Cynthia D. Rudin

Research Themes

Applications, Artificial Intelligence & Machine Learning

Education

• Ph.D. Princeton University, 2004

Positions

• Gilbert, Louis, and Edward Lehrman Distinguished Professor
• Professor of Computer Science
• Professor of Electrical and Computer Engineering
• Professor of Biostatistics and Bioinformatics
• Professor of Statistical Science
• Professor of Mathematics

Courses Taught

• STA 693: Research Independent Study
• STA 671D: Theory and Algorithms for Machine Learning
• STA 493: Research Independent Study
• STA 393: Research Independent Study
• ME 555: Advanced Topics in Mechanical Engineering
• MATH 494: Research Independent Study
• MATH 491: Independent Study
• ISS 796T: Bass Connections Information, Society & Culture Research Team
• ISS 396T: Bass Connections Information, Society & Culture Research Team
• ECE 687D: Theory and Algorithms for Machine Learning
• ECE 493: Projects in Electrical and Computer Engineering
• ECE 392: Projects in Electrical and Computer Engineering
• COMPSCI 671D: Theory and Algorithms for Machine Learning
• COMPSCI 474: Data Science Competition
• COMPSCI 394: Research Independent Study
• COMPSCI 393: Research Independent Study
• COMPSCI 391: Independent Study

Publications

• Zhang H, Mahabadi RK, Rudin C, Guilleminot J, Brinson LC. Uncertainty quantification of acoustic metamaterial bandgaps with stochastic material properties and geometric defects (Accepted). Computers and Structures. 2024 Dec 1;305.
• Chen SF, Guo Z, Ding C, Hu X, Rudin C. Sparse learned kernels for interpretable and efficient medical time series processing. Nature Machine Intelligence. 2024 Oct 1;6(10):1132–44.
• Semenova L, Wang Y, Falcinelli S, Archin N, Cooper-Volkheimer AD, Margolis DM, et al. Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy. Elife. 2024 Sep 9;13.
• Hahn S, Yin J, Zhu R, Xu W, Jiang Y, Mak S, et al. SentHYMNent: An Interpretable and Sentiment-Driven Model for Algorithmic Melody Harmonization. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2024. p. 5050–60.
• Ding C, Guo Z, Chen Z, Lee RJ, Rudin C, Hu X. SiamQuality: a ConvNet-based foundation model for photoplethysmography signals. Physiological measurement. 2024 Aug;45(8).
• Parikh H, Sun H, Amerineni R, Rosenthal ES, Volfovsky A, Rudin C, et al. How many patients do you need? Investigating trial designs for anti-seizure treatment in acute brain injury patients. Annals of clinical and translational neurology. 2024 Jul;11(7):1681–90.
• Bravo F, Rudin C, Shaposhnik Y, Yuan Y. Interpretable Prediction Rules for Congestion Risk in Intensive Care Units. Stochastic Systems. 2024 Jun 1;14(2):111–30.
• Ashokkumar M, Mei W, Peterson JJ, Harigaya Y, Murdoch DM, Margolis DM, et al. Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation. Genomics Proteomics Bioinformatics. 2024 May 9;22(1).
• Zhang R, Xin R, Seltzer M, Rudin C. Optimal Sparse Survival Trees. In: Proceedings of machine learning research. 2024. p. 352–60.
• Ding C, Guo Z, Rudin C, Xiao R, Shah A, Do DH, et al. Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms. IEEE journal of biomedical and health informatics. 2024 May;28(5):2650–61.
• Seale-Carlisle T, Jain S, Lee C, Levenson C, Ramprasad S, Garrett B, et al. Evaluating Pre-trial Programs Using Interpretable Machine Learning Matching Algorithms for Causal Inference. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2024. p. 22331–40.
• Donnelly J, Moffett L, Barnett AJ, Trivedi H, Schwartz F, Lo J, et al. AsymMirai: Interpretable Mammography-based Deep Learning Model for 1-5-year Breast Cancer Risk Prediction. Radiology. 2024 Mar;310(3):e232780.
• Garrett BL, Rudin C. The Right to a Glass Box: Rethinking the Use of Artificial Intelligence in Criminal Justice. Cornell Law Review. 2024;109(3):561–627.
• Sun Y, Chen Z, Orlandi V, Wang T, Rudin C. Sparse and Faithful Explanations Without Sparse Models. In: Proceedings of Machine Learning Research. 2024. p. 2071–9.
• Parikh H, Lanners Q, Akras Z, Zafar SF, Westover MB, Rudin C, et al. Safe and Interpretable Estimation of Optimal Treatment Regimes. In: Proceedings of Machine Learning Research. 2024. p. 2134–42.
• Katta S, Parikh H, Rudin C, Volfovsky A. Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data. In: Proceedings of Machine Learning Research. 2024. p. 3340–8.
• Yang J, Barnett AJ, Donnelly J, Kishore S, Fang J, Schwartz FR, et al. FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 2024. p. 5003–9.
• Mahabadi RK, Chen Z, Daraio C, Rudin C, Brinson LC. A robust framework for the generation of random metamaterials based on a graph algorithm. In: Proceedings of SPIE - The International Society for Optical Engineering. 2024.
