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.
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AdaFlood: Adaptive Flood Regularization
AdaFlood: Adaptive Flood Regularization
W. Bae, Y. Ren, M. O. Ahmed, F. Tung, D. J. Sutherland, and G. Oliveira. Transactions on Machine Learning Research (TMLR), 2024
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Interpretation for Variational Autoencoder Used to Generate Financial Synthetic Tabular Data
Interpretation for Variational Autoencoder Used to Generate Financial Synthetic Tabular Data
J. Wu, K. N. Plataniotis, *L. Z. Liu, *E. Amjadian, and Y. A. Lawryshyn. Special Issue Interpretability, Accountability and Robustness in Machine Learning (Algorithims), 2023
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ATOM: Attention Mixer for Efficient Dataset Distillation
ATOM: Attention Mixer for Efficient Dataset Distillation
*S. Khaki, *A. Sajedi, K. Wang, L. Z. Liu, Y. A. Lawryshyn, and K. N. Plataniotis. Oral presentation at The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2024
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DataDAM: Efficient Dataset Distillation with Attention Matching
DataDAM: Efficient Dataset Distillation with Attention Matching
*A. Sajedi, *S. Khaki, E. Amjadian, L. Z. Liu, Y. A. Lawryshyn, and K. N. Plataniotis. International Conference in Computer Vision (ICCV), 2023
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EBBS: An Ensemble with Bi-Level Beam Search for Zero-Shot Machine Translation
EBBS: An Ensemble with Bi-Level Beam Search for Zero-Shot Machine Translation
Y. Wen, B. Shayegh, C. Huang, Y. Cao, and L. Mou. Workshop at International Conference on Machine Learning (ICML), 2024
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 Machine Intelligence beyond Predictive ML.
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.