About me
I am a PhD candidate at EPFL, Switzerland, supervised by Devis Tuia. My research focuses on applying recent machine learning methods to ecology and conservation. In particular, I study how deep learning can support large-scale biodiversity monitoring by improving species distribution models. As part of this work, I developed MaskSDM and co-developed CISO, two new deep learning frameworks for species distribution modeling. More broadly, while I remain critical of certain applications of AI, I am enthusiastic about the role it can play in monitoring the Earth and helping preserve its ecological diversity. In this regard, I am one of the organizers of the AI+Environment Summit, a full-day event designed to inspire, ignite, and innovate work in AI for the environment.
Before starting my PhD, I completed a Master’s degree in Data Science and a Bachelor’s degree in Communication Systems at EPFL. During my studies, I worked with several research groups, including the Data Science Lab (DLAB) of Robert West on analyzing food consumption at EPFL and the Machine Learning and Optimization Lab (MLO) of Martin Jaggi on text-to-image generation. I also worked with the INDY Lab of Patrick Thiran, where I co-built the first version of Climpact, which led to a publication. In addition, I interned at Sony in Stuttgart, working on neural architecture search. I also served as a student assistant for several courses: see Teachings.
News
- 06 Jan 2026 — I will be presenting CISO at the TIBS conference in Aarhus 🇩🇰
- 10 Dec 2025 — Our CISO paper has been accepted at Methods in Ecology and Evolution 🎉
- 26 Nov 2025 — Our MaskSDM paper is now published in Methods in Ecology and Evolution 🎉
- 01 Oct 2025 — I helped organize the AI+Environment Summit in Zürich 🌿
Selected publications
MaskSDM with Shapley values to improve flexibility, robustness and explainability in species distribution modelling
Contact me
Feel free to reach out to me at robin.zbinden[hat]epfl.ch, or come say hi at conferences. I’m always happy to discuss research ideas, potential collaborations, or anything related to machine learning and ecology — and I also enjoy chatting about sports, literature, films, music, or politics.