• Rudin C, Zhong C, Semenova L, Seltzer M, Parr R, Liu J, et al. Position: Amazing Things Come From Having Many Good Models. In: Proceedings of Machine Learning Research. 2024. p. 42783–95.
• Zhong C, Chen Z, Liu J, Seltzer M, Rudin C. Exploring and Interacting with the Set of Good Sparse Generalized Additive Models. Advances in neural information processing systems. 2023 Dec;36:56673–99.
• Liu J, Rosen S, Zhong C, Rudin C. OKRidge: Scalable Optimal k-Sparse Ridge Regression. Advances in neural information processing systems. 2023 Dec;36:41076–258.
• Semenova L, Chen H, Parr R, Rudin C. A Path to Simpler Models Starts With Noise. Advances in neural information processing systems. 2023 Dec;36:3362–401.
• Falcinelli SD, Cooper-Volkheimer AD, Semenova L, Wu E, Richardson A, Ashokkumar M, et al. Impact of Cannabis Use on Immune Cell Populations and the Viral Reservoir in People With HIV on Suppressive Antiretroviral Therapy. J Infect Dis. 2023 Nov 28;228(11):1600–9.
• Garrett BL, Rudin C. Interpretable algorithmic forensics. Proceedings of the National Academy of Sciences of the United States of America. 2023 Oct;120(41):e2301842120.
• Hahn S, Zhu R, Mak S, Rudin C, Jiang Y. An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2023. p. 4089–99.
• Parikh H, Hoffman K, Sun H, Zafar SF, Ge W, Jing J, et al. Effects of epileptiform activity on discharge outcome in critically ill patients in the USA: a retrospective cross-sectional study. The Lancet Digital health. 2023 Aug;5(8):e495–502.
• Peloquin J, Kirillova A, Rudin C, Brinson LC, Gall K. Prediction of tensile performance for 3D printed photopolymer gyroid lattices using structural porosity, base material properties, and machine learning. Materials and Design. 2023 Aug 1;232.
• McDonald SM, Augustine EK, Lanners Q, Rudin C, Catherine Brinson L, Becker ML. Applied machine learning as a driver for polymeric biomaterials design. Nature communications. 2023 Aug;14(1):4838.
• Peloquin J, Kirillova A, Mathey E, Rudin C, Brinson LC, Gall K. Tensile performance data of 3D printed photopolymer gyroid lattices. Data in brief. 2023 Aug;49:109396.
• Zhang R, Xin R, Seltzer M, Rudin C. Optimal Sparse Regression Trees. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023. 2023. p. 11270–9.
• Zhang R, Xin R, Seltzer M, Rudin C. Optimal Sparse Regression Trees. In: Proceedings of the . AAAI Conference on Artificial Intelligence AAAI Conference on Artificial Intelligence. 2023. p. 11270–9.
• Wang C, Han B, Patel B, Rudin C. In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction. Journal of Quantitative Criminology. 2023 Jun 1;39(2):519–81.
• Donnelly J, Rudin C, Katta S, Browne EP. The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance. In: Advances in Neural Information Processing Systems. 2023.
• Agnew E, Qiu M, Zhu L, Wiseman S, Rudin C. The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2023. p. 1627–38.
• Ou YJ, Barnett AJ, Mitra A, Schwartz FR, Chen C, Grimm L, et al. A user interface to communicate interpretable AI decisions to radiologists. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2023.
• Ma C, Zhao B, Chen C, Rudin C. This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations. In: Advances in Neural Information Processing Systems. 2023.
• Chen Z, Tan S, Chajewska U, Rudin C, Caruana R. Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? In: Proceedings of Machine Learning Research. 2023. p. 86–99.
• Lanners Q, Parikh H, Volfovsky A, Rudin C, Page D. Variable Importance Matching for Causal Inference. In: Proceedings of Machine Learning Research. 2023. p. 1174–84.
• Zhou L, Rudin C, Gombolay M, Spohrer J, Zhou M, Paul S. From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues. AIS Transactions on Human-Computer Interaction. 2023 Jan 1;15(1):111–35.
• Rudin C. Why black box machine learning should be avoided for high-stakes decisions, in brief. Nature Reviews Methods Primers. 2022 Dec 1;2(1).
• Chen Z, Ogren A, Daraio C, Brinson LC, Rudin C. How to see hidden patterns in metamaterials with interpretable machine learning. Extreme Mechanics Letters. 2022 Nov 1;57.
• Behrouz A, Lécuyer M, Rudin C, Seltzer M. Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design. In: CEUR workshop proceedings. 2022. p. 26.
• Parikh H, Rudin C, Volfovsky A. MALTS: Matching After Learning to Stretch. Journal of Machine Learning Research. 2022 Aug 1;23.
• Afnan M, Afnan MAM, Liu Y, Savulescu J, Mishra A, Conitzer V, et al. Data solidarity for machine learning for embryo selection: a call for the creation of an open access repository of embryo data. Reproductive biomedicine online. 2022 Jul;45(1):10–3.
