The Promise and Limitations of Machine Learning

Ruslan Salakhutdinov, Carnegie Mellon University

The Promise and Limitations of Machine Learning

Ruslan Salakhutdinov, Associate Professor of Mechanical Engineering, Carnegie Mellon University

Ruslan Salakhutdinov received his PhD in computer science from the University of Toronto in 2009. After spending two postdoctoral years at the Massachusetts Institute of Technology Artificial Intelligence Lab, he joined the University of Toronto as an assistant professor in the Departments of Statistics and Computer Science. In 2016 he joined the Machine Learning Department at Carnegie Mellon University as an associate professor. Ruslan's primary interests lie in deep learning, machine learning, and large-scale optimization. His main research goal is to understand the computational and statistical principles required for discovering structure in large amounts of data. He is an action editor of the Journal of Machine Learning Research and served on the senior program committee of several learning conferences, including NIPS and ICML. He is an Alfred P. Sloan Research Fellow, a Microsoft Research Faculty Fellow, a Canada Research Chair in Statistical Machine Learning, a senior fellow of the Canadian Institute for Advanced Research, and a recipient of the Early Researcher Award, Connaught New Researcher Award, Google Faculty Award, and Nvidia's Pioneers of AI award.

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Intelligent Machines
Artificial intelligence and robots are transforming how we work and live.