Brian Goldfain
Impact in
- Automotive Engineering top 5%
- Autonomous Vehicle Technology and Safety
- Vehicle Dynamics and Control Systems
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- Advanced Control Systems Optimization
- Fault Detection and Control Systems
- Control Systems and Identification
Papers in
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- Advanced Control Systems Optimization 5
- Control Systems and Identification 3
- Fault Detection and Control Systems 2
- Traffic control and management 1
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- Reinforcement Learning in Robotics 3
- Gaussian Processes and Bayesian Inference 1
- Co-authors
- Grady Williams (7 shared papers)Evangelos A. Theodorou (7 shared papers)James M. Rehg (7 shared papers)Paul Drews (6 shared papers)Nolan Wagener (1 shared paper)Byron Boots (1 shared paper)Kamil Saigol (1 shared paper)Keuntaek Lee (1 shared paper)
- Partner nations
- United States
In The Last Decade
Brian Goldfain
7 papers receiving 617 citations
Peers
Comparison fields: 5 of 67
- Automotive Engineering 179
- Control and Systems Engineering 320
- Computer Vision and Pattern Recognition 220
- Artificial Intelligence 187
- Aerospace Engineering 87
Countries citing papers authored by Brian Goldfain
This map shows the geographic impact of Brian Goldfain'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 Brian Goldfain with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian Goldfain more than expected).
Fields of papers citing papers by Brian Goldfain
This network shows the impact of papers produced by Brian Goldfain. 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 Brian Goldfain. The network helps show where Brian Goldfain may publish in the future.
Co-authors
The 8 scholars most cited alongside Brian Goldfain, 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 | 2017 | 264 | |
| 2 | 2016 | 233 | |
| 3 | 2018 | 45 | |
| 4 | 2019 | 37 | |
| 5 | 2018 | 32 | |
| 6 | Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control | 2017 | 19 |
| 7 | Locally Weighted Regression Pseudo-Rehearsal for Adaptive Model Predictive Control | 2019 | 7 |
About Brian Goldfain
Brian Goldfain is a scholar working on Control and Systems Engineering, Artificial Intelligence, Automotive Engineering, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 7 papers that have together received 637 indexed citations. Recurring topics across this work include Advanced Control Systems Optimization (5 papers), Reinforcement Learning in Robotics (3 papers), Control Systems and Identification (3 papers), Autonomous Vehicle Technology and Safety (2 papers), Fault Detection and Control Systems (2 papers), Simulation Techniques and Applications (1 paper), Traffic control and management (1 paper) and Gaussian Processes and Bayesian Inference (1 paper). The work is most often cited by research in Automotive Engineering (179 citations), Control and Systems Engineering (320 citations), Computer Vision and Pattern Recognition (220 citations), Artificial Intelligence (187 citations) and Aerospace Engineering (87 citations). Brian Goldfain has collaborated with scholars based in United States. Frequent co-authors include Grady Williams, Evangelos A. Theodorou, James M. Rehg, Paul Drews, Nolan Wagener, Byron Boots, Kamil Saigol and Keuntaek Lee. Their work appears in journals such as IEEE Robotics and Automation Letters.
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.