Dan Feldman
Impact in
- Computational Mathematics top 10%
- Artificial Intelligence top 2%
- Privacy-Preserving Technologies in Data
- Machine Learning and Algorithms
- Cryptography and Data Security
- Stochastic Gradient Optimization Techniques
Papers in
-
- Machine Learning and Algorithms 14
- Stochastic Gradient Optimization Techniques 10
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- Face and Expression Recognition 8
- Advanced Neural Network Applications 7
- Co-authors
- Daniela Rus (21 shared papers)Christian Sohler (5 shared papers)Morteza Monemizadeh (3 shared papers)Amos Fiat (3 shared papers)Cynthia Sung (4 shared papers)Matthew Faulkner (3 shared papers)Andreas Krause (3 shared papers)Vincent Jacob (1 shared paper)
- Journals
- Sensors (3 papers)IEEE Robotics and Automation Letters (2 papers)IEEE Transactions on Knowledge and Data Engineering (2 papers)Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2 papers)IEEE Transactions on Neural Networks and Learning Systems (2 papers)
- Partner nations
- IsraelUnited StatesGermany
In The Last Decade
Dan Feldman
88 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 121
- Computational Mathematics 11
- Artificial Intelligence 568
- Signal Processing 184
- Computer Vision and Pattern Recognition 337
- Computer Graphics and Computer-Aided Design 47
Countries citing papers authored by Dan Feldman
This map shows the geographic impact of Dan Feldman'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 Dan Feldman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Feldman more than expected).
Fields of papers citing papers by Dan Feldman
This network shows the impact of papers produced by Dan Feldman. 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 Dan Feldman. The network helps show where Dan Feldman may publish in the future.
Co-authors
The 25 scholars most cited alongside Dan Feldman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 90 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2007 | 112 | |
| 2 | 2007 | 93 | |
| 3 | 2009 | 67 | |
| 4 | 2020 | 60 | |
| 5 | Scalable Training of Mixture Models via Coresets | 2011 | 54 |
| 6 | 2012 | 44 | |
| 7 | 2010 | 30 | |
| 8 | 2020 | 27 | |
| 9 | Coresets for k-Segmentation of Streaming Data | 2014 | 26 |
| 10 | 2019 | 26 | |
| 11 | 2006 | 25 | |
| 12 | 2017 | 24 | |
| 13 | 2010 | 23 | |
| 14 | 2013 | 22 | |
| 15 | 2015 | 22 | |
| 16 | 2012 | 22 | |
| 17 | 2018 | 21 | |
| 18 | 2021 | 18 | |
| 19 | 2016 | 18 | |
| 20 | 2017 | 18 |
About Dan Feldman
Dan Feldman is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mechanics, Signal Processing and Computational Theory and Mathematics, having authored 90 papers that have together received 1.2k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (20 papers), Machine Learning and Algorithms (14 papers), Complexity and Algorithms in Graphs (13 papers), Data Management and Algorithms (10 papers), Stochastic Gradient Optimization Techniques (10 papers), Robotics and Sensor-Based Localization (10 papers), Face and Expression Recognition (8 papers) and Advanced Neural Network Applications (7 papers). The work is most often cited by research in Computational Mathematics (11 citations), Artificial Intelligence (568 citations), Signal Processing (184 citations), Computer Vision and Pattern Recognition (337 citations) and Computer Graphics and Computer-Aided Design (47 citations). Dan Feldman has collaborated with scholars based in Israel, United States and Germany. Frequent co-authors include Daniela Rus, Christian Sohler, Morteza Monemizadeh, Amos Fiat, Cynthia Sung, Matthew Faulkner, Andreas Krause, Vincent Jacob, Irina Erchova and Daniel E. Shulz. Their work appears in journals such as Sensors, IEEE Robotics and Automation Letters, IEEE Transactions on Knowledge and Data Engineering, Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery and IEEE Transactions on Neural Networks and Learning Systems.
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.