecular Biology 123
- Statistics and Probability 102
- Mathematical Physics 97
Countries citing papers authored by Jack H. Lutz
This map shows the geographic impact of Jack H. Lutz'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 Jack H. Lutz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jack H. Lutz more than expected).
Fields of papers citing papers by Jack H. Lutz
This network shows the impact of papers produced by Jack H. Lutz. 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 Jack H. Lutz. The network helps show where Jack H. Lutz may publish in the future.
Co-authorship network of co-authors of Jack H. Lutz
This figure shows the co-authorship network connecting the top 25 collaborators of Jack H. Lutz. A scholar is included among the top collaborators of Jack H. Lutz 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 Jack H. Lutz. Jack H. Lutz 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 | 1 | |
| 3 | The Ambiguous Effect of Population Size on the Prevalence of Terrorism, | 2 |
| 4 | 4 | |
| 5 | 1 | |
| 6 | 4 | |
| 7 | Inseparability and Strong Hypotheses for Disjoint NP Pairs. | 1 |
| 8 | 39 | |
| 9 | 14 | |
| 10 | Twelve Problems in Resource-Bounded Measure. | 9 |
| 11 | Observations on Measure and Lowness for Delta^P_2 | 1 |
| 12 | 42 | |
| 13 | 4 | |
| 14 | 23 | |
| 15 | 22 | |
| 16 | 14 | |
| 17 | 5 | |
| 18 | 133 | |
| 19 | 6 | |
| 20 | 8 |
About Jack H. Lutz
Jack H. Lutz is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Statistics and Probability, having authored 88 papers that have together received 989 indexed citations. Recurring topics across this work include Computability, Logic, AI Algorithms (63 papers), semigroups and automata theory (38 papers) and Algorithms and Data Compression (15 papers). The work is most often cited by research in Computational Theory and Mathematics (871 citations), Artificial Intelligence (470 citations) and Statistics and Probability (102 citations). Jack H. Lutz has collaborated with scholars based in United States, Spain and Germany. Frequent co-authors include Elvira Mayordomo, David Juedes, James I. Lathrop, Scott M. Summers, John M. Hitchcock, Krishna B. Athreya, Ronald V. Book, Matthew J. Patitz, Damien Woods and David Doty. Their work appears in journals such as IEEE Transactions on Information Theory, SIAM Journal on Computing and Theoretical Computer Science.
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.