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.
I am interested in geospatial science and its application for spatiotemporal analyses, policy, and decision-making. I have nearly fifteen years of experience in combining observations from multiple satellite and airborne sensors, vector data from secondary and primary sources, and models to monitor and understand spatiotemporal variation across a range of topics. My specific interests include geospatial analysis, image processing, location analysis, spatially distributed dynamic models, model inversion, radiative transfer modeling, signal processing, and nonlinear dynamics. In my research and consulting I have analyzed a variety of data including remotely sensed data such as from MODIS, Landsat, AVHRR, OCO-2, AIRS, AVIRIS, and SRTM; vector data such as from Census, road network, FLUXNET, SURFRED, OzFlux, Phenocam, and weather stations; and gridded and reanalysis data such as from CMIP5, NCEP, NOAA, and NASA. I often combine multiple languages including MATLAB, R, and Python, extensively use ArcGIS and QGIS, and occasionally also turn to Mathematica and ENVI/IDL. I have assimilated data across multiple spatial and temporal scales to bridge gaps in our understanding of small-scale processes and large-scale patterns. My domain knowledge includes remote sensing, mass and energy exchange between the land surface and the atmosphere, photosynthesis, carbon and water cycle, ecosystem ecology, and watershed analyses. I have set up and used instruments such as radiometer, spectrometer, gas exchange, and PAR sensors. A list of my publications is available at https://www.researchgate.net/profile/Manish_Verma21.
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 traveling 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.
Alex has more than 14 years of research experience in projects involving medical devices, surgical simulation, human factors testing, tele-operated robotics, Raman spectroscopy, and condition-based maintenance for military helicopters. He has worked for several academic institutions, an aerospace company, a start-up medical device company, and with NASA.