Uncovering Spatio-temporal Patterns in Ocean Data
Ocean research is undergoing a revolution, with new data streams coming online and constantly increasing in volume. But emerging research in data analytical methods needs to be tailored to the specific challenges of ocean data, including heterogeneous data types, spatial and temporal dependencies, and high-volume streaming data. As Canada Research Chair in Spatiotemporal Ocean Data Analytics, Dr. Luis Torgo aims to address these challenges, and eventually inform and support better policy and business decisions.
Torgo and his research team are studying and developing methods for handling spatial and temporal data dependencies and for incorporating expert domain knowledge into data-driven machine learning models. They are also working to find ways to detect anomalies in ocean ecosystems and methodologies for analyzing data from ship trajectories, marine animal populations (such as whales), and other ocean-related sources.
By collaborating with other experts as well as industry and government, Torgo and his team hope to maximize the impact of the research and create new opportunities for multidisciplinary training.
Torgo’s research program fills a knowledge gap and will uncover important spatio-temporal patterns in the ocean. It will also support everything from microbiology to fisheries to maritime transport safety in Canada. Given the potential applications of the results from ocean data analytics, this research could also support better decision-making and policy development, and enhance the ocean economy.