🧑‍🔬 Cynthia D Rudin

🏛️ Affiliations

🎓Duke UniversityDurham · United States2011–2026 · 207 papersEmployment since 2016 · Computer Sciencecurrent🎓Massachusetts Institute of TechnologyCambridge · United States2009–2018 · 74 papers🔬MIT Computer Science and Artificial Intelligence LaboratoryCambridge · United States2013–2016 · 8 papers🎓Columbia UniversityNew York · United States2007–2012 · 8 papers🎓Princeton UniversityPrinceton · United States2003–2007 · 4 papersEducation until 2004🎓New York UniversityNew York · United States2004–2006 · 4 papers🎓Harvard UniversityCambridge · United States2016–2024 · 3 papers🎓Johns Hopkins UniversityBaltimore · United States2023 · 2 papers🏥Massachusetts General HospitalBoston · United States2022–2023 · 2 papers🎓University of WashingtonSeattle · United States2012–2013 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

2010s

2000s

📄 Papers

The most cited first (OpenAlex and our library).

1. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead (2018 · 10,460 citations)
   This perspective article argues against building black box predictors and then attaching a second, post-hoc explainer to them for decisions that deeply affect people, such as parole, bail, medical care and credit.
2. Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking (2008 · 217 citations)
   MADA picks, for each Arabic word, one analysis from the candidates that the BAMA dictionary proposes. It scores each candidate by summing the weights of the features on which the candidate agrees with the model's prediction. The features are 14 morphological tags, each predicted by an SVM, two checks (spelling match…
3. A Transformer Approach to Contextual Sarcasm Detection in Twitter (2020 · 38 citations)
   The paper tackles automatic sarcasm detection in tweets for the FigLang 2020 shared task, where each reply comes with its conversation context.
4. The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation (2023 · 2 citations)
   The paper generates Shakespearean sonnets with a small pretrained model (GPT-2, fine-tuned on poems and sonnets) and enforces the sonnet form explicitly rather than hoping training teaches it.

All works

330 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

1. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models InsteadNature Machine IntelligenceCynthia Rudin2018 · 10,460 citations
2. All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously ↗arXiv (Cornell University) · no summary yetAaron Fisher, Cynthia D Rudin, Francesca Dominici2018 · 1,155 citations
3. Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model ↗The Annals of Applied Statistics · no summary yetBenjamin Letham et al.2015 · 783 citations
4. This Looks Like That: Deep Learning for Interpretable Image Recognition ↗arXiv (Cornell University) · no summary yetChaofan Chen et al.2018 · 590 citations
5. Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetOscar Li et al.2018 · 547 citations
6. PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetSachit Menon et al.2020 · 540 citations
7. The Big Data Newsvendor: Practical Insights from Machine Learning ↗Operations Research · no summary yetGah‐Yi Ban, Cynthia D Rudin2018 · 508 citations
8. Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From An Explainable AI Competition ↗Harvard Data Science Review · no summary yetCynthia D Rudin, Joanna Radin2019 · 437 citations
9. Supersparse linear integer models for optimized medical scoring systems ↗Machine Learning · no summary yetBerk Ustun, Cynthia D Rudin2015 · 335 citations
10. Concept whitening for interpretable image recognition ↗Nature Machine Intelligence · no summary yetZhi Chen, Yijie Bei, Cynthia D Rudin2020 · 294 citations
11. Machine Learning for the New York City Power Grid ↗IEEE Transactions on Pattern Analysis and Machine Intelligence · no summary yetCynthia D Rudin et al.2011 · 263 citations
12. Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization ↗arXiv (Cornell University) · no summary yetYingfan Wang et al.2020 · 252 citations
13. Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature RankingRyan Roth, Owen Rambow, Nizar Habash, et al.Semitic languages · 217 citations
14. A Shared Vision for Machine Learning in Neuroscience ↗Journal of Neuroscience · no summary yetMai-Anh T. Vu et al.2018 · 190 citations
15. The Age of Secrecy and Unfairness in Recidivism Prediction ↗Harvard Data Science Review · no summary yetCynthia D Rudin, Caroline Wang, Beau Coker2018 · 183 citations
16. Applied machine learning as a driver for polymeric biomaterials design ↗Nature Communications · no summary yetSamantha M. McDonald et al.2023 · 167 citations
17. Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test ↗Machine Learning · no summary yetWilliam Souillard‐Mandar et al.2015 · 166 citations
18. A case-based interpretable deep learning model for classification of mass lesions in digital mammography ↗Nature Machine Intelligence · no summary yetAlina Jade Barnett et al.2021 · 164 citations
19. Interpretable Classification Models for Recidivism Prediction ↗Journal of the Royal Statistical Society Series A (Statistics in Society) · no summary yetJiaming Zeng, Berk Ustun, Cynthia D Rudin2016 · 162 citations
20. Towards a comprehensive evaluation of dimension reduction methods for transcriptomic data visualization ↗Communications Biology · no summary yetHaiyang Huang et al.2022 · 148 citations
21. Interpretable Image Recognition with Hierarchical Prototypes ↗Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · no summary yetPeter Hase et al.2019 · 131 citations
22. The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification ↗arXiv (Cornell University) · no summary yetBeen Kim, Cynthia D Rudin, Julie Shah2014 · 131 citations
23. Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice ↗INFORMS Journal on Applied Analytics · no summary yetCynthia D Rudin, Berk Ustun2018 · 122 citations
24. Learning to Detect Patterns of Crime ↗Lecture notes in computer science · no summary yetTong Wang et al.2013 · 120 citations
25. Learning Certifiably Optimal Rule Lists for Categorical Data ↗arXiv (Cornell University) · no summary yetElaine Lee Angelino et al.2017 · 118 citations

See all 330 works by Cynthia D Rudin →

🤝 Co-authors here

5 co-authors with a page, the most papers together first.

• Mona Diab
• Nizar Habash
• Owen Rambow
• Ryan Roth
• Sam Wiseman

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• SSRN Electronic Journal ×15
• neural information processing systems ×13
• Machine Learning ×9
• PubMed ×7
• Lecture notes in computer science ×6
• Proceedings of the AAAI Conference on Artificial Intelligence ×6
• Nature Machine Intelligence ×5: Nature Machine Intelligence 2018
• ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) ×5
• Harvard Data Science Review ×5
• The Annals of Applied Statistics ×4
• Big Data ×3
• SPIE Medical Imaging: Image Perception, Observer Performance, and Technology Assessment ×3

🔗 Cited by

Papers of our library cite their works 1,051 times (of the 149,439 papers whose bibliographies we read); 92 citing papers with a page here →.

Updated 30 October 2026

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