Cynthia D. Rudin

About

Cynthia D. Rudin is a professor of computer science, electrical and computer engineering, statistical science, and biostatistics & bioinformatics at Duke University. She directs the Interpretable Machine Learning Lab and holds the Gilbert, Louis, and Edward Lehrman Distinguished Professorship. Her academic background includes an undergraduate degree from the University at Buffalo and a PhD from Princeton University. She has previously held positions at MIT, Columbia, and NYU. Her research focuses on artificial intelligence, machine learning, and data science, with an emphasis on interpretability and practical applications. Rudin has received numerous awards, including the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the AAAI, which is comparable to the Nobel Prize and the Turing Award. She is a three-time winner of the INFORMS Innovative Applications in Analytics Award and has been recognized as one of the 'Top 40 Under 40' by Poets and Quants and one of the most impressive professors at MIT by Businessinsider.com. She is a fellow of the American Statistical Association and the Institute of Mathematical Statistics, and has served as chair of sections within INFORMS and the American Statistical Association. Rudin has served on committees for DARPA, the National Institute of Justice, AAAI, ACM SIGKDD, and three committees for the National Academies of Sciences, Engineering, and Medicine. She has delivered keynote and invited talks at…

Research topics

• Machine Learning
• Artificial Intelligence
• Computer Science
• Mathematics
• Data Mining
• Theoretical computer science

Selected publications

• Interpretable machine learning: Fundamental principles and 10 grand challenges

  Statistics Surveys · 2022 · 818 citations

  1st authorCorresponding

  Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. In this work, we provide fundamental principles for interpretable ML, and dispel common misunderstandings that dilute the importance of this crucial topic. We also identify 10 technical challenge areas in interpretable machine learning and provide history and background on each problem. Some of these problems are classically important, and some are recent problems that have arisen in the last few…

  DOI

• Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization

  arXiv (Cornell University) · 2020 · 242 citations

  Dimension reduction (DR) techniques such as t-SNE, UMAP, and TriMAP have demonstrated impressive visualization performance on many real world datasets. One tension that has always faced these methods is the trade-off between preservation of global structure and preservation of local structure: these methods can either handle one or the other, but not both. In this work, our main goal is to understand what aspects of DR methods are important for preserving both local and global structure: it is d…

  DOI

• On the Existence of Simpler Machine Learning Models

  2022 ACM Conference on Fairness, Accountability, and Transparency · 2022 · 66 citations

  It is almost always easier to find an accurate-but-complex model than an accurate-yet-simple model. Finding optimal, sparse, accurate models of various forms (linear models with integer coefficients, decision sets, rule lists, decision trees) is generally NP-hard. We often do not know whether the search for a simpler model will be worthwhile, and thus we do not go to the trouble of searching for one. In this work, we ask an important practical question: can accurate-yet-simple models be proven t…

  PublisherOA PDFDOI

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

  Computer Methods in Applied Mechanics and Engineering · 2025-03-19 · 6 citations

  articleOpen access

  Manipulating the dispersive characteristics of vibrational waves is beneficial for many applications, e.g., high-precision instruments. architected hierarchical phononic materials have sparked promise tunability of elastodynamic waves and vibrations over multiple frequency ranges. In this article, hierarchical unit-cells are obtained, where features at each length scale result in a band gap within a targeted frequency range. Our novel approach, the “hierarchical unit-cell template method ,” is a…

  PublisherDOI

• Dimension Reduction with Locally Adjusted Graphs

  Proceedings of the AAAI Conference on Artificial Intelligence · 2025-04-11 · 5 citations

  articleOpen accessSenior author

  Dimension reduction (DR) algorithms have proven to be extremely useful for gaining insight into large-scale high-dimensional datasets, particularly finding clusters in transcriptomic data. The initial phase of these DR methods often involves converting the original high-dimensional data into a graph. In this graph, each edge represents the similarity or dissimilarity between pairs of data points. However, this graph is frequently suboptimal due to unreliable high-dimensional distances and the li…

  PublisherOA PDFDOI

Recent grants

• CAREER: New Approaches for Ranking in Machine Learning

  NSF · $480k · 2011–2017

• FAI: An Interpretable AI Framework for Care of Critically Ill Patients Involving Matching and Decision Trees

  NSF · $625k · 2022–2026

• CAREER: New Approaches for Ranking in Machine Learning

  NSF · $480k · 2016–2018

Frequent coauthors

• Alina Jade Barnett

  Duke University

  38 shared

• Alexander Volfovsky

  Duke University

  32 shared

• M. Brandon Westover

  Harvard University

  31 shared

• Margo Seltzer

  28 shared

• Wendong Ge

  Beth Israel Deaconess Medical Center

  24 shared

• Edward P. Browne

  University of North Carolina at Chapel Hill

  23 shared

• Chaofan Chen

  Southeast University

  23 shared

• Lesia Semenova

  Duke University

  22 shared

Awards & honors

• 2022 Squirrel AI Award for Artificial Intelligence for the B…
• Three-time winner of the INFORMS Innovative Applications in…
• Fellow of the American Statistical Association
• Fellow of the Institute of Mathematical Statistics
• Named as one of the "Top 40 Under 40" by Poets and Quants in…

Similar researchers at Duke University

• John H. Reifh-68
• Pankaj K. Agarwalh-62
• Alberto Bartesaghih-56