2019 - Fellow of the American Statistical Association (ASA)
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.
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.
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.
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.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin.
Nature Machine Intelligence (2019)
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)
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)
Falling Rule Lists
Fulton Wang;Cynthia Rudin.
international conference on artificial intelligence and statistics (2015)
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)
Supersparse linear integer models for optimized medical scoring systems
Berk Ustun;Cynthia Rudin.
Machine Learning (2016)
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)
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)
This Looks Like That: Deep Learning for Interpretable Image Recognition
Chaofan Chen;Oscar Li;Daniel Tao;Alina Barnett.
neural information processing systems (2019)
Interpretable classification models for recidivism prediction
Jiaming Zeng;Berk Ustun;Cynthia Rudin.
Journal of The Royal Statistical Society Series A-statistics in Society (2017)
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.
If you think any of the details on this page are incorrect, let us know.
Northeastern University
Pennsylvania State University
Microsoft (United States)
University of British Columbia
South Dakota State University
Columbia University
Duke University
Google (United States)
Cornell University
Columbia University
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: