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Cynthia D. Rudin
Interpretable Machine Learning
Healthcare Predictive Modeling
Criminal Justice Risk Assessment
Energy Systems Optimization
About
Cynthia D. Rudin is the Gilbert, Louis, and Edward Lehrman Distinguished Professor of Computer Science at Duke University, with joint appointments in Electrical & Computer Engineering, Mathematics, Statistical Science, and Biostatistics & Bioinformatics. She directs the Interpretable Machine Learning Lab, where her research focuses on designing interpretable machine learning models that humans can understand and trust, and applying them to real‑world problems in healthcare, criminal justice, energy systems, and more. She received her Ph.D. from Princeton University and undergraduate degrees from the University at Buffalo. Rudin is a winner of prestigious awards such as the AAAI’s Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity, INFORMS prizes, a Guggenheim Fellowship, and is a fellow of multiple professional societies in statistics and AI
Research Performance Summary
1994-2026
Active Research Span
First Recorded Paper
OPERA TIONS RESEARCH CENTER
Year:
1994
Citations:
0
Venue:
N/A
Latest Recorded Paper
Applying a Cost-Benefit Analysis to Geofence Searches
Year:
2026
Citations:
0
Venue:
Jurimetrics, forthcoming (2026)
Last 10 Years Publication Activity
This timeline shows the professor's yearly publication activity.
Publication Venues and Collaboration
Journal, Conference, and Book Publication Breakdown
Research Impact by Period
Papers
37
Citations
73
Avg. Citations / Paper
2
H-Index
4
Papers
148
Citations
7488
Avg. Citations / Paper
50.6
H-Index
37
Papers
71
Citations
26326
Avg. Citations / Paper
370.8
H-Index
33
Papers
67
Citations
3563
Avg. Citations / Paper
53.2
H-Index
28
Papers
12
Citations
968
Avg. Citations / Paper
80.7
H-Index
9
Papers
4
Citations
196
Avg. Citations / Paper
49
H-Index
4
Papers
2
Citations
24
Avg. Citations / Paper
12
H-Index
1
Papers
1
Citations
0
Avg. Citations / Paper
0
H-Index
0
Contact and Professional Links
Detected Research Keywords
Interpretable Machine Learning
Machine Learning Models
Sparse Decision Trees
Matching Causal Inference
Machine Learning Approach
Stop Explaining Black
Explaining Black Box
Black Box Machine
Box Machine Learning
Models Stakes Decisions