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MINDS Data Science, Machine Learning, and AI @ Purdue

Purdue MINDS aims to address complex problems in data science, machine learning, and artificial intelligence for the benefit of society.
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Kamyar Azizzadenesheli
(CS)
Machine Learning,
Reinforcement Learning,
Learning Theory

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Chris Clifton
(CS)
Privacy issues in Machine
Learning and Data Mining

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Petros Drineas
(CS)
Randomized Linear Algebra,
Matrix Factorizations,
Dimensionality Reduction

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David Gleich
(CS)
Large Scale Network,
Matrix Methods

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Dan Goldwasser
(CS)
Machine Learning, Natural
Language Processing

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Jean Honorio
(CS)
Learning Theory, Optimization,
Game Theory, Graphical Models

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Pan Li
(CS)
Machine Learning, Graph Representation Learning

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Jennifer Neville
(CS & Statistics)
Data Mining, Machine Learning, Social Networks

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Bruno Ribeiro
(CS)
Machine Learning, Graph Representation Learning, Causality & Invariances

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Ming Yin
(CS)
Social computing, Crowdsourcing

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Yexiang Xue
(CS)
Probabilistic reasoning, Statistical modeling, Decision-making Under Uncertainty

Collaborators

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Vinayak Rao
(Statistics)
Nonparametric Bayes,
Machine Learning,
Bayesian statistics

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Eugenio Culurciello
(Affiliated: Biomedical Engineering)
Deep Learning,
Neural Networks