North Star Research

North Star
Research

Borealis AI conducts research in artificial intelligence for financial services. We are part of RBC, one of the largest banks in the world. At Borealis, we aspire to publish impactful research and build industry-leading products that serve our clients’ needs from the algorithms we create.

North Star core
scientific problems

As part of RBC, we have access to data, problems, and domain experts to drive our research agenda. We organize our research into North Star areas – guiding directions for our work and formulated as a challenging problem we are trying to solve.

Asynchronous Temporal Models (Atom)

With Asynchronous Temporal Models (ATOM), we build machine learning models capable of making inferences from partially-observed, multi-source, asynchronous temporal data. These are the types of data commonly found in banking applications — from various types of transaction data to client interactions with our banking services.

The goal of this North Star research direction is to develop novel machine learning technologies for asynchronous time series data, commonly found in banking and other relevant customer-centric domains. 

This line of research focuses on training machine learning models in the challenging data environment of partially-observed, multi-source, imbalanced, and asynchronous time series. 

Our research will help build technologies that can leverage the data from multiple channels, but also work for clients that use only a subset of these channels, in a way that respects privacy and upholds principles of Responsible AI.

Casual Machine Learning from observational data (Causmos)

Our machine learning products drive decision making. We picked Casual Machine Learning from observational data (Causmos) as one of our north star research areas, because we believe that ultimately, the world needs better models that can answer causal questions and generalize out of distribution.

This North Star research project focuses on developing machine learning models that can answer causal questions and generalize out of distribution (OOD). Such capabilities improve model trustworthiness and robustness in a dynamic business environment, and can advance AI adoption and development of products and applications in variety of industries, including finance.

As it is usually impossible for many large organizations to perform randomized experiments, we will emphasize causal machine learning from observational data. 

Established by Pearl’s Hierarchy, it is impossible to draw causal conclusions from observation data without causal assumptions. Hence this project is as much about the machine learning and causal inference methods as about the various causal assumptions one can make on tasks of interest.

These causal assumptions could be tied to specific problem domains (in our case in finance), or be valid across several different domains e.g. additive non-Gaussian noise assumption, for which we intend to rely on public open-domain datasets.

Non-cooperative learning in competing markets (Photon)

We build models for Capital Markets data, where particular challenges include low signal-to-noise ratio, structured prediction, and game theoretic impacts from decisions we make.

Globally, Capital Markets have gone through a paradigm shift towards complete automation through Artificial Intelligence, turning it into a highly competitive area at the intersection of statistical models from various branches of machine learning.

We believe that a principled understanding of the interactions between statistical models that operate in a common environment will soon be a key success factor for leaders in the field. The Photon north star research team plans to approach this challenge from two angles:

Atomistic: a symptom-based research stream focusing on novel solutions to challenges that are a direct consequence of a competing market: large data, high noise, non-stationary dynamics, and constrained environments

Holistic: a system-based research stream focusing on a meta-level framework for holistic properties of a competing market: local stability, asymptotic behaviour, perturbation theory, and adversarial robustness.

Research Topics

Data visualization Causality Graph representation learning Learning and generalization Natural language processing Optimization Quantitative finance Reinforcement learning Responsible AI Time series modelling Transformers Unsupervised learning

Research library

Our widely published research covers a broad range of topics including Reinforcement learning, Natural Language Processing and Time Series Modeling. Our research is made freely available to support the AI community.

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Partnerships

Our partnerships enable us to benefit from the latest in academia and transfer that knowledge into real-world impact.

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Careers

Join our team of researchers with backgrounds in computer vision, machine learning, and natural language processing, and with PhDs in computer science, physics, computational finance, mathematics and more. Bring your research and prototypes to life – and reimagine the future of finance.

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