Jeff Ullman
Impact in
- Signal Processing top 5%
- Data Management and Algorithms
-
- Advanced Database Systems and Queries
- Distributed systems and fault tolerance
- Advanced Data Storage Technologies
Papers in
-
- Advanced Database Systems and Queries 4
-
- Data Mining Algorithms and Applications 3
- Web Data Mining and Analysis 1
- Co-authors
- Avi Silberschatz (2 shared papers)Michael Stonebraker (2 shared papers)Rajeev Motwani (1 shared paper)Craig Silverstein (1 shared paper)Sergey Brin (1 shared paper)Mike Stonebraker (2 shared papers)Jim Gray (2 shared papers)Alfred V. Aho (2 shared papers)
- Journals
- ACM SIGMOD Record (3 papers)Communications of the ACM (1 paper)Data Mining and Knowledge Discovery (1 paper)Computers & Mathematics with Applications (1 paper)Lecture notes in computer science (1 paper)
- Partner nations
- United StatesSwitzerlandUnited Kingdom
In The Last Decade
Jeff Ullman
11 papers receiving 591 citations
Peers
Comparison fields: 5 of 56
- Signal Processing 221
- Computer Networks and Communications 399
- Information Systems 281
- Artificial Intelligence 334
- Computational Theory and Mathematics 119
Countries citing papers authored by Jeff Ullman
This map shows the geographic impact of Jeff Ullman'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 Jeff Ullman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeff Ullman more than expected).
Fields of papers citing papers by Jeff Ullman
This network shows the impact of papers produced by Jeff Ullman. 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 Jeff Ullman. The network helps show where Jeff Ullman may publish in the future.
Co-authors
The 25 scholars most cited alongside Jeff Ullman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2000 | 175 | |
| 2 | 1998 | 171 | |
| 3 | 1991 | 154 | |
| 4 | 1996 | 72 | |
| 5 | 1996 | 38 | |
| 6 | 1979 | 21 | |
| 7 | 1982 | 19 | |
| 8 | Data Mining Research: Opportunities and Challenges | 2008 | 12 |
| 9 | 1996 | 6 | |
| 10 | 2003 | 6 | |
| 11 | 2015 | 2 | |
| 12 | 1996 | 0 |
About Jeff Ullman
Jeff Ullman is a scholar working on Computer Networks and Communications, Information Systems, Artificial Intelligence, Computational Theory and Mathematics and Molecular Biology, having authored 12 papers that have together received 676 indexed citations. Recurring topics across this work include Advanced Database Systems and Queries (4 papers), Data Mining Algorithms and Applications (3 papers), Bayesian Modeling and Causal Inference (2 papers), DNA and Biological Computing (2 papers), semigroups and automata theory (2 papers), Cellular Automata and Applications (1 paper), Web Data Mining and Analysis (1 paper) and Complexity and Algorithms in Graphs (1 paper). The work is most often cited by research in Signal Processing (221 citations), Computer Networks and Communications (399 citations), Information Systems (281 citations), Artificial Intelligence (334 citations) and Computational Theory and Mathematics (119 citations). Jeff Ullman has collaborated with scholars based in United States, Switzerland and United Kingdom. Frequent co-authors include Avi Silberschatz, Michael Stonebraker, Rajeev Motwani, Craig Silverstein, Sergey Brin, Mike Stonebraker, Jim Gray, Alfred V. Aho, Mihalis Yannakakis and Phil Bernstein. Their work appears in journals such as ACM SIGMOD Record, Communications of the ACM, Data Mining and Knowledge Discovery, Computers & Mathematics with Applications and Lecture notes in 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.