• Huang H, Wang Y, Rudin C, Browne EP. Towards a comprehensive evaluation of dimension reduction methods for transcriptomic data visualization. Communications biology. 2022 Jul;5(1):719.
• Semenova L, Rudin C, Parr R. On the Existence of Simpler Machine Learning Models. In: ACM International Conference Proceeding Series. 2022. p. 1827–58.
• Wang T, Rudin C. Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effects. INFORMS Journal on Computing. 2022 May 1;34(3):1626–43.
• Liu J, Zhong C, Seltzer M, Rudin C. Fast Sparse Classification for Generalized Linear and Additive Models. Proceedings of machine learning research. 2022 Mar;151:9304–33.
• Barnett AJ, Sharma V, Gajjar N, Fang J, Schwartz FR, Chen C, et al. Interpretable Deep Learning Models for Better Clinician-AI Communication in Clinical Mammography. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2022.
• Liu J, Zhong C, Li B, Seltzer M, Rudin C. FasterRisk: Fast and Accurate Interpretable Risk Scores. In: Advances in Neural Information Processing Systems. 2022.
• Lobo E, Singh H, Petrik M, Rudin C, Lakkaraju H. Data Poisoning Attacks on Off-Policy Policy Evaluation Methods. In: Proceedings of Machine Learning Research. 2022. p. 1264–74.
• Chen C, Lin K, Rudin C, Shaposhnik Y, Wang S, Wang T. A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations. Decision Support Systems. 2022 Jan 1;152.
• Wang ZJ, Zhong C, Xin R, Takagi T, Chen Z, Chau DH, et al. TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization. In: Proceedings - 2022 IEEE Visualization Conference - Short Papers, VIS 2022. 2022. p. 60–4.
• Rudin C, Chen C, Chen Z, Huang H, Semenova L, Zhong C. Interpretable machine learning: Fundamental principles and 10 grand challenges. Statistics Surveys. 2022 Jan 1;16:1–85.
• Xin R, Zhong C, Chen Z, Takagi T, Seltzer M, Rudin C. Exploring the Whole Rashomon Set of Sparse Decision Trees. In: Advances in neural information processing systems. 2022. p. 14071–84.
• Li C, Rudin C, McCormick TH. Rethinking Nonlinear Instrumental Variable Models through Prediction Validity. Journal of machine learning research : JMLR. 2022 Jan;23:96.
• McTavish H, Zhong C, Achermann R, Karimalis I, Chen J, Rudin C, et al. Fast Sparse Decision Tree Optimization via Reference Ensembles. Proceedings of the . AAAI Conference on Artificial Intelligence AAAI Conference on Artificial Intelligence. 2022 Jan;36(9):9604–13.
• Lobo E, Singh H, Petrik M, Rudin C, Lakkaraju H. Data Poisoning Attacks on Off-Policy Policy Evaluation Methods. In: Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022. 2022. p. 1264–74.
• Barnett AJ, Schwartz FR, Tao C, Chen C, Ren Y, Lo JY, et al. A case-based interpretable deep learning model for classification of mass lesions in digital mammography. Nature Machine Intelligence. 2021 Dec 1;3(12):1061–70.
• Guo Z, Ding C, Hu X, Rudin C. A supervised machine learning semantic segmentation approach for detecting artifacts in plethysmography signals from wearables. Physiological measurement. 2021 Dec;42(12).
• Coker B, Rudin C, King G. A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results. Management Science. 2021 Oct 1;67(10):6174–97.
• Afnan MAM, Rudin C, Conitzer V, Savulescu J, Mishra A, Liu Y, et al. Ethical Implementation of Artificial Intelligence to Select Embryos in in Vitro Fertilization. In: AIES 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. 2021. p. 316–26.
• Wang J, Zhang X, Zhou Y, Suh C, Rudin C. There once was a really bad poet, it was automated but you didn’t know it. Transactions of the Association for Computational Linguistics. 2021 Jul 8;9:605–20.
• Barnett AJ, Schwartz FR, Tao C, Chen C, Ren Y, Lo JY, et al. IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography. 2021 Mar 23;
• Gupta NR, Orlandi V, Chang C-R, Wang T, Morucci M, Dey P, et al. dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference. 2021 Jan 5;
• Afnan MAM, Liu Y, Conitzer V, Rudin C, Mishra A, Savulescu J, et al. Interpretable, not black-box, artificial intelligence should be used for embryo selection. Human reproduction open. 2021 Jan;2021(4):hoab040.
• Traca S, Rudin C, Yan W. Regulating greed over time in multi-armed bandits. Journal of Machine Learning Research. 2021 Jan 1;22.
• Wang T, Morucci M, Awan MU, Liu Y, Roy S, Rudin C, et al. FLAME: A fast large-scale almost matching exactly approach to causal inference. Journal of Machine Learning Research. 2021 Jan 1;22.
• Wang Y, Huang H, Rudin C, Shaposhnik Y. Understanding how dimension reduction tools work: An empirical approach to deciphering T-SNE, UMAP, TriMap, and PaCMAP for data visualization. Journal of Machine Learning Research. 2021 Jan 1;22.
• Wang T, Morucci M, Awan MU, Liu Y, Roy S, Rudin C, et al. FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference. J Mach Learn Res. 2021;22:31:1-31:1.
