Cynthia Rudin

Duke University

United States

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Articles (18)

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2025

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2025

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2024

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2022

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2021

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2021

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2020

Collaborators (10)

Ran Xiao

Assistant Professor

Emory University

UNITED STATES

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Lynda Brinson

Professor

Duke University

UNITED STATES

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Alina Kirillova

Assistant Professor

Iowa State University

UNITED STATES

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Chaofan Chen

Assistant Professor in Computer Science

University of Maine

UNITED STATES

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Md. Noor-E-Alam

Northeastern University

UNITED STATES

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Duc Do

University of California, Los Angeles

UNITED STATES

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Lesia Semenova

Assistant Professor

Rutgers University

UNITED STATES

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Chiara Daraio

Professor

California Institute of Technology

UNITED STATES

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David Margolis

Sarah Graham Kenan Distinguished Professor, and Director UNC HIV Cure Center

University of North Carolina at Chapel Hill

UNITED STATES

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Jacob Peloquin

Co-Founder and Chief Technology Officer

UNITED STATES

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Cynthia Rudin

Research Interests

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Articles (18)

Graph-based design of irregular metamaterials

A multiscale design method using interpretable machine learning for phononic materials with closely interacting scales

Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation

Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms

Sparse Density Trees and Lists: An Interpretable Alternative to High-Dimensional Histograms

Sparse learned kernels for interpretable and efficient medical time series processing

AsymMirai: Interpretable Mammography-based Deep Learning Model for 1–5-year Breast Cancer Risk Prediction

Prediction of tensile performance for 3D printed photopolymer gyroid lattices using structural porosity, base material properties, and machine learning

Tensile performance data of 3D printed photopolymer gyroid lattices

Applied machine learning as a driver for polymeric biomaterials design

Interpretable algorithmic forensics

Why black box machine learning should be avoided for high-stakes decisions, in brief

A Robust Approach to Quantifying Uncertainty in Matching Problems of Causal Inference

Towards a comprehensive evaluation of dimension reduction methods for transcriptomic data visualization

Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effects

A case-based interpretable deep learning model for classification of mass lesions in digital mammography

A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results

Exploring the cloud of variable importance for the set of all good models

Collaborators (10)

Ran Xiao

Lynda Brinson

Alina Kirillova

Chaofan Chen

Md. Noor-E-Alam

Duc Do

Lesia Semenova

Chiara Daraio

David Margolis

Jacob Peloquin

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