# Cynthia Rudin Cynthia Diane Rudin (born 1976) is an American computer scientist and statistician known for her work in interpretable machine learning. She directs the Interpretable Machine Learning Lab at Duke University and advocates for transparent AI models in high-stakes domains. Cynthia Diane Rudin (born 1976) is an American computer scientist and statistician specializing in [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) and known for her work in interpretable machine learning. She is the director of the Interpretable Machine Learning Lab at Duke University, where she is a professor of computer science, electrical and computer engineering, statistical science, and biostatistics and bioinformatics. In 2022, she won the Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence (AAAI) for her work on the importance of transparency for AI systems in high-risk domains. In addition, she was elected as a fellow of the American Association for the Advancement of Science (AAAS) in 2024, in recognition of her research in machine learning. Rudin's research challenges the prevailing reliance on [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) and [neural-network](https://www.wikiprompt.org/wiki/neural-network) models in critical applications, arguing that interpretable models can achieve comparable accuracy while providing transparency. Her advocacy has influenced policy discussions and academic practice, particularly in criminal justice and healthcare, where decisions carry significant consequences for individuals and society. ## Education and career Rudin graduated summa cum laude from the University at Buffalo with a double major in mathematical physics and music theory in 1999. She completed her Ph.D. in applied and computational mathematics at Princeton University in 2004. Her dissertation, entitled "Boosting, Margins, and Dynamics," was supervised by Ingrid Daubechies and Robert Schapire. In her graduate work, Rudin proved convergence properties of boosting algorithms, answering a well-studied question of whether AdaBoost maximizes the L1 margin, a type of distance between a decision boundary and the closest data observation. Following positions as a postdoctoral researcher at New York University and an associate research scientist at Columbia University, she took a faculty position at the MIT Sloan School of Management in 2009, and then moved to Duke University in 2016. At Duke, she holds joint appointments across multiple departments, reflecting the interdisciplinary nature of her work. Rudin has served as chair of the Data Mining Section of INFORMS and of the Statistical Learning and Data Science Section of the American Statistical Association. She served on the ISAT faculty advisory board for DARPA, was a councilor for AAAI, and a member of the Bureau of Justice Assistance Criminal Justice Technology Forecasting Group (BJA CJTFG). She currently serves on the executive committee for ACM SIGKDD and is a member of both the Committee on Applied and Theoretical Statistics (CATS) and the Committee on Law and Justice (CLAJ) of the National Academies of Sciences, Engineering, and Medicine. She is an associate editor for Management Science, the Harvard Data Science Review, and the Journal of Quantitative Criminology. ## Awards and honors Rudin's contributions have earned numerous accolades. In 2019, she was elected as a Fellow of the American Statistical Association and of the Institute of Mathematical Statistics "for her contributions to interpretable machine learning algorithms, prediction in large scale medical databases, and theoretical properties of ranking algorithms." She was elected as a Fellow of the Association for the Advancement of Artificial Intelligence in 2022 and received the Guggenheim Fellowship in Natural Sciences in the same year. She received the 2013 INFORMS Innovative Applications in Analytics Award for her work on electrical grid reliability, the 2016 INFORMS Innovative Applications in Analytics Award for work on interpretable machine learning models for assessing cognitive decline, and the 2019 INFORMS Innovative Applications in Analytics Award for work on interpretable machine learning models for seizure prediction in critically ill patients, leading to the 2HELPS2B score used in intensive care units. Rudin was the co-winner of the Manufacturing and Service Operations Management Best Operations Management Paper in Operations Research Award from INFORMS in 2021 and a winner of the FICO Recognition Award for the Explainable Machine Learning Challenge in 2018. She was a finalist for the 2017 Daniel H. Wagner Prize for Excellence in Operations Research. Rudin was named by Business Insider as one of the 12 most impressive professors at MIT in 2015. She has given keynote talks at KDD (2014 and 2019), AISTATS, the Nobel Conference (2021), and IJCAI 2025. She was named to the 2025 class of ACM Fellows "for contributions to and leadership in interpretable machine learning and societal