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.
Josh has academic degrees in both Statistics and Computer Science, with deep experience in statistical methodology and use of statistical software. His areas of interest include causal inference, propensity score matching, multiple regression diagnosis, structural equation modeling, software implementation and general computing concerns.
Before coming to research/academia I worked for more than a decade with non-governmental organizations promoting better and inclusive management of natural resources in ecologically fragile areas. I also worked as a consultant and advised several organizations including Oxfam, KfW, Winrock, and Aga Khan Rural Support Program. I love wild animals and go through phases where my enchantment skips from birds to reptiles to mammals. I like reading, cooking, seeking connections between abstract ideas and realities, and travelling with my spouse (Preeti) and my son (Aman).
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.