Dr. Craig Lawrence is interim executive director of ARLIS. Previously, he served as mission area director and research scientist. He is also a Visiting Research Scientist with the Institute for Systems Research at the University of Maryland at College Park. Prior to the University of Maryland, Dr. Lawrence was a Program Manager for the Strategic Technology Office (6/2013 - 4/2019). At DARPA, Dr. Lawrence created and managed the Battle Management Command and Control (BMC2) portfolio of programs, where he was responsible for five major DARPA programs, including the creation of a family of four unique programs (plus multiple studies, SBIR projects, and a young faculty award) valued at over $180M addressing critical BMC2 technology gaps within the services and to provide key enablers for the DARPA/STO system of systems (later “Mosaic Warfare”) vision. Dr. Lawrence was awarded the DARPA Meritorious Public Service Medal for his service (2019).
From 1999 to 2013, Dr. Lawrence was in industry, culminating with the position of Technical Director in the Technology Solutions division (now Fast Labs) at BAE Systems where he managed a group focused on defense and intelligence R&D. Highlights include leading the DARPA Advanced ISR Management (AIM) program, developing the Multi-Asset Synchronizer, an ISR planning tool for which he was recognized with the BAE Systems Bronze Chairman’s Award in 2005, and later the BAE Systems Technology Transition of the Year award for successful transition of MAS. Dr. Lawrence ran the DARPA Conflict Modeling, Planning, and Outcome Experimentation (COMPOEX) program developing a modeling and simulation framework, planning tools, and modeling technologies in support of country-level strategic planning (recognized with the BAE Systems Bronze Chairman’s Award in 2008). Dr. Lawrence also led the DARPA Behavioral Learning and Adaptive Electronic Warfare (BLADE) program (recipient of the Technology Solutions Best Collaboration of the Year award for the proposal effort) applying machine learning technology to learn behaviors of complex unknown RF threats in real-time and developing control-based technologies to construct surgical jamming strategies.