Eugene Katsevich

597 citations
12 papers · 260 · h-index 8

Impact in

Papers in

    • Gene expression and cancer classification 3
    • Single-cell and spatial transcriptomics 3
    • CRISPR and Genetic Engineering 3
    • Statistical Methods in Clinical Trials 5
    • Statistical Methods and Inference 4
    • Statistical Methods and Bayesian Inference 2

Eugene Katsevich

12 papers receiving 259 citations

Peers

Eugene Katsevich
Comparison fields: 5 of 58
  • Structural Biology 38
  • Statistics and Probability 45
  • Surfaces, Coatings and Films 29
  • Genetics 54
  • Molecular Biology 120
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Citations per field
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Citations per year

Countries citing papers authored by Eugene Katsevich

Since Specialization
Citations

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

Fields of papers citing papers by Eugene Katsevich

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Eugene Katsevich, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Eugene Katsevich Line = papers co-authored together Eugene Katsevich links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1 202396
2 201545
3 202040
4 201219
5 202118
6 201912
7 20227
8 20247
9 20226
10 20245
11
Towards "simultaneous selective inference": post-hoc bounds on the false discovery proportion
20183
12 20242

About Eugene Katsevich

Eugene Katsevich is a scholar working on Molecular Biology, Statistics and Probability, Structural Biology, Mathematical Physics and Computational Mechanics, having authored 12 papers that have together received 260 indexed citations. Recurring topics across this work include Statistical Methods in Clinical Trials (5 papers), Statistical Methods and Inference (4 papers), Gene expression and cancer classification (3 papers), Single-cell and spatial transcriptomics (3 papers), CRISPR and Genetic Engineering (3 papers), Statistical Methods and Bayesian Inference (2 papers), VLSI and Analog Circuit Testing (1 paper) and Advanced Electron Microscopy Techniques and Applications (1 paper). The work is most often cited by research in Structural Biology (38 citations), Statistics and Probability (45 citations), Surfaces, Coatings and Films (29 citations), Genetics (54 citations) and Molecular Biology (120 citations). Eugene Katsevich has collaborated with scholars based in United States, Netherlands and Belgium. Frequent co-authors include Alexander Katsevich, Amit Singer, Chiara Sabatti, Aaditya Ramdas, Matteo Sesia, Emmanuel J. Candès, Stephen Bates, Timothy Barry, Kathryn Roeder and Zharko Daniloski. Their work appears in journals such as Nature Communications, Electronic Journal of Statistics, Inverse Problems, Genome biology and Scandinavian Journal of Statistics.

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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