Ruslan Salakhutdinov

Ruslan Salakhutdinov received his PhD in machine learning (computer science) from the University of Toronto in 2009. After spending two post-doctoral years at the Massachusetts Institute of Technology Artificial Intelligence Lab, he joined the University of Toronto as an Assistant Professor in the Department of Computer Science and Department of Statistics. Dr. Salakhutdinov’s primary interests lie in statistical machine learning, Deep Learning, probabilistic graphical models, and large-scale optimization. He is an action editor of the Journal of Machine Learning Research and served on the senior programme committee of several learning conferences including NIPS and ICML. He is the recipient of the Early Researcher Award, Connaught New Researcher Award, Alfred P. Sloan Research Fellowship, Microsoft Research Faculty Fellowship, Google Faculty Research Award, and a Fellow of the Canadian Institute for Advanced Research. A major focus on Dr. Salakhutdinov’s work is Deep Learning, an active research area that stems, in large part, from his 2006 Science article co-authored with Geoffrey Hinton. This work developed a formulation for stacked Restricted Boltzmann Machines (RBMs), showing an algorithm capable of learning large-scale multi-layer networks using a combination of unsupervised pre-training followed by supervised training. Dr. Salakhutdinov subsequently pioneered a new class of deep generative models, called Deep Boltzmann Machines (DBMs). These are probabilistic graphical models that contain multiple layers of latent variables. Each nonlinear layer captures progressively more complex patterns of data, which is a promising way of solving visual object recognition, language understanding, and speech perception problems. Dr. Salakhutdinov’s contributions to Deep Learning have already received over 5000 citations according to Google Scholar, and have been applied broadly in speech, language, and image analysis.