---
title: "Cynthia D Rudin · Duke University — author profile and papers"
description: "Cynthia D Rudin, Duke University: 4 papers on Elamur Research (2008–2023), h-index 54 — affiliations, topics, co-authors and who cites…"
canonical: "https://research.elamur.ai/free/en/authors/cynthia-d-rudin/"
---
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# Cynthia D Rudin

Cynthia D Rudin has 4 papers in our library, in 2008–2023, most of them on [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/), [Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/), [Morphology](https://md.research.elamur.ai/free/en/topics/morphology/); often with [Mona Diab](https://md.research.elamur.ai/free/en/authors/mona-diab/), [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/), [Owen Rambow](https://md.research.elamur.ai/free/en/authors/owen-rambow/). 2 of them are among the papers read by [Mova, the project](https://md.research.elamur.ai/free/en/projects/mova/).

4 papers in our library · 2008–2023

[OpenAlex](https://openalex.org/A5040468715) · [ORCID](https://orcid.org/0000-0003-4283-2780) · [Wikidata](https://www.wikidata.org/wiki/Q59662692) · [**h-index 54**](https://openalex.org/authors/A5040468715) · [344 works](https://openalex.org/works?filter=author.id:A5040468715) · [24,687 citations](https://openalex.org/works?filter=author.id:A5040468715&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 4 works in our library](https://md.research.elamur.ai/free/en/authors/cynthia-d-rudin/#papers) · [🔗 Cited 1,051 times in our library](https://md.research.elamur.ai/free/en/authors/cynthia-d-rudin/cited-by/) · [326 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #303](https://md.research.elamur.ai/free/en/paper-stars/page/4/#star-303)

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Cynthia+D+Rudin) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Cynthia+D+Rudin) · [dblp](https://dblp.org/search?q=Cynthia+D+Rudin) · [ACL Anthology](https://aclanthology.org/search/?q=Cynthia+D+Rudin)

## Affiliations

[🎓**Duke University** Durham · United States 2011–2026 · 207 papersEmployment since 2016 · Computer Science*current*](https://md.research.elamur.ai/free/en/universities/duke-university/) · [🎓**Massachusetts Institute of Technology** Cambridge · United States 2009–2018 · 74 papers](https://md.research.elamur.ai/free/en/universities/massachusetts-institute-of-technology/) · [🔬**MIT Computer Science and Artificial Intelligence Laboratory** Cambridge · United States 2013–2016 · 8 papers](https://md.research.elamur.ai/free/en/labs/mit-computer-science-and-artificial-intelligence-laboratory/) · [🎓**Columbia University** New York · United States 2007–2012 · 8 papers](https://md.research.elamur.ai/free/en/universities/columbia-university/) · [🎓**Princeton University** Princeton · United States 2003–2007 · 4 papersEducation until 2004](https://md.research.elamur.ai/free/en/universities/princeton-university/) · [🎓**New York University** New York · United States 2004–2006 · 4 papers](https://md.research.elamur.ai/free/en/universities/new-york-university/) · [🎓**Harvard University** Cambridge · United States 2016–2024 · 3 papers](https://md.research.elamur.ai/free/en/universities/harvard-university/) · [🎓**Johns Hopkins University** Baltimore · United States 2023 · 2 papers](https://md.research.elamur.ai/free/en/universities/johns-hopkins-university/) · [🏥**Massachusetts General Hospital** Boston · United States 2022–2023 · 2 papers](https://md.research.elamur.ai/free/en/hospitals/massachusetts-general-hospital/) · [🎓**University of Washington** Seattle · United States 2012–2013 · 2 papers](https://md.research.elamur.ai/free/en/universities/university-of-washington/)

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.

