James has provided statistical expertise to a variety of projects, particularly in biomedical domains. While a post-doc at the UM Cancer Center, he extensively used advanced bioinformatic and statistical tools for large-scale data analyses. His research interests include structure learning for biological networks, especially from perturbation or time-series data, and enrichment analysis. Other areas of expertise include: regression modeling, graphical models, high performance computing, inter-rater reliability, and parameter estimation for dynamic systems.
Greg is a computational science applications specialist with a background in computational materials science. He has developed scientific software in C++ for modeling solid mechanics and phase field dynamics in materials, using the finite element method. He has experience with the computational libraries PETSc, Trilinos, deal.II, and PetIGA. His research has involved using machine learning techniques, including deep neural networks and the TensorFlow library, to learn free energy surfaces to predict material microstructure.