Modeling and Analytics on Predictive Systems

At MAPS Lab, we explore the frontiers of spatiotemporal data analytics and modeling. We perform at the intersection of machine learning, network science, and applied research, focusing on predictive modeling for real-world applications, such as trajectory problems, network science, probabilistic modeling, spatial analytics, and the intertwining of those topics. We specialize in developing cutting-edge models, algorithms, and methodologies to address univariate, multivariate, and panel data challenges. We aim to deliver innovative solutions that drive advancements in transportation science, network science, computational physics, physics-informed machine learning, and computer vision.

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. Note: Image from David Lasker.

Recent Publications

Gabriel Spadon, Jay Kumar, Jinkun Chen, Matthew Smith, Casey Hilliard, Sarah Vela, Romina Gehrmann, Claudio DiBacco, Stan Matwin, Ronald Pelot (2024) Maritime Tracking Data Analysis and Integration with AISdb SoftwareX

M. Alam, Gabriel Spadon, Mohammad Etemad, Luis Torgo, E. Milios (2024) Enhancing short-term vessel trajectory prediction with clustering for heterogeneous and multi-modal movement patterns Ocean Engineering

Ruixin Song, Gabriel Spadon, Ronald Pelot, Stan Matwin, Amilcar Soares (2024) Enhancing global maritime traffic network forecasting with gravity-inspired deep learning models Scientific Reports

Recent News