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.
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