• Koyyalagunta D, Sun A, Draelos RL, Rudin C. Playing codenames with language graphs and word embeddings. Journal of Artificial Intelligence Research. 2021 Jan 1;71:319–46.
• Dong J, Rudin C. Exploring the cloud of variable importance for the set of all good models. Nature Machine Intelligence. 2020 Dec 1;2(12):810–24.
• Chen Z, Bei Y, Rudin C. Concept whitening for interpretable image recognition. Nature Machine Intelligence. 2020 Dec 1;2(12):772–82.
• Huang Q, Zhou Y, Du X, Chen R, Wang J, Rudin C, et al. Cryo-ZSSR: multiple-image super-resolution based on deep internal learning. 2020 Nov 22;
• Wang T, Ye W, Geng D, Rudin C. Towards Practical Lipschitz Bandits. In: FODS 2020 - Proceedings of the 2020 ACM-IMS Foundations of Data Science Conference. 2020. p. 129–38.
• Rich AS, Rudin C, Jacoby DMP, Freeman R, Wearn OR, Shevlin H, et al. AI reflections in 2019. Nature Machine Intelligence. 2020 Jan 17;2(1):2–9.
• Lin J, Zhong C, Hu D, Rudin C, Seltzer M. Generalized and scalable optimal sparse decision trees. In: 37th International Conference on Machine Learning, ICML 2020. 2020. p. 6106–16.
• Morucci M, Orlandi V, Rudin C, Roy S, Volfovsky A. Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation. In: Proceedings of Machine Learning Research. 2020. p. 1089–98.
• Awan MU, Morucci M, Orlandi V, Roy S, Rudin C, Volfovsky A. Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference. In: Proceedings of Machine Learning Research. 2020. p. 3252–62.
• Wang T, Rudin C. Bandits for bmo functions. In: 37th International Conference on Machine Learning, ICML 2020. 2020. p. 9938–48.
• Menon S, Damian A, Hu S, Ravi N, Rudin C. PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2020. p. 2434–42.
• Gregory H, Li S, Mohammadi P, Tarn N, Draelos R, Rudin C. A transformer approach to contextual sarcasm detection in twitter. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2020. p. 270–5.
• Gregory H, Li S, Mohammadi P, Tarn N, Draelos R, Rudin C. A Transformer Approach to Contextual Sarcasm Detection in Twitter. In: FIGURATIVE LANGUAGE PROCESSING. 2020. p. 270–5.
• Morucci M, Orlandi V, Rudin C, Roy S, Volfovsky A. Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation. CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE (UAI 2020). 2020;124:1089–98.
• Awan MU, Roy S, Morucci M, Rudin C, Orlandi V, Volfovsky A. Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference. INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 108. 2020;108:3252–61.
• Wang F, Rudin C, Mccormick TH, Gore JL. Modeling recovery curves with application to prostatectomy. Biostatistics (Oxford, England). 2019 Oct;20(4):549–64.
• Rudin C. Do Simpler Models Exist and How Can We Find Them? In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM; 2019. p. 1–2.
• Ustun B, Rudin C. Learning optimized risk scores. Journal of Machine Learning Research. 2019 Jun 1;20.
• Rudin C, Shaposhnik Y. Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation. https://www.jmlr.org/papers/. 2019 May 28;24.
• Bravo F, Rudin C, Shaposhnik Y, Yuan Y. Simple Rules for Predicting Congestion Risk in Queueing Systems: Application to ICUs. 2019 May 7;
• Rudin C. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nature machine intelligence. 2019 May;1(5):206–15.
• Dieng A, Liu Y, Roy S, Rudin C, Volfovsky A. Interpretable Almost-Exact Matching for Causal Inference. Proceedings of machine learning research. 2019 Apr;89:2445–53.
• Usaid Awan M, Liu Y, Morucci M, Roy S, Rudin C, Volfovsky A. Interpretable almost-matching-exactly with instrumental variables. 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019. 2019 Jan 1;
• Tracà S, Rudin C, Yan W. Reducing exploration of dying arms in mortal bandits. In: 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019. 2019.
• Rudin C, Carlson D. The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to Be More Effective at Data Analysis. In: Operations Research & Management Science in the Age of Analytics. INFORMS; 2019. p. 44–72.
• Parikh H, Rudin C, Volfovsky A. An Application of Matching After Learning To Stretch (MALTS) to the ACIC 2018 Causal Inference Challenge Data. Observational Studies. 2019 Jan 1;5(2):118–30.
• Tracà S, Rudin C, Yan W. Reducing Exploration of Dying Arms in Mortal Bandits. In: Proceedings of Machine Learning Research. 2019. p. 156–63.
• Awan MU, Liu Y, Morucci M, Roy S, Rudin C, Volfovsky A. Interpretable Almost-Matching-Exactly With Instrumental Variables. In: Proceedings of Machine Learning Research. 2019. p. 1116–26.
• Fisher A, Rudin C, Dominici F. All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously. Journal of machine learning research : JMLR. 2019 Jan;20:177.
