World's Best Scientists 2026 revealed!

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Computer Science

D-Index
66
Citations
43168
World Ranking
2249
National Ranking
1121

Overview

Rich Caruana is affiliated with Microsoft in the United States. Their research primarily spans the fields of computer science and medicine, with significant contributions in artificial intelligence and healthcare applications. Their work focuses on explainable artificial intelligence (XAI), machine learning in healthcare, adversarial robustness, and specialized topics such as maternal and fetal healthcare, pregnancy, and preeclampsia studies.

Their recent publications include the following papers:

  • Neural Additive Models: Interpretable Machine Learning with Neural Nets, 2020, arXiv (Cornell University)
  • Augmenting interpretable models with large language models during training, 2023, Nature Communications
  • Rethinking Interpretability in the Era of Large Language Models, 2024, arXiv (Cornell University)
  • Considerations when learning additive explanations for black-box models, 2023, Machine Learning
  • Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values, 2022, Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Caruana's frequent co-authors include Harsha Nori, Benjamin J. Lengerich, Ian Painter, Kristin Sitcov, and Tomas M. Bosschieter. Collaboration with these researchers is reflected in multiple publications.

The venues where Caruana often publishes are arXiv (Cornell University), American Journal of Obstetrics and Gynecology, bioRxiv (Cold Spring Harbor Laboratory), the Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, and the International Journal of Radiation Oncology*Biology*Physics.

Their main fields of study encompass:

  • Computer Science
  • Medicine

Subfields of study include:

  • Artificial Intelligence
  • Pediatrics, Perinatology and Child Health
  • Obstetrics and Gynecology
  • Statistics and Probability
  • Surgery

The primary research topics covered in their work are:

  • Explainable Artificial Intelligence (XAI)
  • Machine Learning in Healthcare
  • Adversarial Robustness in Machine Learning
  • Topic Modeling
  • Maternal and fetal healthcare
  • Pregnancy and preeclampsia studies
  • Machine Learning and Data Classification

Best Publications

  • Multitask Learning

    Rich Caruana

  • An empirical comparison of supervised learning algorithms

    Rich Caruana;Alexandru Niculescu-Mizil

  • Model compression

    Cristian Buciluǎ;Rich Caruana;Alexandru Niculescu-Mizil

  • Do Deep Nets Really Need to be Deep

    Jimmy Ba;Rich Caruana

  • Multitask learning

    Rich Caruana

  • Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission

    Rich Caruana;Yin Lou;Johannes Gehrke;Paul Koch

  • Predicting good probabilities with supervised learning

    Alexandru Niculescu-Mizil;Rich Caruana

  • Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping

    Rich Caruana;Steve Lawrence;C. Lee Giles

  • Multitask learning: a knowledge-based source of inductive bias

    Rich Caruana

  • Ensemble selection from libraries of models

    Rich Caruana;Alexandru Niculescu-Mizil;Geoff Crew;Alex Ksikes

  • Removing the Genetics from the Standard Genetic Algorithm

    Shumeet Baluja;Rich Caruana

  • Greedy attribute selection

    Rich Caruana;Dayne Freitag

  • An empirical evaluation of supervised learning in high dimensions

    Rich Caruana;Nikos Karampatziakis;Ainur Yessenalina

  • Experience with a learning personal assistant

    Tom M. Mitchell;Rich Caruana;Dayne Freitag;John McDermott

  • Self-Optimizing Memory Controllers: A Reinforcement Learning Approach

    Engin Ipek;Onur Mutlu;José F. Martínez;Rich Caruana

  • Intelligible models for classification and regression

    Yin Lou;Rich Caruana;Johannes Gehrke

  • Accurate intelligible models with pairwise interactions

    Yin Lou;Rich Caruana;Johannes Gehrke;Giles Hooker

  • Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning

    Harmanpreet Kaur;Harsha Nori;Samuel Jenkins;Rich Caruana

  • Data mining in metric space: an empirical analysis of supervised learning performance criteria

    Rich Caruana;Alexandru Niculescu-Mizil

  • Semi-Supervised Clustering with User Feedback

    David Cohn;Rich Caruana;Andrew Kachites McCallum

  • Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining

    Pavel Berkhin;Rich Caruana;Xindong Wu

  • InterpretML: A Unified Framework for Machine Learning Interpretability.

    Harsha Nori;Samuel Jenkins;Paul Koch;Rich Caruana

Frequent Co-Authors

Giles Hooker
Giles Hooker University of Pennsylvania
Eric Horvitz
Eric Horvitz Microsoft (United States)
Matthew Richardson
Matthew Richardson Microsoft (United States)
Abdel-rahman Mohamed
Abdel-rahman Mohamed Facebook (United States)
Ece Kamar
Ece Kamar Microsoft (United States)
Matthai Philipose
Matthai Philipose Microsoft (United States)
Dale R. Durran
Dale R. Durran University of Washington
Johannes Gehrke
Johannes Gehrke Microsoft (United States)
Martin Schulz
Martin Schulz Technical University of Munich
Charles Sutton
Charles Sutton Google (United States)

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