Evan Racah

872 total citations
9 papers, 149 citations indexed

About

Evan Racah is a scholar working on Artificial Intelligence, Molecular Biology and Nuclear and High Energy Physics. According to data from OpenAlex, Evan Racah has authored 9 papers receiving a total of 149 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 2 papers in Molecular Biology and 2 papers in Nuclear and High Energy Physics. Recurrent topics in Evan Racah's work include Computational Physics and Python Applications (4 papers), Astrophysics and Cosmic Phenomena (2 papers) and Algorithms and Data Compression (2 papers). Evan Racah is often cited by papers focused on Computational Physics and Python Applications (4 papers), Astrophysics and Cosmic Phenomena (2 papers) and Algorithms and Data Compression (2 papers). Evan Racah collaborates with scholars based in United States, Canada and South Korea. Evan Racah's co-authors include Prabhat, Wahid Bhimji, Md. Mostofa Ali Patwary, Narayanan Sundaram, Pradeep Dubey, C. E. Tull, Peter Sadowski, Satish Nadathur, Ioannis Mitliagkas and Tareq B. Malas and has published in prestigious journals such as arXiv (Cornell University), eScholarship (California Digital Library) and OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information).

In The Last Decade

Evan Racah

9 papers receiving 142 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Evan Racah United States	7	56	53	39	23	16	9	149
Peiyan Dong United States	10	76 1.4×	69 1.3×	17 0.4×	4 0.2×	3 0.2×	25	214
Tong Zhou China	6	38 0.7×	37 0.7×	12 0.3×	10 0.4×	1 0.1×	46	128
Kevin Zhang United States	7	40 0.7×	33 0.6×	31 0.8×	3 0.1×	1 0.1×	30	140
Peter Stütz Germany	7	31 0.6×	100 1.9×	20 0.5×	14 0.6×		61	192
Antony Browne United Kingdom	6	125 2.2×	22 0.4×	18 0.5×	2 0.1×	4 0.3×	24	214
Xinpeng Li China	7	45 0.8×	121 2.3×	31 0.8×	2 0.1×		30	191
Jérémy E. Cohen France	7	22 0.4×	53 1.0×	13 0.3×	3 0.1×	2 0.1×	21	232
Tianyuan Yu China	8	70 1.3×	103 1.9×	66 1.7×	5 0.2×		33	239
Jelena Luketina Switzerland	4	69 1.2×	34 0.6×	8 0.2×	4 0.2×	1 0.1×	4	143

Countries citing papers authored by Evan Racah

Since Specialization

Citations

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

Fields of papers citing papers by Evan Racah

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Evan Racah

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

All Works

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9 of 9 papers shown

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

Seijen, Harm van, et al.. (2020). The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning. PolyPublie (École Polytechnique de Montréal). 33. 6562–6572. 1 indexed citations

Racah, Evan, Christopher Beckham, Tegan Maharaj, Prabhat, & Christopher Pal. (2017). Semi-Supervised Detection of Extreme Weather Events in Large Climate Datasets. arXiv (Cornell University). 7 indexed citations

Kurth, Thorsten, Jian Zhang, Satish Nadathur, et al.. (2017). Deep learning at 15PF. 1–11. 41 indexed citations

Racah, Evan, Peter Sadowski, Wahid Bhimji, et al.. (2016). Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks. eScholarship (California Digital Library). 892–897. 7 indexed citations

Racah, Evan, Peter Sadowski, Wahid Bhimji, et al.. (2016). Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks. arXiv (Cornell University). 9 indexed citations

Patwary, Md. Mostofa Ali, Nadathur Satish, Narayanan Sundaram, et al.. (2016). PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures. arXiv (Cornell University). 494–503. 26 indexed citations

Gittens, Alex, Jiyan Yang, Michael F. Ringenburg, et al.. (2016). A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 3 indexed citations

Gittens, Alex, Aditya Devarakonda, Evan Racah, et al.. (2016). Matrix factorizations at scale: A comparison of scientific data analytics in spark and C+MPI using three case studies. 204–213. 26 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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