Aurick Zhou

7.6k total citations · 1 hit paper
8 papers, 364 citations indexed

About

Aurick Zhou is a scholar working on Artificial Intelligence, Automotive Engineering and Control and Systems Engineering. According to data from OpenAlex, Aurick Zhou has authored 8 papers receiving a total of 364 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 2 papers in Automotive Engineering and 1 paper in Control and Systems Engineering. Recurrent topics in Aurick Zhou's work include Reinforcement Learning in Robotics (4 papers), Autonomous Vehicle Technology and Safety (2 papers) and Adversarial Robustness in Machine Learning (2 papers). Aurick Zhou is often cited by papers focused on Reinforcement Learning in Robotics (4 papers), Autonomous Vehicle Technology and Safety (2 papers) and Adversarial Robustness in Machine Learning (2 papers). Aurick Zhou collaborates with scholars based in United States. Aurick Zhou's co-authors include Sergey Levine, Tuomas Haarnoja, Pieter Abbeel, Khaled S. Refaat, Nigamaa Nayakanti, Benjamin Sapp, Rami Al‐Rfou, Kratarth Goel, Kate Rakelly and Deirdre Quillen and has published in prestigious journals such as arXiv (Cornell University), Neural Information Processing Systems and International Conference on Machine Learning.

In The Last Decade

Aurick Zhou

8 papers receiving 349 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.

Wayformer: Motion Forecasting via Simple & Efficient Attention Networks

2023 118 citations Nigamaa Nayakanti, Rami Al‐Rfou et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Aurick Zhou United States	6	196	103	95	87	52	8	364
David Isele United States	10	131 0.7×	106 1.0×	80 0.8×	83 1.0×	12 0.2×	37	280
Moritz Klischat Germany	9	72 0.4×	175 1.7×	66 0.7×	109 1.3×	13 0.3×	11	298
Stefan Witwicki United States	11	159 0.8×	35 0.3×	64 0.7×	40 0.5×	67 1.3×	27	349
Khaled S. Refaat United States	6	101 0.5×	232 2.3×	137 1.4×	56 0.6×	98 1.9×	11	368
Jinmei Shu China	4	78 0.4×	68 0.7×	73 0.8×	34 0.4×	35 0.7×	5	370
Chih‐Hong Cheng Germany	11	124 0.6×	45 0.4×	47 0.5×	41 0.5×	12 0.2×	31	311
Richard Liaw United States	7	113 0.6×	45 0.4×	45 0.5×	89 1.0×	19 0.4×	10	264
Monireh Dabaghchian United States	12	95 0.5×	46 0.4×	50 0.5×	82 0.9×	11 0.2×	16	402
Jianyu Chen China	6	130 0.7×	218 2.1×	127 1.3×	160 1.8×	19 0.4×	13	380
Junwei Liang Singapore	9	171 0.9×	27 0.3×	42 0.4×	39 0.4×	15 0.3×	16	360

Countries citing papers authored by Aurick Zhou

Since Specialization

Citations

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

Fields of papers citing papers by Aurick Zhou

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aurick Zhou

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

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Nayakanti, Nigamaa, Rami Al‐Rfou, Aurick Zhou, et al.. (2023). Wayformer: Motion Forecasting via Simple & Efficient Attention Networks. 2980–2987. 118 indexed citations breakdown →

2.

Seff, Ari, Dian Chen, Aurick Zhou, et al.. (2023). MotionLM: Multi-Agent Motion Forecasting as Language Modeling. 8545–8556. 35 indexed citations

3.

Zhou, Aurick & Sergey Levine. (2021). Bayesian Adaptation for Covariate Shift. Neural Information Processing Systems. 34. 6 indexed citations

4.

Zhou, Aurick & Sergey Levine. (2021). Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation. International Conference on Machine Learning. 12803–12812. 2 indexed citations

5.

Kumar, Aviral, Aurick Zhou, George Tucker, & Sergey Levine. (2020). Conservative Q-Learning for Offline Reinforcement Learning. arXiv (Cornell University). 33. 1179–1191. 5 indexed citations

6.

Rakelly, Kate, Aurick Zhou, Deirdre Quillen, Chelsea Finn, & Sergey Levine. (2019). Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables. arXiv (Cornell University). 5331–5340. 75 indexed citations

7.

Haarnoja, Tuomas, Aurick Zhou, Pieter Abbeel, & Sergey Levine. (2018). Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. International Conference on Machine Learning. 1861–1870. 108 indexed citations

8.

Haarnoja, Tuomas, Sehoon Ha, Aurick Zhou, et al.. (2018). Learning to Walk via Deep Reinforcement Learning. arXiv (Cornell University). 15 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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