Developed a clinical trial demand simulator to replace expensive 3rd party software, reducing costs & enhancing user experience and analytic capabilities; currently being, estimated cost savings > $200K/yr.Developed a global drug trial short-term demand forecast tool, using dynamic model scoring and selection with Python and R, to enable Global Clinical Supply team to determine drug kit supply distribution.Currently developing a NoSQL/Python solution for integrating diverse structured data sets to answer fundamental historical questions about clinical drug development in a statistically rigorous way.

Providing population health, data integrity and operational insights via statistical analysis for the Support and Services at Home (SASH) program. Increased staff workflow efficiency by automating certain tasks. Boosted conformance to SASH program standards via performance feedback reports.Federal and state grant administration.

Environmental modeling, research and development.Applied and improved SLAMM, a statistical environmental model that simulates the dominant processes involved in wetland conversions and shoreline modifications during long-term sea level rise. Map distributions of wetlands are predicted under conditions of accelerated sea level rise, and results are summarized in tabular and graphical form. Played a key role in development of SLAMM result visualization tools, including the OpenGL-based bitmap-to-3D viewer as well as the uncertainty analysis output viewer.Researched and applied SLAMM for various clients, including National Wildlife Federation, U.S. Fish and Wildlife Service, The Nature Conservancy, and Ducks Unlimited.