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.

Kamyar Azizzadenesheli
(CS)
Machine Learning,
Reinforcement Learning,
Learning Theory

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

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

David Gleich
(CS)
Large Scale Network,
Matrix Methods

Dan Goldwasser
(CS)
Machine Learning, Natural
Language Processing

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

Pan Li
(CS)
Machine Learning, Graph Representation Learning

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

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

Ming Yin
(CS)
Social computing, Crowdsourcing

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

Collaborators

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

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