Research interests

My research centers on developing advanced machine learning techniques to solve urgent open challenges in biology and ecology. For example, I have developed biodiversity monitoring models that integrate Earth observation data (satellite imagery, aerial photography), citizen-science wildlife observations and geospatial datasets. In 2025 we published a perspective article detailing how machine learning can dramatically scale up protected area monitoring using these data sources.

I work with a wide range of machine learning techniques, ranging from Bayesian inference to LLMs. I enjoy writing code a lot, and still get excited when models learn to perform a task well. More specifically, I have a strong interest in AI methods that extract efficient features from high-dimensional data and simultaneously minimise data requirements, such as self-supervised, weakly supervised and unsupervised learning. Our brains are phenomenal in doing so – we take in a constant stream of sensory information and rarely get explicit feedback – and therefore continue to inspire me.

I’m also passionate about reproducible code, promoting open-access science (e.g., as an associate editor for ESE), and contributing to open-source software. If this interests you, I have written a Python tutorial on reproducible figure making for scientists.

Four challenges need to be overcome to deliver high-resolution biodiversity information, which can be addressed by advances in Machine Learning (ML).

Figure from Van der Plas et al., 2025, Ecol. Sol. Evid.