Machine Learning for a better financial future.
Borealis AI conducts research in artificial intelligence for financial services. We are a large team of researchers with backgrounds across artificial intelligence including computer vision, machine learning, and natural language processing, with PhDs in computer science, physics, computational finance, mathematics and more.
The research team undertakes fundamental and applied research, publishes papers, and works with large-scale datasets, deriving impactful machine learning models in collaboration with machine learning product owners and software engineers who help bring the research and prototypes to life.
Publications
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What Constitutes Good Contrastive Learning in Time-Series Forecasting?
What Constitutes Good Contrastive Learning in Time-Series Forecasting?
C. Zhang, Q. Yan, L. Meng, and T. Sylvain. Workshop at International Joint Conference on Artificial Intelligence (IJCAI), 2023
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Constant Memory Attention Block
Constant Memory Attention Block
L. Feng, F. Tung, H. Hajimirsadeghi, Y. Bengio, and M. O. Ahmed. Workshop at International Conference on Machine Learning (ICML), 2023
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DynaShare: Task and Instance Conditioned Parameter Sharing for Multi-Task Learning
DynaShare: Task and Instance Conditioned Parameter Sharing for Multi-Task Learning
E. Rahimian, G. Javadi, F. Tung, and G. Oliveira. Workshop at The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023
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Ranking Regularization for Critical Rare Classes: Minimizing False Positives at a High True Positive Rate
Ranking Regularization for Critical Rare Classes: Minimizing False Positives at a High True Positive Rate
*M. Kiarash, H. Zhao, M. Zhai, and F. Tung. The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023
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Meta Temporal Point Processes
Meta Temporal Point Processes
W. Bae, M. O. Ahmed, F. Tung, and G. Oliveira. International Conference on Learning Representations (ICLR), 2023
North Star
Research Areas
We focus on a set of challenging North Star research problems: Asynchronous Temporal Models, Non-Cooperative Learning in Competing Markets, and Causal Machine Learning from Observational Data.
Academic Partnerships
Our partnerships enable us to benefit from the latest in academia and transform that knowledge into real-world impact.
Let’s SOLVE It
New and diverse perspectives, awareness of challenges specific to local communities, and commitment to making a difference are needed today more than ever. Let’s SOLVE it is a Borealis AI mentorship program for undergraduate students on a mission to solve real problem in their communities using AI. Let’s SOLVE it together.
Fellowships
Supporting academic research sits at the core of Borealis AI. Our Fellowship program supports graduate students’ research and career goals, helping advance the science of AI.
Internships
Research interns work with all our teams, collaborate with RBC on large-scale projects, and publish original research.