H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 45 Citations 9,033 145 World Ranking 3561 National Ranking 1832

Research.com Recognitions

Awards & Achievements

2019 - Fellow of the American Statistical Association (ASA)

Overview

What is she best known for?

The fields of study she is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Her scientific interests lie mostly in Artificial intelligence, Machine learning, Interpretability, Probabilistic logic and Black box. Her Artificial intelligence course of study focuses on Pattern recognition and Boosting. Her work deals with themes such as Bayesian framework and Falling, which intersect with Machine learning.

Her Interpretability study incorporates themes from Network architecture, Decision list, Deep learning, Bayesian probability and Case-based reasoning. Cynthia Rudin combines subjects such as Association rule learning, Covariate, Linear model and Parametric statistics with her study of Probabilistic logic. Her research investigates the connection with Association rule learning and areas like Theoretical computer science which intersect with concerns in Ranking.

Her most cited work include:

  • Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead (811 citations)
  • Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model (350 citations)
  • Machine Learning for the New York City Power Grid (161 citations)

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

Cynthia Rudin focuses on Artificial intelligence, Machine learning, Interpretability, Data mining and Mathematical optimization. The concepts of her Artificial intelligence study are interwoven with issues in Natural language processing, Generalization and Pattern recognition. Many of her studies on Machine learning apply to Bayesian probability as well.

The concepts of her Interpretability study are interwoven with issues in Black box, Deep learning and Linear model. Her study focuses on the intersection of Data mining and fields such as Categorical variable with connections in the field of Covariate, Matching and Causal inference. Her Mathematical optimization research focuses on Decision tree and how it connects with Scalability.

She most often published in these fields:

  • Artificial intelligence (52.57%)
  • Machine learning (37.94%)
  • Interpretability (16.60%)

What were the highlights of her more recent work (between 2018-2021)?

  • Artificial intelligence (52.57%)
  • Machine learning (37.94%)
  • Interpretability (16.60%)

In recent papers she was focusing on the following fields of study:

Cynthia Rudin mainly focuses on Artificial intelligence, Machine learning, Interpretability, Matching and Causal inference. Her work deals with themes such as Pattern recognition and Natural language processing, which intersect with Artificial intelligence. The Dimensionality reduction research Cynthia Rudin does as part of her general Machine learning study is frequently linked to other disciplines of science, such as Focus, therefore creating a link between diverse domains of science.

Her work carried out in the field of Interpretability brings together such families of science as Competition, Statistical learning theory, Landmark, Generalization and Risk assessment. Her Matching research integrates issues from Algorithm, Covariate and Inference. She combines subjects such as Optimization problem, Robustness and Process with her study of Causal inference.

Between 2018 and 2021, her most popular works were:

  • Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead (811 citations)
  • This Looks Like That: Deep Learning for Interpretable Image Recognition (145 citations)
  • All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously (125 citations)

In her most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

Her primary areas of study are Artificial intelligence, Machine learning, Interpretability, Mathematical optimization and Space. Cynthia Rudin focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Pattern recognition and, in certain cases, Upsampling. Her Machine learning study incorporates themes from Contextual image classification and Causal inference.

Interpretability is closely attributed to Data mining in her work. The various areas that Cynthia Rudin examines in her Mathematical optimization study include Decision tree, Optimal decision and Scalability. Cynthia Rudin has researched Black box in several fields, including Competition, Landmark and Key.

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.

Top Publications

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

Cynthia Rudin.
Nature Machine Intelligence (2019)

811 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)

478 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)

235 Citations

Falling Rule Lists

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

198 Citations

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

Cynthia Rudin.
Journal of Machine Learning Research (2009)

197 Citations

Supersparse linear integer models for optimized medical scoring systems

Berk Ustun;Cynthia Rudin.
Machine Learning (2016)

194 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)

190 Citations

Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking

Ryan Roth;Owen Rambow;Nizar Habash;Mona Diab.
meeting of the association for computational linguistics (2008)

185 Citations

This Looks Like That: Deep Learning for Interpretable Image Recognition

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

170 Citations

Interpretable classification models for recidivism prediction

Jiaming Zeng;Berk Ustun;Cynthia Rudin.
Journal of The Royal Statistical Society Series A-statistics in Society (2017)

162 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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