Mathew Monfort

4.0k total citations · 1 hit paper
11 papers, 480 citations indexed

About

Mathew Monfort is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Mathew Monfort has authored 11 papers receiving a total of 480 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 3 papers in Control and Systems Engineering. Recurrent topics in Mathew Monfort's work include Reinforcement Learning in Robotics (4 papers), Robot Manipulation and Learning (3 papers) and Human Pose and Action Recognition (3 papers). Mathew Monfort is often cited by papers focused on Reinforcement Learning in Robotics (4 papers), Robot Manipulation and Learning (3 papers) and Human Pose and Action Recognition (3 papers). Mathew Monfort collaborates with scholars based in United States, United Kingdom and China. Mathew Monfort's co-authors include Wongun Choi, Yizhou Wang, Ying Wu, Chris Baker, Yibiao Zhao, Yifei Xu, Brian D. Ziebart, Anqi Liu, Patrick Lucey and Iain Matthews and has published in prestigious journals such as DSpace@MIT (Massachusetts Institute of Technology), International Conference on Artificial Intelligence and Statistics and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Mathew Monfort

11 papers receiving 456 citations

Hit Papers

Multi-Agent Tensor Fusion for Contextual Trajectory Predi... 2019 2026 2021 2023 2019 100 200 300

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mathew Monfort United States 7 284 199 158 119 96 11 480
Carla Maria Modena Italy 12 107 0.4× 468 2.4× 70 0.4× 42 0.4× 59 0.6× 17 621
Hengbo Ma United States 12 332 1.2× 235 1.2× 159 1.0× 128 1.1× 91 0.9× 15 460
Anirudh Vemula United States 5 341 1.2× 295 1.5× 176 1.1× 136 1.1× 80 0.8× 9 475
Shawn Kimmel United States 7 264 0.9× 145 0.7× 56 0.4× 57 0.5× 18 0.2× 11 400
Yi‐Ting Chen Taiwan 10 96 0.3× 159 0.8× 92 0.6× 39 0.3× 25 0.3× 40 435
Takashi Bando Japan 14 273 1.0× 142 0.7× 205 1.3× 84 0.7× 108 1.1× 41 586
James A Misener United States 13 242 0.9× 78 0.4× 27 0.2× 153 1.3× 95 1.0× 41 530
Hua-Tsung Chen Taiwan 15 51 0.2× 711 3.6× 139 0.9× 10 0.1× 20 0.2× 54 820
Kazuhito Takenaka Japan 12 195 0.7× 90 0.5× 156 1.0× 57 0.5× 73 0.8× 22 410
Yoshihiro Nishiwaki Japan 5 242 0.9× 63 0.3× 67 0.4× 67 0.6× 72 0.8× 6 325

Countries citing papers authored by Mathew Monfort

Since Specialization
Citations

This map shows the geographic impact of Mathew Monfort'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 Mathew Monfort with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mathew Monfort more than expected).

Fields of papers citing papers by Mathew Monfort

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mathew Monfort. 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 Mathew Monfort. The network helps show where Mathew Monfort may publish in the future.

Co-authorship network of co-authors of Mathew Monfort

This figure shows the co-authorship network connecting the top 25 collaborators of Mathew Monfort. A scholar is included among the top collaborators of Mathew Monfort 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 Mathew Monfort. Mathew Monfort is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

11 of 11 papers shown
1.
Goel, Karan, Arjun Desai, Lingjiao Chen, et al.. (2024). Model ChangeLists: Characterizing Updates to ML Models. 2432–2453. 1 indexed citations
2.
Xiao, Tete, et al.. (2019). Reasoning About Human-Object Interactions Through Dual Attention Networks. 3918–3927. 24 indexed citations
3.
Xu, Yifei, Mathew Monfort, Wongun Choi, et al.. (2019). Multi-Agent Tensor Fusion for Contextual Trajectory Prediction. 12118–12126. 320 indexed citations breakdown →
4.
Monfort, Mathew, et al.. (2018). A Large Scale Multi-Label Action Dataset for Video Understanding. 1 indexed citations
5.
Monfort, Mathew, Matthew Johnson, Aude Oliva, & Katja Hofmann. (2017). Asynchronous Data Aggregation for Training End to End Visual Control Networks. Adaptive Agents and Multi-Agents Systems. 530–537. 3 indexed citations
6.
Schultz, Christopher J., et al.. (2017). Goal-predictive robotic teleoperation from noisy sensors. 3. 5377–5383. 15 indexed citations
7.
Chen, Xiangli, Mathew Monfort, Anqi Liu, & Brian D. Ziebart. (2016). Robust Covariate Shift Regression. International Conference on Artificial Intelligence and Statistics. 1270–1279. 13 indexed citations
8.
Monfort, Mathew, Brenden M. Lake, Brian D. Ziebart, Patrick Lucey, & Joshua B. Tenenbaum. (2015). Softstar: heuristic-guided probabilistic inference. DSpace@MIT (Massachusetts Institute of Technology). 28. 2764–2772. 3 indexed citations
9.
Lucey, Patrick, Alina Bialkowski, Mathew Monfort, Peter Carr, & Iain Matthews. (2015). "Quality vs Quantity": Improved Shot Prediction in Soccer using Strategic Features from Spatiotemporal Data. Queensland's institutional digital repository (The University of Queensland). 55 indexed citations
10.
Byravan, Arunkumar, Mathew Monfort, Brian D. Ziebart, Byron Boots, & Dieter Fox. (2015). Graph-based inverse optimal control for robot manipulation. 1874–1890. 13 indexed citations
11.
Monfort, Mathew, Anqi Liu, & Brian D. Ziebart. (2015). Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation. Proceedings of the AAAI Conference on Artificial Intelligence. 29(1). 32 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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