Vassilis Sitokonstantinou
AI for Earth and Environmental Sciences
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Assistant Professor at Wageningen University & Research (WUR) - Artificial Intelligence Group (AIN)
eMail: firstname.lastname@wur.nl
I am an Assistant Professor at the Artificial Intelligence Chair Group of Wageningen University & Research. My research focuses on advancing AI for scientific discovery in Earth and Environmental Sciences, with an emphasis on sustainable agriculture and food security. I work mainly with causal and interpretable machine learning, while also exploring deep learning approaches to understand and predict complex interactions within agroecosystems. See Research for more.
Before joining WUR, I was a postdoctoral researcher with Gustau Camps-Valls at the Image and Signal Processing Group (ISP) of the University of Valencia. Earlier, I led AgriHUB at the National Observatory of Athens, focusing on ML4EO applications for climate-smart farming and monitoring the EU Common Agricultural Policy (CAP).
I hold a PhD from the National Technical University of Athens (NTUA), where my research on Big Earth Data and machine learning for sustainable agriculture laid the foundation for much of my current work. I also hold MSc degrees from the National Observatory of Athens (Remote Sensing) and University College London (UCL) (Wireless Communications) and a BEng in Electrical and Electronic Engineering from Imperial College London.
Keywords: artificial intelligence, causal inference, remote sensing, Earth system science, sustainable agriculture, food security
News
News
10/2025 - Move to the Netherlands to work as an Assistant Professor at Wageningen University & Research
08/2025 - SOTA Showcase of FDL ESL challenge on Foundation Models for Extreme Environments
06/2025 - Appointed Faculty for the Frontier Development Lab ESL challenge on Foundation Models for Extreme Environments
04/2025 -New paper: Leveraging Causality and Explainability in Digital Agriculture in Environmental Data Science
01/2025 -New paper: Large Language Models for Causal Hypothesis Generation in Science in Machine Learning: Science and Technology
12/2024 - New paper: Cloud gap-filling with deep learning for improved grassland monitoring in Computers and Electronics in Agriculture
08/2024 - Preprint of my perspectives on Causal machine learning for sustainable agroecosystems
07/2024 - Attended IEEE IGARSS2024. The two papers presented are: 1. Transfer learning for global crop maps, 2. Causal ML for assessing humanitarian aid on food security. I co-organized and co-chaired the session on Causality and Machine Learning for Sustainable Agriculture and Food Security.
06/2024 - New paper: Causal discovery reveals complex patterns of drought-induced displacement in iScience
05/2024 - Attended ESA's EO4Agri2024. The three works presented are: 1. ML for national yield forecasting, 2. Personalizing practices w/ causal ML, 3. Causal ML for food security
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