*Abstract* Estimating the geographical range of a species from sparse observations is a challenging and important geospatial prediction problem. Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. This problem has a long history in ecology, but traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets which can include tens of millions of observations of hundreds of thousands of species in addition to the availability of multi-modal data sources such as paired images and natural language descriptions. In this talk, I will present recent work from my group where we have developed deep learning-based solutions for estimating species' ranges from sparse presence-only data. I will also discuss some of the open challenges that exist in this space. *Bio* Oisin Mac Aodha is a Reader in Machine Learning in the School of Informatics at the University of Edinburgh. He is also an ELLIS Scholar and former Turing Fellow. He obtained his PhD from University College London and was a postdoc at Caltech prior to his current role. His current research interests are in the areas of self-supervised learning, 3D vision, fine-grained learning, and human-in-the-loop learning. In addition, he works on questions related to AI for conservation and biodiversity monitoring.
