Dr. Christopher Henry

Biography

My academic career started with research into how the similarity of objects can be assessed within computing applications. Inspired by the human ability to quantify similarity based on the comparison of object features, I developed formal and systematic processes for quantifying the similarity of objects with a focus on practical image analysis problems and applications. This work was computationally complex and naturally led me to general-purpose computing using GPUs in the late 2000s. Building on this experience, I co-established the Applied Parallel Computing and Collaborative Research Laboratory. My background in quantifying similarity, which is closely related to pattern classification, and GPGPU meant I was well-positioned to explore machine learning as the deep learning revolution unfolded in the early to mid-2010s. Since then, I have been involved in the theory and application of machine learning, focusing on applications in digital agriculture and remote sensing. This led me to co-create and co-lead the TerraByte research group, concentrating on automated methods, such as embedded and robotic systems, to create labeled datasets and the development of machine learning models to solve agricultural problems. My research interests include machine learning, deep learning, domain adaptation, generative image generation, digital agriculture, remote sensing, GPGPU, and image analysis.

Research Interests