Joel is an Applied Research Scientist in the Emerging Technologies Lab at ServiceNow Research, with experience in various fields of artificial intelligence including computer vision and natural language processing. He focuses on efficient implementations of deep neural networks, particularly on large scale distributed training and large language models. He recently proposed a new form of 3d parallelism, which improves on the state of the art in scalability and computational efficiency.
Prior to joining ServiceNow, he was a graduate researcher at the Perimeter Institute for Theoretical Physics, where he worked on exact computations in supersymmetric quantum field theory and string theory. He has a Ph.D. in physics from the University of Waterloo (2016).