• Chen C, Li O, Tao C, Barnett AJ, Su J, Rudin C. This looks like that: Deep learning for interpretable image recognition. In: Advances in Neural Information Processing Systems. 2019.
• Usaid Awan M, Liu Y, Morucci M, Roy S, Rudin C, Volfovsky A. Interpretable almost-matching-exactly with instrumental variables. In: 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019. 2019.
• Tracà S, Rudin C, Yan W. Reducing exploration of dying arms in mortal bandits. In: 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019. 2019.
• Ban GY, Rudin C. The big Data newsvendor: Practical insights from machine learning. Operations Research. 2019 Jan 1;67(1):90–108.
• Hase P, Chen C, Li O, Rudin C. Interpretable Image Recognition with Hierarchical Prototypes. In: Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. 2019. p. 32–40.
• Timofte R, Gu S, Wu J, Van Gool L, Zhang L, Yang MH, et al. NTIRE 2018 challenge on single image super-resolution: Methods and results. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 2018. p. 965–76.
• Bei Y, Damian A, Hu S, Menon S, Ravi N, Rudin C. New techniques for preserving global structure and denoising with low information loss in single-image super-resolution. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 2018. p. 987–94.
• Rudin C, Ertekin Ş. Learning customized and optimized lists of rules with mathematical programming. Mathematical Programming Computation. 2018 Dec 1;10(4):659–702.
• Parikh H, Rudin C, Volfovsky A. MALTS: Matching After Learning to Stretch. Journal.ofMachineLearningResearch 23(240) (2022) 1-42. 2018 Nov 18;
• Rudin C, Ustunb B. Optimized scoring systems: Toward trust in machine learning for healthcare and criminal justice. Interfaces. 2018 Sep 1;48(5):449–66.
• Liu Y, Dieng A, Roy S, Rudin C, Volfovsky A. Interpretable Almost Matching Exactly for Causal Inference. 2018 Jun 18;
• Vu M-AT, Adalı T, Ba D, Buzsáki G, Carlson D, Heller K, et al. A Shared Vision for Machine Learning in Neuroscience. J Neurosci. 2018 Feb 14;38(7):1601–7.
• Rudin C, Wang Y. Direct learning to rank and rerank. In: International Conference on Artificial Intelligence and Statistics, AISTATS 2018. 2018. p. 775–83.
• Li O, Liu H, Chen C, Rudin C. Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions. In: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. 2018. p. 3530–7.
• Angelino E, Larus-Stone N, Alabi D, Seltzer M, Rudin C. Learning certifiably optimal rule lists for categorical data. Journal of Machine Learning Research. 2018 Jan 1;18:1–78.
• Chen C, Rudin C. An optimization approach to learning falling rule lists. In: International Conference on Artificial Intelligence and Statistics, AISTATS 2018. 2018. p. 604–12.
• Struck AF, Ustun B, Ruiz AR, Lee JW, LaRoche SM, Hirsch LJ, et al. Association of an Electroencephalography-Based Risk Score With Seizure Probability in Hospitalized Patients. JAMA neurology. 2017 Dec;74(12):1419–24.
• Ustun B, Rudin C. Optimized risk scores. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017. p. 1125–34.
• Angelino E, Larus-Stone N, Alabi D, Seltzer M, Rudin C. Learning certifiably optimal rule lists. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017. p. 35–44.
• Wang T, Rudin C, Doshi-Velez F, Liu Y, Klampfl E, MacNeille P. A Bayesian framework for learning rule sets for interpretable classification. Journal of Machine Learning Research. 2017 Aug 1;18:1–37.
• Letham B, Letham PA, Rudin C, Browne EP. Erratum: "Prediction uncertainty and optimal experimental design for learning dynamical systems" [Chaos 26, 063110 (2016)]. Chaos (Woodbury, NY). 2017 Jun;27(6):069901.
• Zeng J, Ustun B, Rudin C. Interpretable classification models for recidivism prediction. Journal of the Royal Statistical Society Series A: Statistics in Society. 2017 Jun 1;180(3):689–722.
• Ustun B, Adler LA, Rudin C, Faraone SV, Spencer TJ, Berglund P, et al. The World Health Organization Adult Attention-Deficit/Hyperactivity Disorder Self-Report Screening Scale for DSM-5. JAMA psychiatry. 2017 May;74(5):520–7.
• Yang H, Rudin C, Seltzer M. Scalable Bayesian rule lists. In: 34th International Conference on Machine Learning, ICML 2017. 2017. p. 5971–80.
• Lakkaraju H, Rudin C. Learning cost-effective and interpretable treatment regimes. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017. 2017.
• Lakkaraju H, Rudin C. Learning cost-effective and interpretable treatment regimes. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017. 2017.
• Letham B, Letham LM, Rudin C. Bayesian inference of arrival rate and substitution behavior from sales transaction data with stockouts. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016. p. 1695–704.
• Wang T, Rudin C, Velez-Doshi F, Liu Y, Klampfl E, Macneille P. Bayesian rule sets for interpretable classification. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2016. p. 1269–74.
• Moghaddass R, Rudin C, Madigan D. The factorized self-controlled case series method: An approach for estimating the effects of many drugs on many outcomes. Journal of Machine Learning Research. 2016 Jun 1;17.
