Professor Aragam studies causality, statistical machine learning, and probabilistic modeling. His current interests involve causal machine learning, deep generative models, latent variable models, and statistical learning theory. In particular, this work focuses on applications of artificial intelligence, including tools such as ChatGPT and DALL-E. He is also involved with developing open-source software and solving problems in interpretability, ethics, and fairness in artificial intelligence. His work has been published in top statistics and machine learning venues such as the Annals of Statistics, Neural Information Processing Systems, the International Conference on Machine Learning, and the Journal of Machine Learning Research.

Learn more about Professor Aragam here.