Grady Williams
Impact in
-
- Advanced Control Systems Optimization
- Fault Detection and Control Systems
- Control Systems and Identification
- Automotive Engineering top 5%
- Autonomous Vehicle Technology and Safety
- Vehicle Dynamics and Control Systems
Papers in
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- Advanced Control Systems Optimization 7
- Control Systems and Identification 4
- Fault Detection and Control Systems 3
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- Reinforcement Learning in Robotics 3
- Co-authors
- Evangelos A. Theodorou (10 shared papers)Brian Goldfain (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)
- Journals
- IEEE Robotics and Automation Letters (2 papers)Journal of Guidance Control and Dynamics (1 paper)
- Partner nations
- United States
In The Last Decade
Grady Williams
10 papers receiving 832 citations
Peers
Comparison fields: 5 of 69
- Control and Systems Engineering 434
- Automotive Engineering 208
- Computer Vision and Pattern Recognition 314
- Artificial Intelligence 231
- Aerospace Engineering 148
Countries citing papers authored by Grady Williams
This map shows the geographic impact of Grady Williams'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 Grady Williams with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Grady Williams more than expected).
Fields of papers citing papers by Grady Williams
This network shows the impact of papers produced by Grady Williams. 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 Grady Williams. The network helps show where Grady Williams may publish in the future.
Co-authors
The 8 scholars most cited alongside Grady Williams, 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 | 2017 | 179 | |
| 4 | 2018 | 45 | |
| 5 | 2019 | 37 | |
| 6 | 2021 | 34 | |
| 7 | 2018 | 32 | |
| 8 | Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control | 2017 | 19 |
| 9 | Locally Weighted Regression Pseudo-Rehearsal for Adaptive Model Predictive Control | 2019 | 7 |
| 10 | 2019 | 6 |
About Grady Williams
Grady Williams 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 10 papers that have together received 856 indexed citations. Recurring topics across this work include Advanced Control Systems Optimization (7 papers), Control Systems and Identification (4 papers), Reinforcement Learning in Robotics (3 papers), Fault Detection and Control Systems (3 papers), Autonomous Vehicle Technology and Safety (2 papers), Robotics and Sensor-Based Localization (1 paper), Markov Chains and Monte Carlo Methods (1 paper) and Robotic Path Planning Algorithms (1 paper). The work is most often cited by research in Control and Systems Engineering (434 citations), Automotive Engineering (208 citations), Computer Vision and Pattern Recognition (314 citations), Artificial Intelligence (231 citations) and Aerospace Engineering (148 citations). Grady Williams has collaborated with scholars based in United States. Frequent co-authors include Evangelos A. Theodorou, Brian Goldfain, 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 and Journal of Guidance Control and Dynamics.
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.