Rainer Hilscher

Rainer Hilscher, PhD, is investigating the use of social media data for e-cigarette surveillance and policy research for a project funded by the National Institutes of Health.

At RTI, Dr. Hilscher has worked on projects to improve eldercare facilities and protect food from contamination. He was a senior developer on the USDA-funded NorOPTIMAL agent-based model of Norovirus disease dynamics and transmission in an eldercare facility and the lead modeler on the FDA-funded Quantitative Predictive Risk Assessment Model (QPRAM), which simulates pathogen contamination of lettuce and tomatoes from fork to farm.

Dr. Hilscher has extensive experience in applying computational modeling to domains ranging from behavioral prediction in military contexts to modeling issues in public health using methods from complexity science. His published PhD work involved computational modeling of biological speciation.

Dr. Hilscher serves as a senior technical leader for RTI Merge(TM). As part of this strategic initiative, he leads the implementation of knowledge graphs and high-performance simulation modeling leveraging the platform's AI and scalable computing resources.

Prior to joining RTI in 2013, Dr. Hilscher spent two years at the Center for Social Epidemiology & Population Health at the University of Michigan, working on disentangling the neighborhood effect from self-selection in the context of health disparities. Before that, Dr. Hilscher was a principal investigator at the Altarum Institute, where he initiated new complex adaptive system research projects in social epidemics-related domains. Earlier in his career, Dr. Hilscher worked as a senior systems engineer in the Emerging Markets Group at Vector Research Center. While there, he was involved in projects ranging from applying distributed decision making AI research to the group’s polyagent swarming technology to developing a multiagent collaborative knowledge generation system for the intelligence community.

Dr. Hilscher has extensive programming experience with Java, Python, and R for machine learning and database applications, textual analysis, and data visualization. He has also designed and implemented agent-based and micro-simulation models using RepastS and NetLogo.