Prior to joining UC Davis, Mason worked as a Data Science Engineer at Apple where he created new deep learning and computer vision tools within hardware engineering. During his post-doctoral and doctoral work, he advanced new computational techniques for 3D image processing, quantification, and coupled biophysical modeling of plants. By combining these computational techniques with custom physiological and ecological instrumentation, he provided insight into organismal CO2 diffusion and photosynthesis, H2O transport, and carbohydrate metabolism for species across the plant kingdom. As a faculty member in the Departments of Viticulture & Enology and Biological & Agricultural Engineering, Mason will build on this industry and academic experience to lead the Plant AI and Biophysics Lab.

The Plant AI and Biophysics Lab aims to develop low-cost AI systems that generate novel insight into plant biology, ultimately leading to more precise and sustainable agricultural practice. A major emphasis in the lab is to build agricultural sensing and automation systems powered by deep learning algorithms that can precisely monitor and predict plant biophysical status, such as stress, productivity, and yield. At the same time, we want to push our AI systems beyond black-box prediction and toward interpretability of biological mechanism. Thus, we also investigate basic plant biophysical mechanisms from molecular- to organismal-levels to understand scalability and impact on agricultural systems.