Berk Ustun

2.4k citations
27 papers · 959 · h-index 12

Impact in

Papers in

    • Machine Learning in Healthcare 5
    • Explainable Artificial Intelligence (XAI) 5
    • Adversarial Robustness in Machine Learning 3
    • Machine Learning and Data Classification 3
    • Bayesian Modeling and Causal Inference 2
    • Statistical Methods and Inference 4
    • Advanced Causal Inference Techniques 3

Berk Ustun

22 papers receiving 928 citations

Peers

Berk Ustun
Comparison fields: 5 of 113
  • Health Informatics 48
  • Psychiatry and Mental health 202
  • Artificial Intelligence 268
  • Safety Research 63
  • Clinical Psychology 136
Replace Kenneth Gersing with:
Kenneth Gersing United States
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Sumithra Velupillai Sweden
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Citations per field
00.5×4.8×
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Citations per year

Countries citing papers authored by Berk Ustun

Since Specialization
Citations

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

Fields of papers citing papers by Berk Ustun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Berk Ustun, 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 Berk Ustun Line = papers co-authored together Berk Ustun links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017243
2 2015168
3 2017121
4 2019106
5 201867
6 201662
7
Learning Optimized Risk Scores
201933
8 201528
9
Fairness without Harm: Decoupled Classifiers with Preference Guarantees
201923
10 201723
11 202313
12 202313
13 202411
14 201911
15 20229
16 20228
17 20137
18 20245
19 20183
20 20212

About Berk Ustun

Berk Ustun is a scholar working on Artificial Intelligence, Statistics and Probability, Management Science and Operations Research, Safety Research and Cardiology and Cardiovascular Medicine, having authored 27 papers that have together received 959 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (5 papers), Explainable Artificial Intelligence (XAI) (5 papers), Statistical Methods and Inference (4 papers), Advanced Causal Inference Techniques (3 papers), Adversarial Robustness in Machine Learning (3 papers), Ethics and Social Impacts of AI (3 papers), Machine Learning and Data Classification (3 papers) and Bayesian Modeling and Causal Inference (2 papers). The work is most often cited by research in Health Informatics (48 citations), Psychiatry and Mental health (202 citations), Artificial Intelligence (268 citations), Safety Research (63 citations) and Clinical Psychology (136 citations). Berk Ustun has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Cynthia Rudin, Ronald C. Kessler, Patricia A. Berglund, Thomas Spencer, Michael J. Gruber, Stephen V. Faraone, Lenard A. Adler, M. Brandon Westover, Matt T. Bianchi and Murray B. Stein. Their work appears in journals such as Depression and Anxiety, ACM Transactions on Knowledge Discovery from Data, Journal of Anxiety Disorders, Journal of the American Heart Association and Journal of Machine Learning Research.

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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