Borealis AI Graduate Fellowships - The Class of 2020
May 28, 2020
Meet the ten recipients of this year's fellowships.
School:Perimeter Institute for Theoretical Physics, University of Waterloo. Research areas: Theoretical physics. Research topic: Machine Learning for physics and physics for machine learning.
School: AMII, University of Alberta. Research areas: Machine learning, deep learning and natural language processing. Research topic: Towards empathetic conversational AI.
Elahe Rahimian Najafabadi
School:Concordia Institute for Information System Engineering (CIISE), Concordia University, Montreal. Research areas: Deep learning in health domain applications. Research topic: Designing optimal deep neural networks for hand gesture recognition and force prediction, and developing domain adaptation algorithms for time-domain features.
School:University of Toronto. Research areas: Machine learning. Research topic: Interplay between optimization, generalization and uncertainty of deep learning.
School:Centre for Intelligent Machines (CIM), McGill University. Research areas: Computer vision and artificial intelligence. Research topic: Towards building reliable deep neural network.
School:University of British Columbia. Research areas: Deep learning, chemistry, natural language processing, artificial intelligence, generative models, mass spectrometry. Research topic: Automated discovery of unknown molecules using deep neural networks.
Reyhane Askari Hemmat
School:Mila, Université de Montréal. Research areas: Optimization and deep learning. Research topic: A dynamical systems perspective into game optimization.
School:Mila, Université de Montréal. Research areas: Natural language processing and deep learning. Research topic: Learning and modeling neural representations of text.
School:Artificial Intelligence and Algorithms laboratories at the University of British Columbia. Research areas: Stochastic processes - neural networks - DNA computing. Research topic: Mean first passage time and parameter estimation for continous-time Markov Chains.
School:McMaster University. Research areas: Deep learning, watermarking, steganography, information-theoretical principles. Research topic: New deep neural network architectures for blind image watermarking based on the information-theoretic principles.