About Me
Kevin Ligozio is the Chief AI Architect of the Asymmetric Operations Sector (AOS) of the Johns Hopkins University Applied Physics Laboratory (APL). AOS comprises approximately 1600 staff across three mission areas focused on advancing the nation’s ability to defeat the asymmetric threat, whether human-made or naturally occurring. Areas of focus include countering weapons of mass destruction, mission autonomy, cyber operations, biological threats, and terrorism. Mr. Ligozio oversees efforts to achieve these goals through the advancement and utilization of data science and artificial intelligence.
Education & Industry Experience
Previously, Mr. Ligozio served as the Technical Director and Assistant Group Supervisor of the Tactical Intelligence Systems Group within the AOS, conducting technical research, providing technical oversight, and technical staff management for multiple AI programs focused on researching, developing, and integrating AI technologies into DoD tactical-edge platforms.
Mr. Ligozio is also an adjunct professor in The Johns Hopkins University Whiting School of Engineering’s Computer Science Master’s Program. He received a B.A. in Mathematics and Computer Science from the State University of New York at Geneseo and an M.S. in Computer Science from the Rochester Institute of Technology.
Contact Kevin Ligozio at [email protected].
MY COURSES
Leading Data and AI-Enabled Organizations: For Executive Leaders (US Government)
This course provides executive government leaders with the tools to harness artificial intelligence (AI) for the benefit of their organizations using a holistic perspective on AI technology integration.
View CourseLeading Data and AI-Enabled Organizations: For Senior Leaders
This course provides senior leaders with a comprehensive approach to adopting AI technology. It provides a framework to unpack the question, “How do we turn our organization’s AI strategy into...
View CourseAssurance of AI Enabled Systems
This workshop introduces state-of-the-art methods for developing testing and evaluation plans for AI-driven systems and addresses novel challenges these systems present.
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