applications." ## Interpretable machine learning advocacy Rudin is well known for her critique of black box models in the criminal justice system and for high-stakes decisions, arguing that interpretable models can be constructed that are equally accurate. Her influential paper "Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and use Interpretable Models Instead," published in Nature Machine Intelligence in 2019, outlines several reasons why post-hoc explanations of complex models are insufficient. She contends that [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence) systems used in domains like healthcare, criminal justice, and finance should be inherently transparent rather than relying on external explanation mechanisms. This perspective contrasts with the dominant trend in [generative-ai](https://www.wikiprompt.org/wiki/generative-ai) and large-scale [transformer](https://www.wikiprompt.org/wiki/transformer) models, where performance is often prioritized over interpretability. Rudin's work has become an influential source for the Human-Centered Artificial Intelligence community, which emphasizes aligning AI systems with human values and needs. She has led several efforts to encourage work on societal good applications in machine learning, including editing the Special Issue on Machine Learning for Science and Society in the Machine Learning journal and organizing the American Statistical Association's report "Discovery with Data: Leveraging Statistics with Computer Science to Transform Science and Society." ## Applications in electrical grid and public safety Starting in 2007, Rudin was the lead scientist on a collaborative project between Columbia University and Con Edison to use machine learning to maintain New York City's secondary electrical distribution network. This project, which aimed to predict and prevent equipment failures, was awarded the 2013 INFORMS Innovative Applications in Analytics Award. The work demonstrated how interpretable models could be applied to critical infrastructure, providing actionable insights for engineers while maintaining reliability. Along with student Tong Wang and detectives from the Cambridge Police Department in Cambridge, MA, Rudin developed the Series Finder algorithm for crime series detection. Series Finder was built into the Patternizr algorithm used by the NYPD to detect patterns of crime committed by the same individuals. This application in predictive policing highlighted the importance of transparency in law enforcement tools, as officers needed to understand why certain patterns were flagged. ## Medical scoring systems Rudin's work on scoring systems with former student Berk Ustun has been used for developing medical scoring systems for sleep apnea screening and diagnosis, seizure prediction in ICU patients, ADHD screening in adults, and detection of cognitive decline using handwriting analysis through the Clock Drawing test. These scoring systems are designed to be simple enough for clinicians to use without specialized software, often taking the form of additive point-based tools. This work earned the 2016 and 2019 INFORMS Innovative Applications in Analytics Award and was a finalist for the Wagner Prize. The 2HELPS2B score, developed for seizure prediction in critically ill patients, has been adopted in intensive care units, providing a practical tool that combines interpretability with predictive accuracy. Rudin's approach in medicine emphasizes that models must not only be accurate but also understandable to doctors, nurses, and patients, enabling informed decision-making. ## Teaching and student mentorship At Duke, Rudin coached two teams of undergraduate students who won the 2018 NTIRE Single Image Superresolution Competition (Track 1, classic bicubic) and the 2018 PoeTix Literary Turing Competition. These achievements reflect her commitment to mentoring students in both technical and creative applications of machine learning. Her lab, the Interpretable Machine Learning Lab, trains graduate students and postdocs in developing algorithms that prioritize transparency and fairness. Rudin's teaching extends beyond Duke, as she has delivered keynote addresses at major conferences and contributed to public discussions about the role of AI in society. Her work has been featured in outlets such as NPR, where she discussed how doctors can be sure a self-taught computer is making the right diagnosis, underscoring the practical implications of her research. ## See also - Timnit Gebru - Joy Buolamwini - Explainable artificial intelligence ## References Sources include Wikipedia (CC BY-SA) and academic publications. ## External links - Home page - Cynthia Rudin publications indexed by Google Scholar - Harris, Richard (April 2019), "How Can Doctors Be Sure A Self-Taught Computer Is Making The Right Diagnosis?", at NPR --- Source: https://www.wikiprompt.org/wiki/cynthia-rudin License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/) Last updated: 2026-09-08T15:34:47.692671+00:00