Dr. Richard Zemel

Professor Richard Zemel is one of the world’s leading machine learning researchers. He received his PhD from the University of Toronto in 1994, and after prestigious postdocs at the Salk Institute and Carnegie Mellon University, he joined the faculty of the University of Arizona. In 2000, he returned to the University of Toronto, where he helped to build one of the world’s finest machine learning groups. He was promoted to Professor in 2009, and was appointed the inaugural Research Director of the Vector Institute for Artificial Intelligence in 2017. In 2021, he took a leave from the University of Toronto to join Columbia University.

Professor Zemel has worked on unsupervised learning for many years. 
His PhD developed a method known as an autoencoder, which trains a neural network to predict its own inputs. Autoencoders have become a dominant theme in machine learning and led to arguably the most significant progress in machine learning -- the ability to learn representations that can be utilized for multiple tasks. Another important dimension of Professor Zemel’s research is algorithmic fairness. His seminal paper "Fairness through awareness" (2012) formalized desirable fairness properties of a classifier. His group followed up by showing how existing learning algorithms could be modified to eliminate discriminatory biases.

Professor Zemel’s group has also published some of the foundational papers on few-shot learning, which considers how new concepts and classes can be learned given only a few labeled examples. His work has shown how a neural network could be formulated to address this problem, and his 2017 paper "Prototypical networks for few-shot learning" has become a standard approach in academia and industry. Professor Zemel has also shown how neural networks could be applied to graphs, developing a message-passing approach that has been applied to many important areas, including drug discovery, health, and social networks.

Professor Zemel’s research has received numerous awards and distinctions, including the NVIDIA Pioneer of AI Award, the ONR Young Investigator Award, the NSERC Industrial Research Chair in Machine Learning, and a Canada CIFAR AI Chair. He is an internationally recognized leader in the field (60,133 citations, h-index 76, i10-index 163), having served as Program Chair (2010) and General Chair (2011) of the Neural Information Processing Systems Conference, the most elite conference in machine learning, and as General Chair of the ACM Fairness Accountability and Transparency Conference (FAccT 2021). Professor Zemel’s outstanding mentorship to Canadian students is reflected in his supervision of 19 PhD graduates and 8 postdocs, many of whom have taken on prestigious academic or industrial research roles in Canada and the US.

For his lifetime contribution to the field of AI, CAIAC is delighted to award Dr. Richard Zemel the 2023 CAIAC Lifetime Achievement Award.