D-Index & Metrics Best Publications

Cynthia Rudin

Duke University
United States

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name Citations Publications World Ranking National Ranking

Computer Science D-index 50 Citations 10,414 196 World Ranking 3673 National Ranking 1874

Research.com Recognitions

Awards & Achievements

2019 - Fellow of the American Statistical Association (ASA)

Overview

What is she best known for?

The fields of study Cynthia Rudin is best known for:

Statistics
Machine learning
Artificial intelligence

Cynthia Rudin links relevant scientific disciplines such as Key (lock) and Network architecture in the realm of Computer security. Her research ties Computer security and Key (lock) together. Quantum mechanics is intertwined with Scale (ratio) and Term (time) in her research. Her Scale (ratio) study frequently draws connections to other fields, such as Quantum mechanics. She connects relevant research areas such as Process (computing) and Encoder in the domain of Operating system. In her works, Cynthia Rudin undertakes multidisciplinary study on Process (computing) and Operating system. Her Machine learning study frequently links to adjacent areas such as Interpretability. Her research on Interpretability often connects related areas such as Artificial intelligence. She incorporates Artificial intelligence and Algorithm in her studies.

Her most cited work include:

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead (2589 citations)
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model (330 citations)
The Big Data Newsvendor: Practical Insights from Machine Learning (232 citations)

What are the main themes of her work throughout her whole career to date

Many of her studies on Artificial intelligence apply to Interpretability, Pattern recognition (psychology) and Artificial neural network as well. Cynthia Rudin integrates Machine learning with Data mining in her study. She performs multidisciplinary study in Data mining and Machine learning in her work. She combines topics linked to Set (abstract data type) with her work on Programming language. Her work on Set (abstract data type) is being expanded to include thematically relevant topics such as Programming language. She combines Algorithm and Artificial intelligence in her research.

Cynthia Rudin most often published in these fields:

Artificial intelligence (81.52%)
Machine learning (66.30%)
Programming language (33.70%)

What were the highlights of her more recent work (between 2020-2022)?

Artificial intelligence (87.50%)
Machine learning (87.50%)
Programming language (50.00%)

In recent works Cynthia Rudin was focusing on the following fields of study:

Her research investigates the connection between Troubleshooting and topics such as Operating system that intersect with issues in Process (computing). In her works, she undertakes multidisciplinary study on Process (computing) and Operating system. She integrates several fields in her works, including Programming language, Data structure and Preprocessor. Her Computer vision study has been linked to subjects such as Artifact (error) and Segmentation. Her work on Computer vision expands to the thematically related Segmentation. She performs integrative Artificial intelligence and Data science research in her work. Cynthia Rudin conducted interdisciplinary study in her works that combined Data science and Data visualization. Her Machine learning study frequently involves adjacent topics like Margin (machine learning). She undertakes interdisciplinary study in the fields of Artificial neural network and Reinforcement learning through her works.

Between 2020 and 2022, her most popular works were:

Interpretable machine learning: Fundamental principles and 10 grand challenges (112 citations)
A case-based interpretable deep learning model for classification of mass lesions in digital mammography (24 citations)
Interpretable, not black-box, artificial intelligence should be used for embryo selection (13 citations)

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

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

Cynthia Rudin.
Nature Machine Intelligence (2019)

2393 Citations

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

Benjamin Letham;Cynthia Rudin;Tyler H. McCormick;David Madigan.
The Annals of Applied Statistics (2015)

626 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.
Journal of Machine Learning Research (2019)

334 Citations

Supersparse linear integer models for optimized medical scoring systems

Berk Ustun;Cynthia Rudin.
Machine Learning (2016)

288 Citations

Machine Learning for the New York City Power Grid

C. Rudin;D. Waltz;R. N. Anderson;A. Boulanger.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)

273 Citations

This Looks Like That: Deep Learning for Interpretable Image Recognition

Chaofan Chen;Oscar Li;Daniel Tao;Alina Barnett.
neural information processing systems (2019)

269 Citations

The Big Data Newsvendor: Practical Insights from Machine Learning

Gah-Yi Ban;Cynthia Rudin.
Operations Research (2019)

261 Citations

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

Been Kim;Cynthia Rudin;Julie A Shah.
neural information processing systems (2014)

238 Citations

Falling Rule Lists

Fulton Wang;Cynthia Rudin.
international conference on artificial intelligence and statistics (2015)

233 Citations

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

Oscar Li;Hao Liu;Chaofan Chen;Cynthia Rudin.
national conference on artificial intelligence (2018)

220 Citations

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