Meet the Class of 2019: Introducing RBC Borealis’s Graduate Fellowship winners

RBC Borealis is thrilled to announce our 2018-2019 Graduate Fellowship winners. The fellowships were awarded to exceptional students pursuing graduate-level studies in machine learning and artificial intelligence at top universities across Canada. 

Each of our winners demonstrated outstanding research capabilities, provided strong references, and outlined a clear, thoughtful research focus for the current academic year. We were overwhelmed by the exceptional calibre of this year’s candidates and our adjudication committee had no easy task selecting the 10 finalists. 

We’re proud to introduce our inaugural group (below) and look forward to meeting everyone in person on March 1 as we host an event in their honour in Montreal. 

Alexandra Kearney

School: University of Alberta, Amii, PhD candidate
Research Interests: Reinforcement learning
Research Topic: The Predictive Approach to Knowledge 

Alexia Jolicœur-Martineau

School: Université de Montréal, Mila, PhD candidate
Research Areas: Deep generative modelling, computational statistics
Research Topic: Understanding, improving, and extending GANs

Andre Cianflone

School: McGill University, Mila/RLLab, PhD candidate
Research Interests: Language and interaction, reinforcement learning
Research Topic: Emergent Communication and Representation Learning

Bahare Fatemi

School: University of British Columbia, PhD candidate
Research Interests: ML on knowledge graphs
Research Topic: Improved Knowledge Graph Embedding Using Ontology, Time, and Higher Arity Relations

Gauthier Gidel

School: Université de Montréal, Mila, PhD candidate
Research Interest: Optimization, multi-agent learning
Research Topic: Efficient Saddle-Point Optimization for Modern Machine Learning

Mehrdad Ghadiri

School: University of British Columbia, MSc candidate
Research Interests: Discrete and continuous optimization, theoretical ML, ML and data mining, design and analysis of algorithms, computational neuroscience, computational biology
Research Topic: Non-homogenous Stochastic Gradient Descent

Paul Vicol

School: University of Toronto, Vector Institute, PhD candidate
Research Interests: Optimization, regularization, Bayesian neural networks, generative models
Research Topic: Online Hyperparameter Adaptation for Improved Training and Generalization 

Priyank Jaini

School: University of Waterloo, PhD candidate
Research Interests: Deep generative models, mixture models, online learning, sum-product networks, optimization
Research Topic: Deep Homogenous Mixture Models: Representation, Separation

Scott Fujimoto

School: McGill University, Mila, PhD candidate
Research Interest: Deep reinforcement learning
Research Topic: Unifying Imitation and RL for Data-Efficient Learning

Shengyang Sun

School: University of Toronto, Vector Institute, PhD candidate
Research Areas: Bayesian deep learning, generalization
Research Topic: Reliable Uncertainty Estimation in Bayesian Neural Networks