Research Program Overview
Big Data and Scalability
We explore efficient methods to process and analyze vast environmental datasets. Our focus includes integrating diverse data sources, from climate to genomic, into scalable models for predicting environmental changes. We address challenges like time-based data patterns and anomalies, emphasizing consistency and ease of use in our tools, such as LifeSWS, which combines model management with distributed computing frameworks.
Machine Learning with Human in the Loop
In this research axis, we want to integrate human feedback into machine learning processes to improve model performance. This includes optimizing citizen science data, notably from platforms like Pl@ntNet, in species distribution models, addressing data bias, and refining AI uncertainty management for tasks like biodiversity mapping. We prioritize models that adapt based on real-world interactions and ensure prediction reliability in ecological applications.
Multiscale & Multimodal Data Analytics
Our goal is to leverage diverse environmental data at multiple scales and from various sources to develop innovative analytics techniques. Our application domains spans from biodiversity monitoring to climate modeling and resource management, calling for advances in multivariate time series analysis and efficient data integration strategies. These methods transform complex data into meaningful insights across a wide range of scientific and environmental domains.
