Justin Fu

2.4k total citations
14 papers, 145 citations indexed

About

Justin Fu is a scholar working on Artificial Intelligence, Automotive Engineering and Management Science and Operations Research. According to data from OpenAlex, Justin Fu has authored 14 papers receiving a total of 145 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 3 papers in Automotive Engineering and 2 papers in Management Science and Operations Research. Recurrent topics in Justin Fu's work include Reinforcement Learning in Robotics (7 papers), Autonomous Vehicle Technology and Safety (3 papers) and Adversarial Robustness in Machine Learning (3 papers). Justin Fu is often cited by papers focused on Reinforcement Learning in Robotics (7 papers), Autonomous Vehicle Technology and Safety (3 papers) and Adversarial Robustness in Machine Learning (3 papers). Justin Fu collaborates with scholars based in United States, Germany and United Kingdom. Justin Fu's co-authors include Sergey Levine, George Tucker, Aviral Kumar, Sherry X. Yang, Dragomir Anguelov, Shimon Whiteson, Yiren Lu, Siddharth Verma, Mark Palatucci and Yoram Bachrach and has published in prestigious journals such as Thin Solid Films, Journal of Artificial Intelligence Research and arXiv (Cornell University).

In The Last Decade

Justin Fu

13 papers receiving 139 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Justin Fu United States	6	96	36	35	33	13	14	145
Alexander Sasha Vezhnevets United Kingdom	4	107 1.1×	11 0.3×	33 0.9×	27 0.8×	16 1.2×	6	140
Bilal Piot France	3	89 0.9×	13 0.4×	47 1.3×	20 0.6×	12 0.9×	5	132
Maximilian Igl United Kingdom	6	47 0.5×	25 0.7×	19 0.5×	21 0.6×	6 0.5×	12	85
Joshua Romoff Canada	2	75 0.8×	15 0.4×	13 0.4×	15 0.5×	9 0.7×	4	113
Chenjia Bai China	6	94 1.0×	16 0.4×	41 1.2×	27 0.8×	7 0.5×	26	173
Bei Peng United States	8	159 1.7×	7 0.2×	61 1.7×	21 0.6×	14 1.1×	18	199
Nicholay Topin United States	5	88 0.9×	7 0.2×	16 0.5×	24 0.7×	7 0.5×	9	143
Emmanuel Rachelson France	7	44 0.5×	11 0.3×	29 0.8×	16 0.5×	11 0.8×	21	160
Mark Koren United States	3	65 0.7×	108 3.0×	55 1.6×	17 0.5×	14 1.1×	3	181
Abbas Abdolmaleki Portugal	9	112 1.2×	10 0.3×	46 1.3×	29 0.9×	15 1.2×	29	163

Countries citing papers authored by Justin Fu

Since Specialization

Citations

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

Fields of papers citing papers by Justin Fu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Justin Fu

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

All Works

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14 of 14 papers shown

Anguelov, Dragomir, Justin Fu, Rowan McAllister, et al.. (2023). Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research. 7730–7742.

Lu, Yiren, Justin Fu, George Tucker, et al.. (2023). Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios. 7553–7560. 25 indexed citations

Verma, Siddharth, Justin Fu, Sherry X. Yang, & Sergey Levine. (2022). CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 12 indexed citations

Palatucci, Mark, Brandyn White, Alex Kuefler, et al.. (2022). Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 8652–8659. 29 indexed citations

Yang, Sherry X., et al.. (2022). Context-Aware Language Modeling for Goal-Oriented Dialogue Systems. 2351–2366. 3 indexed citations

Fu, Justin & Sergey Levine. (2021). Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation. arXiv (Cornell University). 1 indexed citations

Fu, Justin, Andrea Tacchetti, Julien Pérolat, & Yoram Bachrach. (2021). Evaluating Strategic Structures in Multi-Agent Inverse Reinforcement Learning. Journal of Artificial Intelligence Research. 71. 925–951. 7 indexed citations

Fu, Justin, Aviral Kumar, Ofir Nachum, George Tucker, & Sergey Levine. (2020). Datasets for Data-Driven Reinforcement Learning. arXiv (Cornell University). 2 indexed citations

Gupta, Abhishek, et al.. (2019). Learning To Reach Goals Without Reinforcement Learning. arXiv (Cornell University). 3 indexed citations

10.

Kumar, Aviral, et al.. (2019). Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. arXiv (Cornell University). 32. 11761–11771. 30 indexed citations

11.

Fu, Justin, et al.. (2019). Diagnosing Bottlenecks in Deep Q-learning Algorithms. arXiv (Cornell University). 2021–2030. 4 indexed citations

12.

Fu, Justin, et al.. (2018). Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition. arXiv (Cornell University). 31. 8538–8547. 4 indexed citations

13.

Fu, Justin, et al.. (2017). EX2: Exploration with Exemplar Models for Deep Reinforcement Learning. arXiv (Cornell University). 30. 2577–2587. 24 indexed citations

14.

Cheng, Quan, et al.. (1999). Monolayer and epi-fluorescence microscopy studies of amino acid derivatized diacetylene lipids. Thin Solid Films. 345(2). 292–299. 1 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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