Gintare Karolina Dziugaite
Karolina is a senior research scientist at Google Brain, based in Toronto, an adjunct professor in the McGill University School of Computer Science, and an associate industry member of Mila, the Quebec AI Institute. Prior to joining Google, Karolina led the Trustworthy AI program at Element AI / ServiceNow. Karolina's research combines theoretical and empirical approaches to understanding deep learning, with a focus on generalization and network compression.
Karolina obtained my Ph.D. in machine learning from the University of Cambridge, under the supervision of Zoubin Ghahramani and studied Mathematics at the University of Warwick and read Part III in Mathematics at the University of Cambridge, receiving a Masters of Advanced Study (MASt) in Applied Mathematics.
Ce is an Assistant Professor in Computer Science at ETH Zurich. The mission of his research is to make machine learning techniques widely accessible---while being cost-efficient and trustworthy---to everyone who wants to use them to make our world a better place. He believes in a system approach to enabling this goal, and his current research focuses on building next-generation machine learning platforms and systems that are data-centric, human-centric, and declaratively scalable. Before joining ETH, Ce finished his PhD at the University of Wisconsin-Madison and spent another year as a postdoctoral researcher at Stanford, both advised by Christopher Ré. His work has received recognitions such as the SIGMOD Best Paper Award, SIGMOD Research Highlight Award, Google Focused Research Award, an ERC Starting Grant, and has been featured and reported by Science, Nature, the Communications of the ACM, and a various media outlets such as Atlantic, WIRED, Quanta Magazine, etc.
Yani is an Assistant Professor at the University of Calgary in the Department of Electrical and Software Engineering of the Schulich School of Engineering, and leads the Calgary Machine Learning Lab. He was previously a Postdoctoral Research Fellow at the Vector Institute and University of Guelph, working with Prof. Graham Taylor, and a Visiting Researcher at Google Brain Toronto/Google AR Core. He completed his PhD at the University of Cambridge in 2018 supported by a Microsoft Research Ph.D. Scholarship, where I was supervised by Professor Roberto Cipolla and Dr. Antonio Criminisi. He is currently interested in efficient deep learning, specifically for computer vision problems, and sparse neural network training. I have in the past worked on exoplanet detection with NASA, medical imaging with Microsoft Research and the Gates Foundation, and 3D computer vision methods for processing and recognizing objects in large point clouds.
Martha White is an Associate Professor of Computing Science at the University of Alberta and a PI of Amii--the Alberta Machine Intelligence Institute--which is one of the top machine learning centers in the world. She holds a Canada CIFAR AI Chair and received IEEE's "AIs 10 to Watch: The Future of AI" award in 2020. She has authored more than 50 papers in top journals and conferences. Martha is an associate editor for TPAMI, and has served as co-program chair for ICLR and area chair for many conferences in AI and ML, including ICML, NeurIPS, AAAI and IJCAI. Her research focus is on developing algorithms for agents continually learning on streams of data, with an emphasis on representation learning and reinforcement learning.