Daniel Smilkov
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 5%
- Explainable Artificial Intelligence (XAI)
- Machine Learning in Healthcare
Papers in
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- Complex Network Analysis Techniques 6
- Opinion Dynamics and Social Influence 4
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- Graph theory and applications 2
- Co-authors
- Martin Wattenberg (3 shared papers)Fernanda Viégas (3 shared papers)Ljupčo Kocarev (6 shared papers)James Wexler (1 shared paper)Kanit Wongsuphasawat (1 shared paper)Dilip Krishnan (1 shared paper)Jason Hipp (1 shared paper)Martin C. Stumpe (1 shared paper)
- Journals
- Physica A Statistical Mechanics and its Applications (2 papers)Scientific Reports (1 paper)IEEE Transactions on Visualization and Computer Graphics (1 paper)arXiv (Cornell University) (1 paper)Physical Review E (3 papers)
- Partner nations
- North MacedoniaUnited StatesItaly
In The Last Decade
Daniel Smilkov
10 papers receiving 512 citations
Peers
Comparison fields: 5 of 117
- Health Informatics 69
- Artificial Intelligence 253
- Computer Vision and Pattern Recognition 145
- Safety Research 52
- Statistical and Nonlinear Physics 65
Countries citing papers authored by Daniel Smilkov
This map shows the geographic impact of Daniel Smilkov'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 Daniel Smilkov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Smilkov more than expected).
Fields of papers citing papers by Daniel Smilkov
This network shows the impact of papers produced by Daniel Smilkov. 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 Daniel Smilkov. The network helps show where Daniel Smilkov may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel Smilkov, 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 | 2019 | 222 | |
| 2 | 2017 | 222 | |
| 3 | 2014 | 29 | |
| 4 | 2011 | 26 | |
| 5 | 2012 | 14 | |
| 6 | 2010 | 8 | |
| 7 | TensorFlow.js: Machine Learning for the Web and Beyond | 2019 | 5 |
| 8 | 2010 | 3 | |
| 9 | 2010 | 3 | |
| 10 | 2011 | 1 |
About Daniel Smilkov
Daniel Smilkov is a scholar working on Statistical and Nonlinear Physics, Geometry and Topology, Computer Vision and Pattern Recognition, Artificial Intelligence and Modeling and Simulation, having authored 10 papers that have together received 533 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (6 papers), Opinion Dynamics and Social Influence (4 papers), COVID-19 epidemiological studies (2 papers), Data Visualization and Analytics (2 papers), Graph theory and applications (2 papers), Caching and Content Delivery (1 paper), Stochastic processes and statistical mechanics (1 paper) and Computational Physics and Python Applications (1 paper). The work is most often cited by research in Health Informatics (69 citations), Artificial Intelligence (253 citations), Computer Vision and Pattern Recognition (145 citations), Safety Research (52 citations) and Statistical and Nonlinear Physics (65 citations). Daniel Smilkov has collaborated with scholars based in North Macedonia, United States and Italy. Frequent co-authors include Martin Wattenberg, Fernanda Viégas, Ljupčo Kocarev, James Wexler, Kanit Wongsuphasawat, Dilip Krishnan, Jason Hipp, Martin C. Stumpe, Michael Terry and Been Kim. Their work appears in journals such as Physica A Statistical Mechanics and its Applications, Scientific Reports, IEEE Transactions on Visualization and Computer Graphics, arXiv (Cornell University) and Physical Review E.
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.