• Letham B, Letham PA, Rudin C, Browne EP. Prediction uncertainty and optimal experimental design for learning dynamical systems. Chaos (Woodbury, NY). 2016 Jun;26(6):063110.
• Souillard-Mandar W, Davis R, Rudin C, Au R, Libon DJ, Swenson R, et al. Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test. Machine Learning. 2016 Mar 1;102(3):393–441.
• Ustun B, Rudin C. Supersparse linear integer models for optimized medical scoring systems. Machine Learning. 2016 Mar 1;102(3):349–91.
• Ustun B, Westover MB, Rudin C, Bianchi MT. Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine. 2016 Feb;12(2):161–8.
• Browne EP, Letham B, Rudin C. A Computational Model of Inhibition of HIV-1 by Interferon-Alpha. PloS one. 2016 Jan;11(3):e0152316.
• Garg VK, Rudin C, Jaakkola T. CRAFT: ClusteR-specific Assorted Feature selecTion. In: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016. 2016. p. 305–13.
• Ertekin Ş, Rudin C. A bayesian approach to learning scoring systems. Big Data. 2015 Dec 1;3(4):267–76.
• Moghaddass R, Rudin C. The latent state hazard model, with application to wind turbine reliability. Annals of Applied Statistics. 2015 Dec 1;9(4):1823–63.
• Tulabandhula T, Rudin C. Generalization bounds for learning with linear, polygonal, quadratic and conic side knowledge. Machine Learning. 2015 Sep 17;100(2–3):183–216.
• Letham B, Rudin C, McCormick TH, Madigan D. Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model. Annals of Applied Statistics. 2015 Sep 1;9(3):1350–71.
• Wang T, Rudin C, Wagner D, Sevieri R. Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores. Big Data. 2015 Mar 1;3(1):3–21.
• Wang F, Rudin C. Falling rule lists. In: Journal of Machine Learning Research. 2015. p. 1013–22.
• Ertekin Ş, Rudin C, McCormick TH. Reactive point processes: A new approach to predicting power failures in underground electrical systems. Annals of Applied Statistics. 2015 Jan 1;9(1):122–44.
• Rudin C. Turning prediction tools into decision tools. Vol. 9356. 2015.
• Rudin C. Turning prediction tools into decision tools. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015.
• Tulabandhula T, Rudin C. Tire changes, fresh air, and yellow flags: Challenges in predictive analytics for professional racing. Big Data. 2014 Jun 1;2(2):97–112.
• Tulabandhula T, Rudin C. Generalization bounds for learning with linear and quadratic side knowledge. In: International Symposium on Artificial Intelligence and Mathematics, ISAIM 2014. 2014.
• Huggins J, Rudin C. Toward a theory of pattern discovery. In: International Symposium on Artificial Intelligence and Mathematics, ISAIM 2014. 2014.
• Tulabandhula T, Rudin C. On combining machine learning with decision making. In: Machine Learning. 2014. p. 33–64.
• Ertekin S, Rudin C, Hirsh H. Approximating the crowd. Data Mining and Knowledge Discovery. 2014 Jan 1;28(5–6):1189–221.
• Tulabandhula T, Rudin C. Generalization bounds for learning with linear and quadratic side knowledge. In: International Symposium on Artificial Intelligence and Mathematics, ISAIM 2014. 2014.
• Kim B, Rudin C. Learning about meetings. Data Mining and Knowledge Discovery. 2014 Jan 1;28(5–6):1134–57.
• Wang D, Passonneau RJ, Collins M, Rudin C. Modeling weather impact on a secondary electrical grid. In: Procedia Computer Science. 2014. p. 631–8.
• Kim B, Rudin C, Shah J. The Bayesian case model: A generative approach for case-based reasoning and prototype classification. In: Advances in Neural Information Processing Systems. 2014. p. 1952–60.
• Huggins JH, Rudin C. A statistical learning theory framework for supervised pattern discovery. In: SIAM International Conference on Data Mining 2014, SDM 2014. 2014. p. 506–14.
• Goh ST, Rudin C. Box drawings for learning with imbalanced data. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2014. p. 333–42.
• Tulabandhula T, Rudin C. Robust optimization using machine learning for uncertainty sets. In: International Symposium on Artificial Intelligence and Mathematics, ISAIM 2014. 2014.
• Huggins J, Rudin C. Toward a theory of pattern discovery. In: International Symposium on Artificial Intelligence and Mathematics, ISAIM 2014. 2014.
• Rudin C, Wagstaff KL. Machine learning for science and society. Machine Learning. 2014 Jan 1;95(1):1–9.
• Rudin C, Ertekin S, Passonneau R, Radeva A, Tomar A, Xie B, et al. Analytics for power grid distribution reliability in New York City. Interfaces. 2014 Jan 1;44(4):364–82.
• Tulabandhula T, Rudin C. Robust optimization using machine learning for uncertainty sets. In: International Symposium on Artificial Intelligence and Mathematics, ISAIM 2014. 2014.
• Letham B, Rudin C, Heller KA. Growing a list. Data Mining and Knowledge Discovery. 2013 Dec 1;27(3):372–95.
