Tom Goode is a statistical analyst at the RAND Corporation who researches homeland and national security policies related to acquisition, counterterrorism, logistics and operations, intelligence, and cybersecurity. His work supports a variety of clients including the Office of the Secretary of Defense, the United States Navy, the United States Air Force, members of the Intelligence Community, the Department of Homeland Security and its component agencies, and the Department of Health and Human Services. Goode has experience applying a wide variety of statistical and machine learning methodology to address policy questions; these methods include various regression analyses, survival analysis, hierarchical and mixture models, clustering and classification methods, record linkage methods, genetic algorithms, forecasting and time series analysis with neural network integration, and natural language processing. He is also familiar with survey development and experimental methodology, approaches for missing or duplicate data, and statistical visualization. He is most comfortable using R but is also experienced with SAS, Python, and SQL on both Windows and Linux platforms.
Goode began contributing to research in the fields of prehospital medicine and emergency medical operations while earning a bachelor's degree in statistics and master's degree in statistical practice at Carnegie Mellon University. He continues to conduct and advise research at the intersection of statistics and emergency medicine in addition to volunteering as an emergency medical technician in the Pittsburgh area.
- Matching Supply and Demand: U.S. Air Force and Global Force Management
- Improving Partner Nation Support for Medical Operations in Contested Environments
- Analysis in Support of the Development of a Counter UAS Implementation Framework
- Immigration Data Integration Initiative Study
- Medicare Ambulance Services Special Analysis