Bio
Alberto Speranzon serves as the Chief Scientist for Autonomy at Lockheed Martin Advanced Technology Labs, where he drives research in neuro-symbolic AI for decision-making agents and runtime assurance for multi-agent systems. Previously, Alberto was a Technical Fellow at Honeywell Aerospace, Advanced Technology Group. He earned his "Laurea" degree in computer engineering from the University of Padova, Italy, in 2000, and a Ph.D. in automatic control from the Royal Institute of Technology (KTH), Stockholm, Sweden, in 2006. Alberto has been working, as principal investigator or co-investigator, on various DARPA and NASA projects developing technologies ranging from novel optimization methods based on homological algebra and sheaf theory to advanced navigation in GPS-degraded and denied environments leveraging algebraic topological methods. He received the Best Poster Award at the 2017 Hybrid Systems: Computation and Control (HSCC) conference for his work on abstraction and compositional methods using sheaf and category theory, a collaboration with MIT. From 2008-2015 he was at United Technologies Research Center (UTRC) where he held various roles of increasing responsibility. He managed and led several projects, focusing on autonomous systems and pioneering new mathematical approaches for swarm intelligence, integrating robust adaptive filtering, AI, graph theory, and algebraic topology. His significant contributions at UTRC were recognized with the Outstanding Achievement Award in 2009, the highest honor given by UTRC. Alberto is a Senior Member of IEEE and he serves as an associate editor for the Open Journal of Control Systems (OJ-CSYS). He was an associate editor IEEE Transactions on Control Systems Technology Journal, and a member of the IEEE Control System Society (CSS) Board of Governors in 2021. He regularly contributes to the Technical Program Committees of various conferences in neuro-symbolic AI, cyber-physical systems, robotics, and networked control systems.
