Dana Pessach

908 total citations · 1 hit paper
7 papers, 509 citations indexed

About

Dana Pessach is a scholar working on Artificial Intelligence, Organizational Behavior and Human Resource Management and Safety Research. According to data from OpenAlex, Dana Pessach has authored 7 papers receiving a total of 509 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Organizational Behavior and Human Resource Management and 2 papers in Safety Research. Recurrent topics in Dana Pessach's work include Privacy-Preserving Technologies in Data (3 papers), AI and HR Technologies (3 papers) and Employer Branding and e-HRM (2 papers). Dana Pessach is often cited by papers focused on Privacy-Preserving Technologies in Data (3 papers), AI and HR Technologies (3 papers) and Employer Branding and e-HRM (2 papers). Dana Pessach collaborates with scholars based in Israel, Chile and United States. Dana Pessach's co-authors include Erez Shmueli, Gonen Singer, Hila Chalutz Ben‐Gal, Irad Ben‐Gal, Tamir Tassa and Bárbara Poblete and has published in prestigious journals such as Expert Systems with Applications, ACM Computing Surveys and Decision Support Systems.

In The Last Decade

Dana Pessach

6 papers receiving 483 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.

A Review on Fairness in Machine Learning

2022 277 citations Dana Pessach, Erez Shmueli ACM Computing Surveys profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dana Pessach Israel	4	219	161	128	71	50	7	509
Edward McFowland United States	6	153 0.7×	73 0.5×	15 0.1×	56 0.8×	37 0.7×	16	432
Weiyu Wang China	5	72 0.3×	48 0.3×	19 0.1×	33 0.5×	44 0.9×	7	325
Mike Horia Teodorescu United States	8	68 0.3×	105 0.7×	19 0.1×	20 0.3×	46 0.9×	34	287
Jules Polonetsky United States	12	183 0.8×	92 0.6×	14 0.1×	18 0.3×	97 1.9×	19	609
Nishith Reddy Mannuru United States	7	376 1.7×	141 0.9×	15 0.1×	359 5.1×	95 1.9×	23	863
A.S.Hovan George India	7	182 0.8×	51 0.3×	8 0.1×	172 2.4×	51 1.0×	29	417
Josephina Antoniou Cyprus	11	77 0.4×	109 0.7×	7 0.1×	60 0.8×	35 0.7×	40	402
Visara Urovi Netherlands	9	163 0.7×	26 0.2×	19 0.1×	35 0.5×	168 3.4×	26	380
Sahil Verma India	5	364 1.7×	366 2.3×	6 0.0×	80 1.1×	72 1.4×	5	636
Axel Hoffmann Germany	9	100 0.5×	28 0.2×	38 0.3×	9 0.1×	130 2.6×	36	373

Countries citing papers authored by Dana Pessach

Since Specialization

Citations

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

Fields of papers citing papers by Dana Pessach

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dana Pessach

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

7 of 7 papers shown

1.

Pessach, Dana & Bárbara Poblete. (2024). Gender Representation Across Online Retail Products. 947–957.

2.

Pessach, Dana, Tamir Tassa, & Erez Shmueli. (2024). Fairness-Driven Private Collaborative Machine Learning. ACM Transactions on Intelligent Systems and Technology. 15(2). 1–30. 2 indexed citations

3.

Pessach, Dana & Erez Shmueli. (2022). A Review on Fairness in Machine Learning. ACM Computing Surveys. 55(3). 1–44. 277 indexed citations breakdown →

4.

Pessach, Dana, et al.. (2022). A human resources analytics and machine-learning examination of turnover: implications for theory and practice. International Journal of Manpower. 43(6). 1405–1424. 29 indexed citations

5.

Pessach, Dana & Erez Shmueli. (2021). Improving fairness of artificial intelligence algorithms in Privileged-Group Selection Bias data settings. Expert Systems with Applications. 185. 115667–115667. 33 indexed citations

6.

Ben‐Gal, Hila Chalutz, et al.. (2021). A Human Resources Analytics Examination of Turnover: Implications for Theory and Practice. Academy of Management Proceedings. 2021(1). 12152–12152. 2 indexed citations

7.

Pessach, Dana, et al.. (2020). Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems. 134. 113290–113290. 166 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.

Explore authors with similar magnitude of impact