Cynthia Rudin

Cynthia Rudin

Duke University

H-index: 56

North America-United States

About Cynthia Rudin

Cynthia Rudin, With an exceptional h-index of 56 and a recent h-index of 47 (since 2020), a distinguished researcher at Duke University, specializes in the field of machine learning, interpretability, data science.

His recent articles reflect a diverse array of research interests and contributions to the field:

OKRidge: Scalable Optimal k-Sparse Ridge Regression

Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data

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

A Path to Simpler Models Starts With Noise

Interpretable deep learning in medical imaging

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

Exploring and Interacting with the Set of Good Sparse Generalized Additive Models

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

Cynthia Rudin Information

University

Position

Professor of Computer Science ECE and Statistics

Citations(all)

21008

Citations(since 2020)

17829

Cited By

7529

hIndex(all)

56

hIndex(since 2020)

47

i10Index(all)

126

i10Index(since 2020)

102

Email

University Profile Page

Duke University

Google Scholar

View Google Scholar Profile

Cynthia Rudin Skills & Research Interests

machine learning

interpretability

data science

Top articles of Cynthia Rudin

Title

Journal

Author(s)

Publication Date

OKRidge: Scalable Optimal k-Sparse Ridge Regression

Advances in Neural Information Processing Systems

Jiachang Liu

Sam Rosen

Chudi Zhong

Cynthia Rudin

2024/2/13

Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data

Srikar Katta

Harsh Parikh

Cynthia Rudin

Alexander Volfovsky

2024/4/18

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

INFORMS Journal on Data Science

Siong Thye Goh

Lesia Semenova

Cynthia Rudin

2024/1/11

A Path to Simpler Models Starts With Noise

Advances in Neural Information Processing Systems

Lesia Semenova

Harry Chen

Ronald Parr

Cynthia Rudin

2024/2/13

Interpretable deep learning in medical imaging

Cynthia Rudin

2024/4/3

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

Genomics, Proteomics & Bioinformatics

Manickam Ashokkumar

Wenwen Mei

Jackson J Peterson

Yuriko Harigaya

David M Murdoch

...

2024/1/10

Exploring and Interacting with the Set of Good Sparse Generalized Additive Models

Advances in Neural Information Processing Systems

Chudi Zhong

Zhi Chen

Jiachang Liu

Margo Seltzer

Cynthia Rudin

2024/2/13

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

Radiology

Jon Donnelly

Luke Moffett

Alina Jade Barnett

Hari Trivedi

Fides Schwartz

...

2024/3/19

Evaluating Pre-Trial Programs Using Interpretable Machine Learning Matching Algorithms for Causal Inference

Travis Seale-Carlisle

Saksham Jain

Courtney Lee

Caroline Levenson

Swathi Ramprasad

...

2024

The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance

Advances in Neural Information Processing Systems

Jon Donnelly

Srikar Katta

Cynthia Rudin

Edward Browne

2024/2/13

Sparse and Faithful Explanations Without Sparse Models

arXiv preprint arXiv:2402.09702

Yiyang Sun

Zhi Chen

Vittorio Orlandi

Tong Wang

Cynthia Rudin

2024/2/15

SiamQuality: A ConvNet-Based Foundation Model for Imperfect Physiological Signals

arXiv preprint arXiv:2404.17667

Cheng Ding

Zhicheng Guo

Zhaoliang Chen

Randall J Lee

Cynthia Rudin

...

2024/4/26

Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise …

IEEE Journal of Biomedical and Health Informatics

Cheng Ding

Zhicheng Guo

Cynthia Rudin

Ran Xiao

Amit Shah

...

2024/2/1

This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations

Advances in Neural Information Processing Systems

Chiyu Ma

Brandon Zhao

Chaofan Chen

Cynthia Rudin

2024/2/13

Safe and Interpretable Estimation of Optimal Treatment Regimes

Harsh Parikh

Quinn M Lanners

Zade Akras

Sahar Zafar

M Brandon Westover

...

2024/4/18

Optimal Sparse Survival Trees

arXiv preprint arXiv:2401.15330

Rui Zhang

Rui Xin

Margo Seltzer

Cynthia Rudin

2024/1/27

From feature importance to distance metric: An almost exact matching approach for causal inference

arXiv e-prints

Quinn Lanners

Harsh Parikh

Alexander Volfovsky

Cynthia Rudin

David Page

2023/2

Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?

Zhi Chen

Sarah Tan

Urszula Chajewska

Cynthia Rudin

Rich Caruna

2023/6/13

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

Materials & Design

Jacob Peloquin

Alina Kirillova

Cynthia Rudin

LC Brinson

Ken Gall

2023/8/1

Learned Kernels for Interpretable and Efficient PPG Signal Quality Assessment and Artifact Segmentation

arXiv preprint arXiv:2307.05385

Sully F Chen

Zhicheng Guo

Cheng Ding

Xiao Hu

Cynthia Rudin

2023/7/6

See List of Professors in Cynthia Rudin University(Duke University)

Co-Authors

H-index: 83
Ingrid Daubechies

Ingrid Daubechies

Duke University

H-index: 61
M. Brandon Westover, MD, PhD

M. Brandon Westover, MD, PhD

Harvard University

H-index: 41
Rebecca J. Passonneau

Rebecca J. Passonneau

Penn State University

H-index: 25
Tyler H. McCormick

Tyler H. McCormick

University of Washington

H-index: 23
Sudeepa Roy

Sudeepa Roy

Duke University

H-index: 15
Berk Ustun

Berk Ustun

University of California, San Diego

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