Sayash Kapoor

1.3k total citations · 1 hit paper
15 papers, 435 citations indexed

About

Sayash Kapoor is a scholar working on Artificial Intelligence, Safety Research and Management Science and Operations Research. According to data from OpenAlex, Sayash Kapoor has authored 15 papers receiving a total of 435 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 3 papers in Safety Research and 3 papers in Management Science and Operations Research. Recurrent topics in Sayash Kapoor's work include Explainable Artificial Intelligence (XAI) (4 papers), Ethics and Social Impacts of AI (3 papers) and Advanced Bandit Algorithms Research (3 papers). Sayash Kapoor is often cited by papers focused on Explainable Artificial Intelligence (XAI) (4 papers), Ethics and Social Impacts of AI (3 papers) and Advanced Bandit Algorithms Research (3 papers). Sayash Kapoor collaborates with scholars based in United States, India and Switzerland. Sayash Kapoor's co-authors include Arvind Narayanan, L. Elisa Celis, Nisheeth K. Vishnoi, Angelina Wang, Solon Barocas, Kumar Kshitij Patel, Purushottam Kar, Rishi Bommasani, Peter Henderson and Percy Liang and has published in prestigious journals such as Nature, Science and Science Advances.

In The Last Decade

Sayash Kapoor

15 papers receiving 418 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.

Leakage and the reproducibility crisis in machine-learning-based science

2023 260 citations Sayash Kapoor, Arvind Narayanan Patterns profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sayash Kapoor United States	9	153	57	44	39	38	15	435
Richard Tomsett United Kingdom	9	262 1.7×	74 1.3×	49 1.1×	67 1.7×	29 0.8×	17	614
Ángel Fernández-Leal Spain	6	157 1.0×	34 0.6×	56 1.3×	14 0.4×	26 0.7×	9	411
José Bobes-Bascarán Spain	4	155 1.0×	36 0.6×	60 1.4×	15 0.4×	21 0.6×	7	383
Hal Hodson United Kingdom	7	91 0.6×	47 0.8×	102 2.3×	20 0.5×	50 1.3×	67	429
Giulia Vilone Ireland	7	311 2.0×	48 0.8×	85 1.9×	11 0.3×	29 0.8×	11	490
Amandalynne Paullada United States	5	149 1.0×	58 1.0×	24 0.5×	19 0.5×	24 0.6×	10	343
Katharina Holzinger Austria	7	160 1.0×	62 1.1×	124 2.8×	14 0.4×	38 1.0×	8	458
Nicolò Navarin Italy	15	325 2.1×	35 0.6×	13 0.3×	71 1.8×	60 1.6×	73	581
Stefano Bromuri Switzerland	14	250 1.6×	21 0.4×	12 0.3×	45 1.2×	52 1.4×	53	567
Cameron Foale Australia	10	165 1.1×	63 1.1×	16 0.4×	8 0.2×	17 0.4×	22	360

Countries citing papers authored by Sayash Kapoor

Since Specialization

Citations

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

Fields of papers citing papers by Sayash Kapoor

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sayash Kapoor

This figure shows the co-authorship network connecting the top 25 collaborators of Sayash Kapoor. A scholar is included among the top collaborators of Sayash Kapoor 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 Sayash Kapoor. Sayash Kapoor 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:

15 of 15 papers shown

1.

Narayanan, Arvind & Sayash Kapoor. (2025). Why an overreliance on AI-driven modelling is bad for science. Nature. 640(8058). 312–314. 7 indexed citations

2.

Agrawal, Anurag, et al.. (2025). The Reality of AI and Biorisk. 763–771. 1 indexed citations

3.

Kapoor, Sayash, Christopher A. Bail, Odd Erik Gundersen, et al.. (2024). REFORMS: Consensus-based Recommendations for Machine-learning-based Science. Science Advances. 10(18). eadk3452–eadk3452. 27 indexed citations

4.

Kapoor, Sayash, Peter Henderson, & Arvind Narayanan. (2024). Promises and Pitfalls of Artificial Intelligence for Legal Applications. SSRN Electronic Journal. 7 indexed citations

5.

Bommasani, Rishi, Shayne Longpre, Sayash Kapoor, et al.. (2024). Foundation Model Transparency Reports. Proceedings of the AAAI/ACM Conference on AI Ethics and Society. 7. 181–195. 9 indexed citations

6.

Bommasani, Rishi, Sayash Kapoor, Shayne Longpre, et al.. (2024). Considerations for governing open foundation models. Science. 386(6718). 151–153. 10 indexed citations

7.

Narayanan, Arvind & Sayash Kapoor. (2024). AI Snake Oil. Princeton University Press eBooks. 22 indexed citations

8.

Kapoor, Sayash, et al.. (2024). AI Snake Oil. Princeton University Press eBooks. 1 indexed citations

9.

Wang, Angelina, Sayash Kapoor, Solon Barocas, & Arvind Narayanan. (2023). Against Predictive Optimization: On the Legitimacy of Decision-making Algorithms That Optimize Predictive Accuracy. 1(1). 1–45. 24 indexed citations

10.

Wang, Angelina, Sayash Kapoor, Solon Barocas, & Arvind Narayanan. (2023). Against Predictive Optimization. 626–626. 8 indexed citations

11.

Kapoor, Sayash & Arvind Narayanan. (2023). Leakage and the reproducibility crisis in machine-learning-based science. Patterns. 4(9). 100804–100804. 260 indexed citations breakdown →

12.

Celis, L. Elisa, et al.. (2019). A dashboard for controlling polarization in personalization. AI Communications. 32(1). 77–89. 1 indexed citations

13.

Celis, L. Elisa, et al.. (2019). Controlling Polarization in Personalization. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 160–169. 41 indexed citations

14.

Kapoor, Sayash, Kumar Kshitij Patel, & Purushottam Kar. (2018). Corruption-tolerant bandit learning. Machine Learning. 108(4). 687–715. 15 indexed citations

15.

Kapoor, Sayash, Vijay Keswani, Nisheeth K. Vishnoi, & L. Elisa Celis. (2018). Balanced News Using Constrained Bandit-based Personalization. 5835–5837. 2 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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