Bio
John is a R&D Associate Staff Member in the Biosciences Division at 91°µÍø.
His research focuses on the development and application of mathematical, computational, and statistical methods to biological datasets to yield new insights into complex dynamical systems.
Approaches include scientific machine and deep learning (neural networks, explainable-AI, computer vision, etc.), graph and network theory (unsupervised clustering, geometric deep learning, etc.), and mathematical modeling (ODEs, PDEs, nonlinear optimization, etc.) to address questions across:
- Biological scales ranging from microbes (bacteria and fungi) to organisms (plants and humans) to global ecosystems (communities and environments).
- Data modalities ranging from structured (tabular and imaging) to unstructured (point clouds and graphs) to sequential (time series and video).
- Computing systems ranging from local (Linux, MacOS, Windows) to intermediate (NVIDIA DGX) to leadership-class systems (Frontier).
John leads multiple projects resulting in a number of accomplishments (e.g., the fastest scientific computation in human history and reaching human-level segmentation accuracy using fewer than 10 training images) and funding opportunities (e.g., a Laboratory Directed Research and Development award).
He actively mentors interns and graduate students and collaborates on multidisciplinary, multi-institutional projects across a wide network of collaborators from around the world.
John earned his MS and PhD in Applied Mathematics from North Carolina State University and a BS in Computational and Applied Mathematics from East Tennessee State University.
Awards
- People's Choice Award, ORPA Research Symposium, ORNL, 2023
- LDRD, AI-enabled association of plant physiology and phenotypes, ORNL, 2022-2023
Professional Service
- Code release: Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa
- Data release: Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa
- Data release: Climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics