<p>Vivien is a PostDoc at the EcoVision Lab, Department of Mathematical Modeling and Machine Learning (DM3L) at the University of Zurich since 2022. He received his PhD in Geospatial Computer Vision in January 2022 at the French mapping agency (IGN, Gustave Eiffel University). His thesis explored novel deep learning architectures for crop type mapping from satellite image time series. Among other aspects, it focused on leveraging the temporal dimension of such data with attention mechanisms. The thesis also proposed a novel method to leverage the hierarchical structure of the crop type taxonomy, and showed how to address crop type mapping as a panoptic segmentation task. For his PostDoc, he is involved in the development of machine learning methods for forest monitoring, and other ecological applications. More broadly, Vivien is interested in methods that leverage remote sensing data to improve our understanding of the Earth&rsquo;s evolution.</p>

<p>Damien joined the EcoVision Lab, Department of Mathematical Modeling and Machine Learning (DM3L) as a PostDoctoral researcher in February 2024. His work at UZH deals with leveraging remote sensing data for forest structure analysis and species distribution modeling. He is also involved in projects for efficient 3D point cloud and 2D image analysis. Damien received his PhD in 3D Deep Learning in January 2024 from Gustave Eiffel University, where he worked at IGN (French mapping agency) and ENGIE lab CRIGEN. During this thesis, his research focused on efficient learning on large-scale 3D point clouds, covering learning from large point clouds and abritrarily-posed images and superpoint-based learning for scalable 3D semantic and panoptic segmentation. Prior to his PhD, Damien spent 2 years in industry, working on 3D and 2D computer vision.</p>

<p>Lukas joined the EcoVision Lab, Department of Mathematical Modeling and Machine Learning (DM3L) in June 2024 after working on his PhD in the remote sensing group at the University of Bonn, focusing on data science for crop systems. There, he also studied at the Institute for Geodesy and Geoinformation. In recent years, he was part of the PhenoRob cluster of excellence, researching machine learning solutions for sustainable agriculture. His emphasis was on image analysis and generating drone images of crop mixtures. In his doctoral thesis entitled &ldquo;Data-driven Image Generation for Crop Growth Modeling&rdquo;, he used generative neural networks to create reasonable and applicable images of future growth stages based on images of early growth stages and growth-influencing variables. For his postdoc, he will keep the focus on UAV-based plant data when it comes to detecting and segmenting invasive species in Switzerland. Methodologically, he will mainly deal with data imbalances, modeling uncertainties, and integrating multi-modalities and prior knowledge into neural networks.</p>

<p>Johannes joined the EcoVision Lab, Department of Mathematical Modeling and Machine Learning (DM3L) as a PhD student in 2023. He received his MSc from ETH Zurich in the Computer Science - Machine Intelligence track, and his BSc in Computer Science plus Computational Linguistics from the University of Munich (LMU). During his master thesis, he worked on a biologically-inspired alternative to backpropagation. His main research interests are applying advancements in deep learning to applications in ecology and specifically biodiversity. Johannes' current research is focused on modeling the distribution of species using satellite imagery. In times of drastic climate change he believes that university research can have a significant impact on solving enviromental problems. Tangentially to his main research, he is interested in advancing the state of the art of deep learning, and its societal and political impact.</p>

<p>Emilia is a PhD student at the EcoVision Lab, Department of Mathematical Modeling and Machine Learning (DM3L) at the University of Zurich since 2024. She received her Bachelor's degree in Cognitive Science from the University of Osnabr&uuml;ck and her Master's degree in Data Science with an interdisciplinary minor in Climate Science from ETH Zurich. During her master's thesis, she worked with the EcoVision lab on automatic damage detection in armed conflict areas where high-resolution data is scarce. Her research now evolves around the use of remote sensing data to address and investigate environmental problems such as biodiversity loss and the spread of invasive species.</p>

<p>Marc is a PhD student at the EcoVision Lab, Department of Mathematical Modeling and Machine Learning (DM3L) at the University of Zurich since 2021. He received his Master&rsquo;s degree in Robotics, Cognition, Intelligence from the Technical University of Munich. His research interests are computer vision and machine learning. Currently he is working on tree cell instance segmentation using microscopic images.</p>

<p>Yuchang Jiang is a PhD student at the EcoVision Lab, Department of Mathematical Modeling and Machine Learning (DM3L) at the University of Zurich since 2021. She received the M.Sc degree in Geomatics from ETH Zurich in 2021 and obtained a B.Sc degree in Geomatics from The Hong Kong Polytechnic University in 2019. During her master thesis, she worked on tracking small fast-​moving objects in challenging environments across multiple cameras. During her PhD, she is working on global vegetation parameter assessments with satellite images. Her research interests lie in computer vision, deep learning, and remote sensing.</p>

<p>Thomas joined the EcoVision Lab, Department of Mathematical Modeling and Machine Learning (DM3L) as a PhD student in May 2025. He received his BSc in Mathematics and Economics from Ecole Polytechnique and his MSc in Data Science from ETH Zurich. After working on remote monitoring of armed conflicts using remote sensing data during his Master Thesis at the EcoVision Lab, his research now focuses on mapping conflict damage using open-access SAR imagery, change detection and computer vision.</p>