• Rudin C, Letham B, Madigan D. Learning theory analysis for association rules and sequential event prediction. Journal of Machine Learning Research. 2013 Nov 1;14:3441–92.
• Letham B, Rudin C, Madigan D. Sequential event prediction. Machine Learning. 2013 Nov 1;93(2–3):357–80.
• Wang T, Rudin C, Wagner D, Sevieri R. Learning to detect patterns of crime. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2013. p. 515–30.
• Mukherjee I, Rudin C, Schapire RE. The rate of convergence of AdaBoost. Journal of Machine Learning Research. 2013 Aug 1;14:2315–47.
• Tulabandhula T, Rudin C. Machine learning with operational costs. Journal of Machine Learning Research. 2013 Jun 1;14:1989–2028.
• Ertekin S, Rudin C, McCormick TH. Predicting power failures with reactive point processes. In: AAAI Workshop - Technical Report. 2013. p. 23–5.
• Kim B, Rudin C. Machine learning for meeting analysis. In: AAAI Workshop - Technical Report. 2013. p. 59–61.
• Letham B, Rudin C, McCormick TH, Madigan D. An interpretable stroke prediction model using rules and Bayesian analysis. In: AAAI Workshop - Technical Report. 2013. p. 65–7.
• Wang T, Rudin C, Wagner D, Sevieri R. Detecting patterns of crime with Series Finder. In: AAAI Workshop - Technical Report. 2013. p. 140–2.
• Ustun B, Tracà S, Rudin C. Supersparse linear integer models for predictive scoring systems. In: AAAI Workshop - Technical Report. 2013. p. 128–30.
• Ertekin S, Hirsh H, Rudin C. Selective sampling of labelers for approximating the crowd. In: AAAI Fall Symposium - Technical Report. 2012. p. 7–13.
• Bertsimas D, Chang A, Rudin C. An integer optimization approach to associative classification. In: Advances in Neural Information Processing Systems. 2012. p. 3302–10.
• Tulabandhula T, Rudin C. The influence of operational cost on estimation. In: International Symposium on Artificial Intelligence and Mathematics, ISAIM 2012. 2012.
• Chang A, Rudin C, Cavaretta M, Robert Thomas, Gloria Chou. How to reverse-engineer quality rankings. Machine Learning. 2012 Sep 1;88(3):369–98.
• McCormick TH, Rudin C, Madigan D. Bayesian hierarchical rule modeling for predicting medical conditions. Annals of Applied Statistics. 2012 Jun 1;6(2):652–68.
• Rudin C, Waltz D, Anderson R, Boulanger A, Salleb-Aouissi A, Chow M, et al. Machine learning for the New York City power grid. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2012 Jan 1;34(2):328–45.
• Tulabandhula T, Rudin C, Jaillet P. The machine learning and traveling repairman problem. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2011. p. 262–76.
• Ertekin S, Rudin C. On equivalence relationships between classification and ranking algorithms. Journal of Machine Learning Research. 2011 Oct 1;12:2905–29.
• Wu L, Kaiser G, Rudin C, Anderson R. Data quality assurance and performance measurement of data mining for preventive maintenance of power grid. In: Proceedings of the 1st International Workshop on Data Mining for Service and Maintenance, KDD4Service 2011 - Held in Conjunction with SIGKDD’11. 2011. p. 28–32.
• Wu L, Teravainen T, Kaiser G, Anderson R, Boulanger A, Rudin C. Estimation of system reliability using a semiparametric model. In: IEEE 2011 EnergyTech, ENERGYTECH 2011. 2011.
• Rudin C, Letham B, Kogan E, Madigan D. A Learning Theory Framework for Association Rules and Sequential Events. 2011 Jun 20;
• Rudin C, Passonneau RJ, Radeva A, Ierome S, Isaac DF. 21st-century data miners meet 19th-century electrical cables. Computer. 2011 Jun 1;44(6):103–5.
• McCormick T, Rudin C, Madigan D. A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction. 2011 Jan 6;
• Mukherjee I, Rudin C, Schapire RE. The rate of convergence of AdaBoost. In: Journal of Machine Learning Research. 2011. p. 537–57.
• Rudin C, Letham B, Salleb-Aouissi A, Kogan E, Madigan D. Sequential event prediction with association rules. In: Journal of Machine Learning Research. 2011. p. 615–34.
• Rudin C, Passonneau RJ, Radeva A, Dutta H, Ierome S, Isaac D. A process for predicting manhole events in Manhattan. Machine Learning. 2010 Jul 1;80(1):1–31.
• Pelossof R, Jones M, Vovsha I, Rudin C. Online coordinate boosting. In: 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009. 2009. p. 1354–61.
• Radeva A, Rudin C, Passonneau R, Isaac D. Report cards for manholes: Eliciting expert feedback for a learning task. In: 8th International Conference on Machine Learning and Applications, ICMLA 2009. 2009. p. 719–24.
• Rudin C. The P-norm push: A simple convex ranking algorithm that concentrates at the top of the list. Journal of Machine Learning Research. 2009 Nov 30;10:2233–71.
