Scrawl is engaged in significant research and development projects in collaboration with Mitacs and our research partner institutions to advance innovative research for both critical Scrawlr capabilities and broad application.
We are utilizing the latest machine-learning technologies as a starting point for text extraction and object identification within images, with an emphasis on enhanced performance and accuracy.
Leveraging machine-learning technology for in-memory database performance optimization. We are creating new data management strategies to facilitate loading and off-loading data to meet expected demand at scale.
Utilizing lexicographic and sentiment analysis, we are developing methods to perform text-based sentiment comparison on large-scale datasets while achieving performance objectives. Our research focus is on a combination of search-space reduction, CPU and GPU core optimization, and improved vertical scaling.
We are focused on the highly accurate identification, classification, and severity assessment of different forms of toxic speech, ranging from threats to insults to other broad components across languages. We are utilizing leading-edge machine-learning models to facilitate this multilingual identification, classification, and assessment system.
Scrawlr is engaging in leading-edge research development projects with leading Canadian research and education insitutitions to rapidly advance key components of our innovative feature set, in particular as it relates to algorithmic assessment of content, and methods of optimization for distributed communication. Through our work with these leading research institutions, we intend to deliver advanced, globally-competitive platform functionality for a broad range of industries, verticals, products, and user use-cases.
Master of Science, Computer Science
Master of Applied Science, Electrical and Computer Engineering
PhD, Electrical and Computer Engineering
Interested in a partnership?