2026 Cynthia Rudin: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	57	3736	3631	1785	1721	220	23063

Cynthia Rudin publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Cynthia Rudin sits on this spectrum.

32 publications 991+

This scientist: 220 publications — 53rd percentile

53% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Cynthia Rudin D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Cynthia Rudin sits on this spectrum.

30 D-Index 131+

This scientist: 57 D-Index — 74th percentile

74% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

2019 - Fellow of the American Statistical Association (ASA)

Overview

Cynthia Rudin is affiliated with Duke University in the United States. Their research predominantly spans the field of Computer Science, with a focus on several subfields including Artificial Intelligence, Statistics and Probability, Computer Vision and Pattern Recognition, Biomedical Engineering, and Cardiology and Cardiovascular Medicine.

The main topics of their work include:

Explainable Artificial Intelligence (XAI)
Machine Learning in Healthcare
Statistical Methods and Inference
Machine Learning and Data Classification
Advanced Causal Inference Techniques
HIV Research and Treatment
Bayesian Modeling and Causal Inference

The scientist has contributed to several recent publications, notable among them are:

"Interpretable machine learning: Fundamental principles and 10 grand challenges," 2022, published in Statistics Surveys
"Concept whitening for interpretable image recognition," 2020, published in Nature Machine Intelligence
"Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization," 2020, published in arXiv (Cornell University)
"A case-based interpretable deep learning model for classification of mass lesions in digital mammography," 2021, published in Nature Machine Intelligence
"Towards a comprehensive evaluation of dimension reduction methods for transcriptomic data visualization," 2022, published in Communications Biology

Frequent co-authors in their research include:

Alexander Volfovsky
Lesia Semenova
Zhicheng Guo
Margo Seltzer
Marco Morucci

Cynthia Rudin's work appears regularly in several publication venues, including:

arXiv (Cornell University)
SSRN Electronic Journal
Harvard Data Science Review
PubMed
Nature Machine Intelligence

In 2019, they were recognized as a Fellow of the American Statistical Association (ASA).

Best Publications

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Cynthia Rudin

7808
Citations
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Aaron Fisher;Cynthia Rudin;Francesca Dominici

1373
Citations
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

Benjamin Letham;Cynthia Rudin;Tyler H. McCormick;David Madigan

877
Citations
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

Cynthia Rudin;Chaofan Chen;Zhi Chen;Haiyang Huang

760
Citations
The Big Data Newsvendor: Practical Insights from Machine Learning

Gah-Yi Ban;Cynthia Rudin

572
Citations
This Looks Like That: Deep Learning for Interpretable Image Recognition

Chaofan Chen;Oscar Li;Chaofan Tao;Alina Jade Barnett

510
Citations
Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions

Oscar Li;Hao Liu;Chaofan Chen;Cynthia Rudin

502
Citations
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

Sachit Menon;Alexandru Damian;Shijia Hu;Nikhil Ravi

491
Citations
Supersparse linear integer models for optimized medical scoring systems

Berk Ustun;Cynthia Rudin

384
Citations
Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From An Explainable AI Competition

Cynthia Rudin;Joanna Radin

374
Citations
Interpretable classification models for recidivism prediction

Jiaming Zeng;Berk Ustun;Cynthia Rudin

344
Citations
Machine Learning for the New York City Power Grid

C. Rudin;D. Waltz;R. N. Anderson;A. Boulanger

329
Citations
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification

Been Kim;Cynthia Rudin;Julie A Shah

293
Citations
This Looks Like That: Deep Learning for Interpretable Image Recognition

Chaofan Chen;Oscar Li;Daniel Tao;Alina Barnett

279
Citations
Learning Certifiably Optimal Rule Lists for Categorical Data

Elaine Angelino;Nicholas Larus-Stone;Daniel Alabi;Margo I. Seltzer

264
Citations
Concept whitening for interpretable image recognition

Zhi Chen;Yijie Bei;Cynthia Rudin

260
Citations
Falling Rule Lists

Fulton Wang;Cynthia Rudin

235
Citations
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization

Yingfan Wang;Haiyang Huang;Cynthia Rudin;Yaron Shaposhnik

234
Citations
A Bayesian framework for learning rule sets for interpretable classification

Tong Wang;Cynthia Rudin;Finale Doshi-Velez;Yimin Liu

231
Citations
The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List

Cynthia Rudin

213
Citations
Scalable Bayesian rule lists

Hongyu Yang;Cynthia Rudin;Margo Seltzer

158
Citations
The Big Data Newsvendor: Practical Insights from Machine Learning Analysis

Cynthia Rudin;Gah-Yi Vahn

8
Citations

Frequent Co-Authors

David Madigan Northeastern University

Rebecca J. Passonneau Pennsylvania State University

Robert E. Schapire Microsoft (United States)

Margo Seltzer University of British Columbia

Roger N. Anderson Columbia University

Tong Wang University of Tennessee at Knoxville

Ingrid Daubechies Duke University

Been Kim Google (United States)

Gail E. Kaiser Columbia University

Haym Hirsh Cornell University

External Links

Wikipedia of Cynthia Rudin Personal website of Cynthia Rudin Google Scholar page

If you think any of the details on this page are incorrect, let us know.

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