The overall objective of this project is to develop and test concrete, scalable ways to support artists through creative communities of practice. The project has two major components. First, the project entails in-depth qualitative and quantitative research to uncover the needs of artists (both known and latent) and to uncover grounded strategies to meet those needs.

ML/AI is widely used and deployed in many industries. Its deployment in Asset Management industry (and especially in Canadian pension fund sector) is significantly behind. Part of it is the fear of “black box” and what recommendation it gives. This sentiment is outdated as the recent advancements in ML/AI allow looking inside the “black box and thus focus on “white box” asset allocation recommendations.
Another reason is that asset management these days is the intersection of three disciplines: Financial Economics, Statistics, and Computer Science.

Most academic literature and practices in the real estate industry use traditional valuation models to predict house prices. While machine learning models have been used more heavily in the finance literature, it is less applied among real estate researchers. While traditional property valuation models rely on simple relationships between the price of a property and each property characteristic, machine learning models allow for complex relationships and can solve such relationships.

We seek to develop an Integrated and Distributed Software Platform for Text Mining, Semantic Web, Machine Learning. Students are invited to carry out applied research in optimizing existing Natural Language Processing (NLP) platforms such as Unstructured Information Management Architecture (UIMA) and other tools to be integrated for real-time big data processing. We apply this technology to High-Frequency, Real-Time Complex Event Processing (CEP), including in Finance, Healthcare, and Cybersecurity. The applications are not industrial grade but will serve as demo to recruit external partners.

Pension funds and insurance companies generally have prior commitments with fixed terms. As a result, these institutional investors might need to alter their investment strategies drastically with low interest rates, which will in turn affect the performances of their portfolios. In this research project, we plan to study the effect of interest rates on the strategies and performances of pension funds and insurance companies. We will achieve this using state-of-the-art econometric methodologies such as regression models with time varying coefficients and TOBIT models.