Focus and publications
Research
2025
Optimizing Future Environmental Embedding Representations for Crop Performance
This work explores how to learn and optimize representations of future environmental conditions, such as climate and soil signals, so that machine learning models can more accurately predict crop performance. The goal is to design embeddings that generalize across regions and seasons, improving forecasting under shifting conditions and supporting data-driven agricultural decisions.
Upcoming
Research updates and publications
A short space for thesis chapters, preprints, datasets, code, and research notes as they become available.