Stephen McAleer

2.5k total citations · 1 hit paper
10 papers, 1.3k citations indexed

About

Stephen McAleer is a scholar working on Artificial Intelligence, Nuclear and High Energy Physics and Computer Networks and Communications. According to data from OpenAlex, Stephen McAleer has authored 10 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 2 papers in Nuclear and High Energy Physics and 1 paper in Computer Networks and Communications. Recurrent topics in Stephen McAleer's work include Reinforcement Learning in Robotics (3 papers), Neutrino Physics Research (2 papers) and Astrophysics and Cosmic Phenomena (2 papers). Stephen McAleer is often cited by papers focused on Reinforcement Learning in Robotics (3 papers), Neutrino Physics Research (2 papers) and Astrophysics and Cosmic Phenomena (2 papers). Stephen McAleer collaborates with scholars based in United States, China and Sweden. Stephen McAleer's co-authors include Ruqiang Yan, Pierre Baldi, Siyu Shao, Pierre Baldi, Forest Agostinelli, Alexander Shmakov, Christian Gläser, Yaodong Yang, S. W. Barwick and Andrew Browne and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Industrial Informatics and Nature Machine Intelligence.

In The Last Decade

Stephen McAleer

10 papers receiving 1.3k 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.

Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning

2018 1.2k citations Siyu Shao, Stephen McAleer et al. IEEE Transactions on Industrial Informatics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Stephen McAleer United States	6	949	531	298	292	125	10	1.3k
Shen Yan China	12	1.2k 1.2×	532 1.0×	369 1.2×	360 1.2×	99 0.8×	21	1.5k
Jingyao Wu China	11	867 0.9×	435 0.8×	318 1.1×	208 0.7×	121 1.0×	26	1.2k
Funa Zhou China	13	643 0.7×	310 0.6×	268 0.9×	189 0.6×	110 0.9×	64	1.0k
Xiaoyang Liu China	12	651 0.7×	447 0.8×	139 0.5×	224 0.8×	54 0.4×	21	878
Zhiyi He China	16	1.2k 1.3×	619 1.2×	232 0.8×	337 1.2×	91 0.7×	35	1.5k
Yuantao Yang China	12	1.1k 1.1×	623 1.2×	181 0.6×	371 1.3×	38 0.3×	17	1.3k
Xianzhi Wang China	16	977 1.0×	554 1.0×	130 0.4×	407 1.4×	56 0.4×	45	1.3k
Yongfang Mao China	17	684 0.7×	387 0.7×	140 0.5×	216 0.7×	56 0.4×	49	998
Jinyu Tong China	19	833 0.9×	513 1.0×	147 0.5×	288 1.0×	47 0.4×	73	1.1k
Chuanjiang Li China	14	519 0.5×	268 0.5×	265 0.9×	144 0.5×	65 0.5×	35	774

Countries citing papers authored by Stephen McAleer

Since Specialization

Citations

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

Fields of papers citing papers by Stephen McAleer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Stephen McAleer

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

All Works

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10 of 10 papers shown

1.

Farina, Gabriele, et al.. (2024). Steering No-Regret Learners to a Desired Equilibrium. 73–74. 1 indexed citations

2.

McAleer, Stephen, et al.. (2024). ASP: Learn a Universal Neural Solver!. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(6). 4102–4114. 8 indexed citations

3.

Panageas, Ioannis, et al.. (2023). Algorithms and Complexity for Computing Nash Equilibria in Adversarial Team Games. 89–89. 2 indexed citations

4.

Gläser, Christian, et al.. (2022). Deep-learning-based reconstruction of the neutrino direction and energy for in-ice radio detectors. Astroparticle Physics. 145. 102781–102781. 9 indexed citations

5.

Wan, Ziyu, Bo Liu, Stephen McAleer, et al.. (2021). Neural Auto-Curricula in Two-Player Zero-Sum Games. UCL Discovery (University College London). 34. 2 indexed citations

6.

Gläser, Christian, et al.. (2021). Deep learning reconstruction of the neutrino energy with a shallow Askaryan detector. Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021). 1051–1051. 2 indexed citations

7.

McAleer, Stephen, Alexander Fast, Magdalene J. Seiler, et al.. (2021). Deep Learning–Assisted Multiphoton Microscopy to Reduce Light Exposure and Expedite Imaging in Tissues With High and Low Light Sensitivity. Translational Vision Science & Technology. 10(12). 30–30. 11 indexed citations

8.

Agostinelli, Forest, Stephen McAleer, Alexander Shmakov, & Pierre Baldi. (2019). Solving the Rubik’s cube with deep reinforcement learning and search. Nature Machine Intelligence. 1(8). 356–363. 77 indexed citations

9.

McAleer, Stephen, Forest Agostinelli, Alexander Shmakov, & Pierre Baldi. (2018). Solving the Rubik's Cube with Approximate Policy Iteration. International Conference on Learning Representations. 7 indexed citations

10.

Shao, Siyu, Stephen McAleer, Ruqiang Yan, & Pierre Baldi. (2018). Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning. IEEE Transactions on Industrial Informatics. 15(4). 2446–2455. 1180 indexed citations breakdown →

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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