Michiel Kallenberg

Michiel Kallenberg is a Postdoctoral Researcher in Artificial Intelligence, focusing on applications in agricultural management and medical imaging. His work bridges reinforcement learning, machine learning, and domain adaptation with practical applications in crop yield prediction, nitrogen optimization, and breast cancer risk assessment.

• Research Areas: AI for agriculture, medical imaging, reinforcement learning
• Collaborations: Involved in interdisciplinary projects like 'Artificial Intelligence for reducing fertilizer and pesticide use' and 'Digital Future Farm'

Recent publications highlight his expertise in constrained reinforcement learning for precision agriculture and volumetric breast density estimation using deep learning. His research trends show a dual focus on environmental sustainability and healthcare diagnostics.

He co-supervises PhD candidates and collaborates with institutions on datasets like CY-Bench, which provides benchmarking for subnational crop yield forecasting. His projects emphasize integrating data-driven approaches with process-based models.