William Solow

OHCAM outperforms traditional methods by efficiently learning complex action models from limited data, even in the presence of noise.

Cold hardiness predictions can now be accurately made in new regions with minimal data, thanks to a framework that learns transferable latent representations.

Achieve 60% better phenology and 40% better cold hardiness prediction by using a neural network to dynamically parameterize biophysical models.