Ali Ghodsi is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, cross-appointed with the Cheriton School of Computer Science. Director of the Data Science Lab, and a Faculty Affiliate at the Vector Institute. His research focuses on advancing machine learning and artificial intelligence through both theoretical innovation and practical applications.

His work is organized around four main areas: (1) efficient and compressed neural networks, including knowledge distillation and model compression techniques for large-scale deep learning; (2) graph and sequence modeling, with a focus on methods that capture structure in complex data; (3) AI for science and biomedicine, where he develops machine learning approaches for drug action modeling and peptide sequencing; and (4) dimensionality reduction and representation learning, where he has made sustained contributions both through original research and by synthesizing the field’s foundations in widely used surveys, tutorials, and the textbook Elements of Dimensionality Reduction and Manifold Learning (Springer, 2023).

He is also the co-author of the forthcoming Elements of Deep Learning (Springer). Beyond his publications and patents, Ghodsi is widely recognized for his popular YouTube courses on machine learning and AI, which have introduced advanced concepts to a broad international audience. His work reflects a commitment to building scalable and impactful AI systems that connect rigorous methodology with real-world applications.