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, Ruixin Song, Vaishnav Vaidheeswaran, Md Mahbub Alam, Floris Goerlandt, Ronald Pelot (2025) Learning Spatio-Temporal Vessel Behavior using AIS Trajectory Data and Markovian Models in the Gulf of St. Lawrence arXiv preprint arXiv:2506.00025 [stat.AP]

Md Mahbub Alam, Amilcar Soares, José F. Rodrigues-Jr, Gabriel Spadon (2025) Physics-Informed Neural Networks for Vessel Trajectory Prediction: Learning Time-Discretized Kinematic Dynamics via Finite Differences arXiv preprint arXiv:2506.12029 [cs.LG]

Joao P. Silva, Erikson J. de Aguiar, Gabriel Spadon, Agma J. M. Traina, Jose F. Rodrigues-Jr (2025) AI-Driven Public Health Surveillance: Analyzing Vulnerable Areas in Brazil Using Remote Sensing and Socioeconomic Data 38th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

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