Modeling and Analytics on Predictive Systems

At MAPS Lab, we advance the frontier of modeling and analytics in predictive systems, developing state-of-the-art computational methods for spatiotemporal forecasting, geoinformatics, and network-based analytics. We leverage machine learning, spatial data engineering, and graph-oriented methodologies to tackle complex, real-world problems, including mobility prediction, trajectory analysis, routing optimization, and geospatial decision support. Through this set of expertise, MAPS Lab delivers impactful, innovative solutions that address scientific challenges in environmental monitoring, urban intelligence, and multimodal predictive analytics.

About the Faculty

The Faculty of Computer Science - FCS at Dalhousie University - DAL was established in 1997 and is the leading information technology research and education institution in Atlantic Canada. Its research portfolio includes Big Data Analytics, Artificial Intelligence, Human-Computer Interaction, Visualization and Computer Graphics, Computer Systems, Algorithms & Bioinformatics, Networking, Security, and Computer Science Education. With around 70 faculty members, the FCS collaborates with national and international partners in sectors like oceans, defense, agriculture, and healthcare to tackle real-world challenges and promote innovation in computer science and its related fields.
Image from David Lasker.

Recent Publications

Gabriel Spadon, Oladapo Oyebode, Camilo M Botero, Tushar Sharma, Floris Goerlandt, Ronald Pelot (2025) Community-Centered Spatial Intelligence for Climate Adaptation at Nova Scotia's Eastern Shore arXiv preprint arXiv:2509.01845

Vaishnav Vaidheeswaran, Dilith Jayakody, Samruddhi Mulay, Anand Lo, Md Mahbub Alam, Gabriel Spadon (2025) Goal-Conditioned Reinforcement Learning for Data-Driven Maritime Navigation arXiv preprint arXiv:2509.01838

Md Mahbub Alam, Jose F Rodrigues-Jr, Gabriel Spadon (2025) Multi-vessel Interaction-Aware Trajectory Prediction and Collision Risk Assessment arXiv preprint arXiv:2509.01836

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