I apply my experience in knowledge graphs, ontology engineering, data science & machine learning to understand and transform data into meaningful insights. I hold a BASc and MASc in Industrial Engineering from the University of Toronto, with research in knowledge engineering, data science, & enterprise modelling.
These are some of the notable projects I've contributed to.
Presented at the 2022 Research Challenges in Information Science (RCIS) conference in Barcelona, Spain, this paper details a framework to model and automatically detect common errors in data science.
Forecasting Model for Air Canada Pilot Productivity. Utilizing machine learning & timeseries forecasting techniques, it resulted in a 40% improvement in forecasting accuracy.
With evolving and increasingly digital processes, enterprises are faced with large process repositories of complex process models. This paper introduces new ways for enterprises to develop and evaluate models to perform meaningful searches of their enterprise process repositories.
In this paper, I developed a framework for execution monitoring based on a case study in compliance issue remediation at a major Canadian bank.
"Meaningful Datatypes: Ontologically-Sound Dependent Type Systems for Data Science" combines the research domains of data science, ontology engineering, & functional programming to detail a framework to encode real-world rules as sophisticated types for use in data science.
On Medium, I write beginner-friendly articles on advanced computer science concepts.