Dan is a Professor at IST Austria. His research focuses on high-performance algorithms, and spans from algorithms and lower bounds, to practical implementations. Before IST, he was a researcher at ETH Zurich and Microsoft Research, Cambridge, UK. Prior to that, he was a Postdoctoral Associate at MIT CSAIL, working with Prof. Nir Shavit. He received his PhD from the EPFL, under the guidance of Prof. Rachid Guerraoui. His research is supported by a 2018 ERC Starting Grant, the Austrian FWF, and generous support from Amazon and Google. He is also a Principal Machine Learning Researcher at Neural Magic.
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.
Aakanksha has led the effort on training large language models at Google Research which led to the 540B PaLM model. Aakanksha has also been a core member of the Pathways project at Google. Prior to joining Google, Aakanksha led interdisciplinary teams at Microsoft Research and Princeton University across machine learning, distributed systems and networking. Aakanksha completed her PhD in Electrical Engineering from Stanford University, and was awarded the Paul Baran Marconi Young Scholar Award for the outstanding scientific contributions in the field of communications and the Internet.
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.
Pavlo Molchanov is a principal research scientist and research lead at NVIDIA Research working in the Learning and Perception Team led by Jan Kautz. He obtained a PhD in 2015 from Tampere University of Technology, Finland. During his PhD studies, Molchanov received the EuRAD Best Paper Award in 2011 and the EuRAD Young Engineer Award in 2013. Since joining NVIDIA in 2015, his research focus has been on efficient deep learning for computer vision and low-latency machine learning, including structured sparsity via channel and token pruning, neural architecture search (NAS), dynamic and adaptive inference for neural networks, and inverting neural networks.
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.
Jeff Dean is a Google Senior Fellow and SVP for Google Research and AI, which focuses on basic computer science and AI research and their use in important problem domains. His work has been integral to much of Google’s infrastructure and developer and machine learning tools. Jeff has a Ph.D. in Computer Science from the University of Washington and a B.S. in Computer Science & Economics from the University of Minnesota. He was awarded the 2012 ACM Prize in Computing, the 2021 IEEE John von Neumann medal, and is a member of the U.S. National Academy of Engineering and the American Academy of Arts and Sciences, and a Fellow of the ACM.
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.