A structured reference to the individuals shaping artificial intelligence across research, industry, governance, ethics, and public discourse. Curated as a resource for professionals navigating the AI landscape. For more see the full list at AI People.

Machine Learning and Statistical Learning Theory

Vladimir Vapnik | Statistical Learning Theory | United States Developed statistical learning theory and co-invented Support Vector Machines with Corinna Cortes. His VC (Vapnik-Chervonenkis) theory provides the mathematical foundation for understanding when and why machine learning algorithms generalise from training data. Key works: "The Nature of Statistical Learning Theory" (1995)

Corinna Cortes | Machine Learning | United States Co-invented Support Vector Machines with Vapnik, creating one of the most influential machine learning algorithms of the 1990s and 2000s. Led machine learning research at Google, applying these methods at unprecedented scale. Key works: "Support-Vector Networks" (1995)

Michael I. Jordan | Probabilistic Models, Machine Learning Foundations | United States One of the most cited researchers in machine learning, known for work on graphical models, variational inference, and the mathematical foundations of ML. Trained many leading researchers including Yoshua Bengio and Zoubin Ghahramani. His 2019 essay warning about AI hype remains widely referenced. Key works: Foundational papers on graphical models, variational methods, and Bayesian approaches

Bernhard Schölkopf | Kernel Methods, Causality | Germany Pioneered kernel methods in machine learning and later became a leading voice on causality-arguing that understanding cause and effect, not just correlation, is essential for robust AI. Leads the Max Planck Institute for Intelligent Systems. Key works: "Learning with Kernels" (2002), causality research

Leo Breiman | Ensemble Methods | United States Created random forests and bagging-techniques that combine multiple models to improve accuracy. His 2001 paper "Statistical Modeling: The Two Cultures" articulated the tension between traditional statistics and machine learning approaches that persists today. Key works: "Random Forests" (2001), "Statistical Modeling: The Two Cultures" (2001)

Daphne Koller | Probabilistic Models, AI in Biology | United States Co-authored the definitive textbook on probabilistic graphical models. Co-founded Coursera, democratising AI education globally. Now leads Insitro, applying machine learning to drug discovery-demonstrating the transition from AI research to real-world impact. Key works: "Probabilistic Graphical Models" (2009), Coursera platform

Christopher Bishop | Bayesian Methods, ML Education | United Kingdom Wrote "Pattern Recognition and Machine Learning," one of the most influential ML textbooks. Led Microsoft Research Cambridge and shaped how a generation learned the mathematical foundations of machine learning. Key works: "Pattern Recognition and Machine Learning" (2006)

Leslie Valiant | Computational Learning Theory | United States Created PAC (Probably Approximately Correct) learning theory, providing the first rigorous mathematical framework for understanding machine learning. Turing Award winner whose theoretical work underpins our understanding of what is learnable. Key works: "A Theory of the Learnable" (1984)

Trevor Hastie, Robert Tibshirani & Jerome Friedman | Statistical Learning | United States Co-authored "The Elements of Statistical Learning," the canonical textbook bridging statistics and machine learning. Tibshirani invented LASSO regularisation; Friedman developed gradient boosting. Their work established statistical learning as a rigorous discipline. Key works: "The Elements of Statistical Learning" (2001), LASSO, gradient boosting

Yoav Freund & Robert Schapire | Boosting | United States Created AdaBoost, demonstrating that combining weak learners could produce strong predictive models. Their theoretical and practical work on boosting influenced machine learning competitions and production systems for two decades. Key works: AdaBoost algorithm and boosting theory

Kevin Murphy | Probabilistic ML, Education | United States Author of "Probabilistic Machine Learning," a comprehensive modern textbook that has become essential reading. His clear exposition has helped thousands of practitioners understand the mathematical foundations of modern AI. Key works: "Probabilistic Machine Learning" (2022)

How to Use This Directory

For research: Each entry includes key works and affiliations for deeper investigation.

For event planning: Filter by geographic base, domain, or public engagement experience.

For understanding the field: The categorisation reveals how different communities, from technical researchers, ethicists, policymakers, industry leaders all shape AI development.

For identifying perspectives: Note whose voices are included and whose might be missing from any particular AI conversation.

This directory is maintained as a resource for the AI age. Last updated: 2026.

Curated by Rahim Hirji for thesuperskills.com.

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