• Rudin C, Schapire RE. Margin-based ranking and an equivalence between AdaBoost and RankBoost. Journal of Machine Learning Research. 2009 Nov 30;10:2193–232.
• Passonneau RJ, Rudin C, Radeva A, Liu ZA. Reducing noise in labels and features for a real world dataset: Application of NLP corpus annotation methods. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2009. p. 86–97.
• Roth R, Rambow O, Habash N, Diab M, Rudin C. Arabic morphological tagging, diacritization, and lemmatization using lexeme models and feature ranking. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. 2008. p. 117–20.
• Roth R, Rambow O, Habash N, Diab M, Rudin C. Arabic morphological tagging, diacritization, and lemmatization using lexeme models and feature ranking. In: ACL-08: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference. 2008. p. 117–20.
• Rudin C, Schapire RE, Daubechies I. Analysis of boosting algorithms using the smooth margin function. Annals of Statistics. 2007 Dec 1;35(6):2723–68.
• Rudin C. Ranking with a P-norm push. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2006. p. 589–604.
• Ji H, Rudin C, Grishman R. Re-Ranking Algorithms for Name Tagging. In: HLT-NAACL 2006 - Computationally Hard Problems and Joint Inference in Speech and Language Processing, Proceedings of the Workshop. 2006. p. 49–56.
• Rudin C, Cortes C, Mohri M, Schapire RE. Margin-based ranking meets boosting in the middle. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. p. 63–78.
• Rudin C, Daubechies I, Schapire RE. The dynamics of AdaBoost: Cyclic behavior and convergence of margins. Journal of Machine Learning Research. 2004 Dec 1;5:1557–95.
• Rudin C, Schapire RE, Daubechies I. Boosting based on a smooth margin. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 2004 Jan 1;3120:502–17.
• Rudin C, Daubechies I, Schapire RE. On the dynamics of boosting. In: Advances in Neural Information Processing Systems. 2004.
• Parikh H, Hoffman K, Sun H, Ge W, Jing J, Liu L, et al. Why Interpretable Causal Inference is Important for High-Stakes Medical Decision Making in Neurology and How to Do it.
• Peloquin J, Kirillova A, Rudin C, Brinson LC, Gall K. Prediction of 3d Printed Photopolymer Lattice Mechanical Performance Using Structural Porosity, Base Material Properties, and Machine Learning.

In The News

• Duke 100 Trailblazer: Cynthia Rudin (Sep 27, 2024 | Duke Centennial)
• Today’s Faculty Reflect on a Century of Scholars (Apr 15, 2024 | Trinity College of Arts & Sciences)
• Regulating Face Recognition Technology (Mar 26, 2024 | Trinity College of Arts & Science)
• Duke Awards 32 New Distinguished Professorships for 2024 (Mar 19, 2024 | Duke Today)
• Duke Faculty Join Federal Roundtable Discussion on AI (Mar 1, 2024 | Pratt School of Engineering)
• Engineering Faculty Help Students Adapt to AI in the Classroom (Oct 20, 2023 | Pratt School of Engineering)
• Is the Artificial Intelligence Boom a 'Runaway Train' ? (Feb 24, 2023 | Duke Today)
• Cynthia Rudin Wins Guggenheim Award (Apr 13, 2022 | Pratt School of Engineering)
• The First AI Breast Cancer Sleuth That Shows Its Work (Jan 20, 2022 | Pratt School of Engineering)
• The Need for Transparency and Interpretability at the Intersection of AI and Criminal Justice (Nov 22, 2021 | Duke Government Relations)
• Duke Professor Wins $1 Million Artificial Intelligence Prize, A ‘New Nobel’ (Oct 13, 2021 | Pratt School of Engineering)
• Duke Professor Wins $1 Million Artificial Intelligence Prize, A ‘New Nobel’ (Oct 12, 2021 | )
• Algorithms That Show Their Work (Aug 30, 2021 | Duke Science & Technology)
• Accurate Neural Network Computer Vision Without The ‘Black Box’ (Dec 15, 2020 | )
• Artificial Intelligence Makes Blurry Faces Look More Than 60 Times Sharper (Jun 11, 2020 | )
• To Save Lives During Seizures, Grab a Scorecard, Machine Learning Style (Dec 10, 2019 | Pratt School of Engineering)
• This A.I. Birdwatcher Lets You ‘See’ Through the Eyes of a Machine (Oct 31, 2019 | )
• Stop Gambling with Black Box and Explainable Models on High-Stakes Decisions (May 21, 2019 | Pratt School of Engineering)
• These Works of Art Were Created by Artificial Intelligence (Mar 18, 2019 | )
• Duke Team Attempts a Real-Life Version of CSI 'Zoom and Enhance' (Dec 5, 2018 | )
• Bard or Bot? (Nov 15, 2018 | )
• Opening the Lid on Criminal Sentencing Software (Jul 19, 2017 | )
• Data in, Decisions Out: Pratt's Cynthia Rudin Designs Algorithms to Turn Raw Information Into Informed Choices (Mar 15, 2017 | Pratt School of Engineering)
• Cynthia Rudin: Training Computers to Find Patterns That Humans Miss (Oct 2, 2016 | )