Johnson Apacible
Impact in
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- Personal Information Management and User Behavior
- Human-Computer Interaction top 5%
- Usability and User Interface Design
Papers in
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- User Authentication and Security Systems 3
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- Data Visualization and Analytics 3
- Co-authors
- Eric Horvitz (4 shared papers)Karthik Kalyanaraman (1 shared paper)Trishul Chilimbi (1 shared paper)Paul Koch (2 shared papers)Michael Gamon (1 shared paper)Tae Yano (1 shared paper)Patrick Pantel (1 shared paper)Xinying Song (2 shared papers)
- Journals
- Journal of Convergence Information Technology (1 paper)Operating Systems Design and Implementation (1 paper)
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
Johnson Apacible
7 papers receiving 622 citations
Johnson Apacible's Hit Papers
Peers
Comparison fields: 5 of 74
- Information Systems and Management 225
- Human-Computer Interaction 122
- Computer Vision and Pattern Recognition 289
- Artificial Intelligence 292
- Computer Science Applications 38
Countries citing papers authored by Johnson Apacible
This map shows the geographic impact of Johnson Apacible'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 Johnson Apacible with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Johnson Apacible more than expected).
Fields of papers citing papers by Johnson Apacible
This network shows the impact of papers produced by Johnson Apacible. 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 Johnson Apacible. The network helps show where Johnson Apacible may publish in the future.
Co-authors
The 12 scholars most cited alongside Johnson Apacible, 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 | Project Adam: building an efficient and scalable deep learning training system Hit paper breakdown → | 2014 | 352 |
| 2 | 2003 | 178 | |
| 3 | 2004 | 109 | |
| 4 | 2013 | 23 | |
| 5 | 2003 | 9 | |
| 6 | 2012 | 4 | |
| 7 | Experiences with the Design, Fielding, and Evaluation of a Real-Time Communications Agent | 2003 | 2 |
| 8 | 2013 | 0 |
About Johnson Apacible
Johnson Apacible is a scholar working on Information Systems, Computer Vision and Pattern Recognition, Information Systems and Management, Artificial Intelligence and Computer Networks and Communications, having authored 8 papers that have together received 677 indexed citations. Recurring topics across this work include Personal Information Management and User Behavior (4 papers), Data Visualization and Analytics (3 papers), User Authentication and Security Systems (3 papers), Advanced Database Systems and Queries (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Data Management and Algorithms (1 paper), Mathematics, Computing, and Information Processing (1 paper) and Complex Network Analysis Techniques (1 paper). The work is most often cited by research in Information Systems and Management (225 citations), Human-Computer Interaction (122 citations), Computer Vision and Pattern Recognition (289 citations), Artificial Intelligence (292 citations) and Computer Science Applications (38 citations). Johnson Apacible has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Eric Horvitz, Karthik Kalyanaraman, Trishul Chilimbi, Paul Koch, Michael Gamon, Tae Yano, Patrick Pantel, Xinying Song, Shahab Kamali and Raman Sarin. Their work appears in journals such as Journal of Convergence Information Technology and Operating Systems Design and Implementation.
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.