Jay Glicksman
- Industrial and Manufacturing Engineering top 5%
- Management of Technology and Innovation top 5%
- Mechanical Engineering
- Artificial Intelligence
- Management Information Systems top 10%
- Co-authors
- Jay M. TenenbaumMark R. CutkoskyGeorge ToyeLarry LeiferTod S. LevittDaryl T. LawtonGlen KramerVijay Kumar
- Topics
- Image Retrieval and Classification Techniques (3 papers)Manufacturing Process and Optimization (2 papers)Advanced Image and Video Retrieval Techniques (2 papers)
- Cited by
- Industrial and Manufacturing EngineeringManagement of Technology and InnovationHuman-Computer Interaction
- Journals
- Communications of the ACMJournal of Intelligent ManufacturingIEEE Transactions on Semiconductor Manufacturing
- Partner nations
- United StatesBritish Virgin Islands
In The Last Decade
Jay Glicksman
8 papers receiving 244 citations
Peers
Comparison fields: 5 of 42
- Industrial and Manufacturing Engineering 128
- Management of Technology and Innovation 86
- Mechanical Engineering 76
- Artificial Intelligence 59
- Management Information Systems 54
Countries citing papers authored by Jay Glicksman
This map shows the geographic impact of Jay Glicksman'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 Jay Glicksman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jay Glicksman more than expected).
Fields of papers citing papers by Jay Glicksman
This network shows the impact of papers produced by Jay Glicksman. 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 Jay Glicksman. The network helps show where Jay Glicksman may publish in the future.
Co-authorship network of co-authors of Jay Glicksman
This figure shows the co-authorship network connecting the top 25 collaborators of Jay Glicksman. A scholar is included among the top collaborators of Jay Glicksman 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 Jay Glicksman. Jay Glicksman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 53 | |
| 3 | 5 | |
| 4 | 124 | |
| 5 | 43 | |
| 6 | 8 | |
| 7 | 36 | |
| 8 | 20 | |
| 9 | Linear Feature Extraction from Radar Imagery. | 1 |
| 10 | Using Multiple Information Sources in a Computational Vision System. | 6 |
About Jay Glicksman
Jay Glicksman is a scholar working on Human-Computer Interaction, Discrete Mathematics and Combinatorics and Industrial and Manufacturing Engineering, having authored 10 papers that have together received 296 indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (3 papers), Manufacturing Process and Optimization (2 papers) and Advanced Image and Video Retrieval Techniques (2 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (128 citations), Management of Technology and Innovation (86 citations) and Human-Computer Interaction (34 citations). Jay Glicksman has collaborated with scholars based in United States and British Virgin Islands. Frequent co-authors include Jay M. Tenenbaum, Mark R. Cutkosky, George Toye, Larry Leifer, Tod S. Levitt, Daryl T. Lawton, Glen Kramer and Vijay Kumar. Their work appears in journals such as Communications of the ACM, Journal of Intelligent Manufacturing and IEEE Transactions on Semiconductor Manufacturing.
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.