Nathaniel Bastian

Nathaniel D. Bastian, Ph.D. is a leader, practitioner, researcher, and educator of mathematical, computational, analytical, data-driven, and decision-centric methods to support the improvement and enhancement of decision-making in cyber security, national defense, military operations, human resources and manpower, healthcare, logistics, energy and finance.

He is a decision analytics professional with expertise in the scientific discovery and translation of actionable insights into effective decisions using algorithms, techniques, tools and technologies from operations research, data science, artificial intelligence, systems engineering, and economics to research, design, develop, and deploy intelligent decision-support models, tools and systems for descriptive, predictive and prescriptive analytics. He has authored over 50 refereed journal and conference papers, several book chapters, and one textbook. He is the recipient of numerous academic awards, honors and grants, to include a Fulbright Scholarship and National Science Foundation Graduate Research Fellowship. He serves as an Associate Editor for five journals, as well as Referee for over 20 journals. He is an active member of MORS, INFORMS, ACM, IEEE, SIAM, AAAI and AAAS.

He currently serves on the Executive Council of the Military Operations Research Society (MORS), the Council of the Military and Security Society within the Institute for Operations Research and the Management Sciences (INFORMS), the Information Sciences and Technology Study Group at the Defense Advanced Research Projects Agency (DARPA), and other professional service activities. He also serves as an Affiliated Researcher at several institutions, as well as an Adjunct Professor teaching graduate courses at several universities. He previously served as a Visiting Research Fellow at the Johns Hopkins University Applied Physics Laboratory, as well as a Distinguished Visiting Professor at the National Security Agency.

Dr. Bastian is actively researching in the areas of generative machine learning for cyber, data science and AI for cyber network security decision-support, AI system assurance (security, robustness, resiliency, explainability, safety), and AI-enabled command and control for multi-domain operations.