📚 Shelves 8 The field of science this work belongs to (OpenAlex fields).[Artificial Intelligence · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computer Science · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Engineering · 1 work](https://md.research.elamur.ai/free/en/shelves/engineering/) · [Safety Research · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/) · [Safety, Risk, Reliability and Quality · 1 work](https://md.research.elamur.ai/free/en/shelves/engineering/safety-risk-reliability-and-quality/) · [Social Sciences · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · [Arts and Humanities · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/)

🧭 Topics 4 Research subjects and languages the work is about.[Morphology · 1 work](https://md.research.elamur.ai/free/en/topics/morphology/) · [Semitic languages · 1 work](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · [Sentiment & emotion · 1 work](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · [Machine translation · 1 work](https://md.research.elamur.ai/free/en/topics/machine-translation/)

💡 Concepts 4 Key ideas and methods explained on our concept pages.[Machine learning · 1 work](https://md.research.elamur.ai/free/en/concepts/machine-learning/) · [Lemma · 1 work](https://md.research.elamur.ai/free/en/concepts/lemma/) · [Lemmatization · 1 work](https://md.research.elamur.ai/free/en/concepts/lemmatization/) · [Morphology · 1 work](https://md.research.elamur.ai/free/en/concepts/morphology/)

## Works by year

### 2020s

[**2023** 1 paper](https://md.research.elamur.ai/free/en/authors/cynthia-d-rudin/year/2023/) · [**2020** 1 paper](https://md.research.elamur.ai/free/en/authors/cynthia-d-rudin/year/2020/)

### 2010s

[**2018** 1 paper](https://md.research.elamur.ai/free/en/authors/cynthia-d-rudin/year/2018/)

### 2000s

[**2008** 1 paper](https://md.research.elamur.ai/free/en/authors/cynthia-d-rudin/year/2008/)

