Why you should work in AI for biodiversity

by Laurens Hogeweg (Intel)

The natural world, including biodiversity, provides a challenging playing ground for developing machine learning techniques that scale and apply to the real world. This talk discusses machine learning research applied to biodiversity performed at Naturalis Biodiversity Center and Intel. It discusses how this research is applied in practice to support large scale automatic monitoring of biodiversity and help citizen scientists collect biodiversity data and become engaged with protecting nature. The talk ends with a discussion of future developments including the rapid development of large scale models and edge AI.

Laurens Hogeweg is a highly accomplished professional with a diverse background in machine learning and deep learning. He is currently associated with Intel Benelux BV and Naturalis Biodiversity Center. Laurens Hogeweg’s work at Intel Benelux BV is primarily focused on the development of out-of-distribution (OOD) detection methods for large-scale hierarchical classification. His work is particularly relevant to the species recognition task in images, which involves dealing with large databases, a large number of fine-grained hierarchical classes, severe class imbalance, and varying image quality.

In addition to his role at Intel, Laurens Hogeweg also contributes to the Naturalis Biodiversity Center. His work here involves the application of deep learning techniques for species recognition from images. He has been involved in research that investigates how the amount of training data influences the performance of species recognition models for various taxa.

Prior to his current roles, Laurens Hogeweg obtained a PhD in machine learning of medical images at Radboud University Nijmegen. After obtaining his PhD, he broadened his focus to include other areas of machine learning and deep learning. Laurens Hogeweg’s work is characterized by his ability to apply advanced machine learning techniques to practical problems. His contributions to the field of machine learning, particularly in the context of species recognition and hierarchical classification, are noteworthy.

Other keynotes at our Spring Meeting 2024:

• A (not so) deep look into image colourisation and shape segmentation by Prof. Dr. Jiří Kosinka (University of Groningen)
• Bayesian Deep Learning for Safe Computer Vision by Dr. Matias Valdenegro Toro (University of Groningen)