Research
My current research sits at the intersection of computer vision, multimodal learning, and controlled-environment agriculture. I focus on using observations that can be collected repeatedly without destroying the plant.
RGB-D plant phenotyping
TAHAF studies attention-based fusion of color and depth for estimating lettuce traits.
Temporal amodal leaf completion
Greenhouse Occlusion investigates how observations of the same leaf across days can inform recovery of regions hidden in the current frame. Identity correspondence, evaluation quality, and reliability estimation are central to this work.
Climate-informed representation learning
CLFM explores image and environmental information for learning useful representations with fewer trait labels.
Longer-term direction
I am interested in connecting perception and growth models to greenhouse digital twins and decision support.