## 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](https://md.research.elamur.ai/free/en/papers/rudin-2018-stop-explaining-black-box/)** *(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](https://md.research.elamur.ai/free/en/papers/roth-2008-arabic-morphological-tagging-diacritization-lemmatization/)** *(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](https://md.research.elamur.ai/free/en/papers/gregory-2020-transformer-approach-contextual-sarcasm-detection/)** *(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](https://md.research.elamur.ai/free/en/papers/agnew-2023-mechanical-bard-interpretable-machine-learning/)** *(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 Instead](https://md.research.elamur.ai/free/en/papers/rudin-2018-stop-explaining-black-box/) [Nature Machine Intelligence](https://md.research.elamur.ai/free/en/venues/nature-machine-intelligence/)**Cynthia Rudin*2018 · 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 ↗](https://arxiv.org/abs/1801.01489) arXiv (Cornell University) · no summary yet**Aaron Fisher, Cynthia D Rudin, Francesca Dominici*2018 · 1,155 citations*
3. **[Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model ↗](https://doi.org/10.1214/15-aoas848) The Annals of Applied Statistics · no summary yet**Benjamin Letham et al.*2015 · 783 citations*
4. **[This Looks Like That: Deep Learning for Interpretable Image Recognition ↗](https://arxiv.org/abs/1806.10574) arXiv (Cornell University) · no summary yet**Chaofan Chen et al.*2018 · 590 citations*
5. **[Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions ↗](https://doi.org/10.1609/aaai.v32i1.11771) Proceedings of the AAAI Conference on Artificial Intelligence · no summary yet**Oscar Li et al.*2018 · 547 citations*
6. **[PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models ↗](https://doi.org/10.1109/cvpr42600.2020.00251) IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yet**Sachit Menon et al.*2020 · 540 citations*
7. **[The Big Data Newsvendor: Practical Insights from Machine Learning ↗](https://doi.org/10.1287/opre.2018.1757) Operations Research · no summary yet**Gah‐Yi Ban, Cynthia D Rudin*2018 · 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 ↗](https://doi.org/10.1162/99608f92.5a8a3a3d) Harvard Data Science Review · no summary yet**Cynthia D Rudin, Joanna Radin*2019 · 437 citations*
9. **[Supersparse linear integer models for optimized medical scoring systems ↗](https://doi.org/10.1007/s10994-015-5528-6) Machine Learning · no summary yet**Berk Ustun, Cynthia D Rudin*2015 · 335 citations*
10. **[Concept whitening for interpretable image recognition ↗](https://doi.org/10.1038/s42256-020-00265-z) Nature Machine Intelligence · no summary yet**Zhi Chen, Yijie Bei, Cynthia D Rudin*2020 · 294 citations*
11. **[Machine Learning for the New York City Power Grid ↗](https://doi.org/10.1109/tpami.2011.108) IEEE Transactions on Pattern Analysis and Machine Intelligence · no summary yet**Cynthia 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 ↗](https://arxiv.org/abs/2012.04456) arXiv (Cornell University) · no summary yet**Yingfan Wang et al.*2020 · 252 citations*
13. [**Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking**Ryan Roth, Owen Rambow, Nizar Habash, et al.*Semitic languages · 217 citations*](https://md.research.elamur.ai/free/en/papers/roth-2008-arabic-morphological-tagging-diacritization-lemmatization/)
14. **[A Shared Vision for Machine Learning in Neuroscience ↗](https://doi.org/10.1523/jneurosci.0508-17.2018) Journal of Neuroscience · no summary yet**Mai-Anh T. Vu et al.*2018 · 190 citations*
15. **[The Age of Secrecy and Unfairness in Recidivism Prediction ↗](https://doi.org/10.1162/99608f92.6ed64b30) Harvard Data Science Review · no summary yet**Cynthia D Rudin, Caroline Wang, Beau Coker*2018 · 183 citations*
16. **[Applied machine learning as a driver for polymeric biomaterials design ↗](https://doi.org/10.1038/s41467-023-40459-8) Nature Communications · no summary yet**Samantha M. McDonald et al.*2023 · 167 citations*
17. **[Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test ↗](https://doi.org/10.1007/s10994-015-5529-5) Machine Learning · no summary yet**William Souillard‐Mandar et al.*2015 · 166 citations*
18. **[A case-based interpretable deep learning model for classification of mass lesions in digital mammography ↗](https://doi.org/10.1038/s42256-021-00423-x) Nature Machine Intelligence · no summary yet**Alina Jade Barnett et al.*2021 · 164 citations*
19. **[Interpretable Classification Models for Recidivism Prediction ↗](https://doi.org/10.1111/rssa.12227) Journal of the Royal Statistical Society Series A (Statistics in Society) · no summary yet**Jiaming Zeng, Berk Ustun, Cynthia D Rudin*2016 · 162 citations*
20. **[Towards a comprehensive evaluation of dimension reduction methods for transcriptomic data visualization ↗](https://doi.org/10.1038/s42003-022-03628-x) Communications Biology · no summary yet**Haiyang Huang et al.*2022 · 148 citations*
21. **[Interpretable Image Recognition with Hierarchical Prototypes ↗](https://doi.org/10.1609/hcomp.v7i1.5265) Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · no summary yet**Peter Hase et al.*2019 · 131 citations*
22. **[The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification ↗](http://hdl.handle.net/1721.1/91918) arXiv (Cornell University) · no summary yet**Been Kim, Cynthia D Rudin, Julie Shah*2014 · 131 citations*
23. **[Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice ↗](https://doi.org/10.1287/inte.2018.0957) INFORMS Journal on Applied Analytics · no summary yet**Cynthia D Rudin, Berk Ustun*2018 · 122 citations*
24. **[Learning to Detect Patterns of Crime ↗](https://doi.org/10.1007/978-3-642-40994-3_33) Lecture notes in computer science · no summary yet**Tong Wang et al.*2013 · 120 citations*
25. **[Learning Certifiably Optimal Rule Lists for Categorical Data ↗](https://arxiv.org/abs/1704.01701) arXiv (Cornell University) · no summary yet**Elaine Lee Angelino et al.*2017 · 118 citations*

[See all 330 works by Cynthia D Rudin →](https://md.research.elamur.ai/free/en/authors/cynthia-d-rudin/works/)

## Co-authors here

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

- [Mona Diab](https://md.research.elamur.ai/free/en/authors/mona-diab/)
- [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/)
- [Owen Rambow](https://md.research.elamur.ai/free/en/authors/owen-rambow/)
- [Ryan Roth](https://md.research.elamur.ai/free/en/authors/ryan-roth/)
- [Sam Wiseman](https://md.research.elamur.ai/free/en/authors/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](https://md.research.elamur.ai/free/en/venues/nature-machine-intelligence/) ×5: [Nature Machine Intelligence 2018](https://md.research.elamur.ai/free/en/venues/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 →](https://md.research.elamur.ai/free/en/authors/cynthia-d-rudin/cited-by/).

Updated 30 October 2026