Brian Goldfain

1.1k total citations · 1 hit paper
7 papers, 625 citations indexed

About

Brian Goldfain is a scholar working on Control and Systems Engineering, Artificial Intelligence and Automotive Engineering. According to data from OpenAlex, Brian Goldfain has authored 7 papers receiving a total of 625 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Control and Systems Engineering, 3 papers in Artificial Intelligence and 2 papers in Automotive Engineering. Recurrent topics in Brian Goldfain's work include Advanced Control Systems Optimization (5 papers), Control Systems and Identification (3 papers) and Reinforcement Learning in Robotics (3 papers). Brian Goldfain is often cited by papers focused on Advanced Control Systems Optimization (5 papers), Control Systems and Identification (3 papers) and Reinforcement Learning in Robotics (3 papers). Brian Goldfain collaborates with scholars based in United States. Brian Goldfain's co-authors include Grady Williams, James M. Rehg, Evangelos A. Theodorou, Paul Drews, Nolan Wagener, Byron Boots, Kamil Saigol and Keuntaek Lee and has published in prestigious journals such as IEEE Robotics and Automation Letters.

In The Last Decade

Brian Goldfain

7 papers receiving 604 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Information theoretic MPC for model-based reinforcement learning

2017 262 citations Grady Williams, Nolan Wagener et al. profile →

Peers

Brian Goldfain

CSE

CVPR

AI

AE

Paul Drews United States

Grady Williams United States

Daniel Axehill Sweden

Bruno Brito Netherlands

Nolan Wagener United States

Edward Schmerling United States

Jeremy Gillula United States

Georges S. Aoude United States

Melissa Greeff Canada

Mark Cutler United States

Paul Drews United States

Brian Goldfain

313 ×1.0

CSE

215 ×1.0

CVPR

199 ×1.1

AI

176 ×1.0

AE

86 ×1.0

AE

Citations per year, relative to Brian Goldfain Brian Goldfain (= 1×) peers Paul Drews

Countries citing papers authored by Brian Goldfain

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Brian Goldfain

This figure shows the co-authorship network connecting the top 25 collaborators of Brian Goldfain. A scholar is included among the top collaborators of Brian Goldfain based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Brian Goldfain. Brian Goldfain is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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7 of 7 papers shown

1.

Williams, Grady, et al.. (2019). Locally Weighted Regression Pseudo-Rehearsal for Adaptive Model Predictive Control. 969–978. 7 indexed citations

2.

Drews, Paul, Grady Williams, Brian Goldfain, Evangelos A. Theodorou, & James M. Rehg. (2019). Vision-Based High-Speed Driving With a Deep Dynamic Observer. IEEE Robotics and Automation Letters. 4(2). 1564–1571. 36 indexed citations

3.

Williams, Grady, Brian Goldfain, Paul Drews, et al.. (2018). Robust Sampling Based Model Predictive Control with Sparse Objective Information. 45 indexed citations

4.

Williams, Grady, Brian Goldfain, Paul Drews, James M. Rehg, & Evangelos A. Theodorou. (2018). Best Response Model Predictive Control for Agile Interactions Between Autonomous Ground Vehicles. 2403–2410. 32 indexed citations

5.

Drews, Paul, Grady Williams, Brian Goldfain, Evangelos A. Theodorou, & James M. Rehg. (2017). Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control. 133–142. 19 indexed citations

6.

Williams, Grady, Nolan Wagener, Brian Goldfain, et al.. (2017). Information theoretic MPC for model-based reinforcement learning. 1714–1721. 262 indexed citations breakdown →

7.

Williams, Grady, Paul Drews, Brian Goldfain, James M. Rehg, & Evangelos A. Theodorou. (2016). Aggressive driving with model predictive path integral control. 1433–1440. 224 indexed citations

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.

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