https://www.researchgate.net/profile/Baba-Camara
Title: Deep learning of the connection graph driving the successful spread of invasive species
Abstract: This two-week research project investigates how deep learning can be used to infer and analyze the underlying connection graph that facilitates the successful spread of invasive species across spatial and ecological networks. Participants will explore how environmental, and biological factors contribute to species dispersal, and how these can be modeled as a graph where nodes represent habitats or regions and edges capture potential pathways of spread. Using real or simulated data, the team will implement graph-based deep learning models, such as Graph Neural Networks, to learn the structure and weights of these connections. The project aims to identify key transmission pathways, predict future spread patterns, and assess the influence of graph topology on invasion dynamics. Applications may span invasive plants, aquatic species, or agricultural pests.
Prerequisites:
· Basic understanding of machine learning and neural networks,
· Exposure to graph theory and network analysis concepts,
· Introductory knowledge of ecology or environmental modeling (helpful but not required),
· Familiarity with deep learning frameworks and programming proficiency.
Group members
Harshit BAJPAI
Fajri MAULANA
Bouasy DOUNGSAVANH
Jhunas Paul VIERNES
Rachelle Anne GUANGA
Moe MOE OO