Gaspar Modelo-howard is a Principal Machine Learning Engineer based in San Francisco with 11 years of experience building AI-driven security solutions for enterprise and cloud environments. He has led teams at Palo Alto Networks and Symantec to design anomaly detection, UEBA and network-traffic analytics informed by MITRE ATT&CK, and recently architected a generative-AI security assistant that translates natural-language queries into cloud security queries. His background spans applied research and productization—from PhD-level work in dependable distributed systems to deploying malware and intrusion detection models in commercial products. A former adjunct professor and long-time security practitioner in critical infrastructure projects, he blends deep academic rigor with hands-on experience in incident response, vulnerability assessment, and secure system architecture. He is notable for bridging threat intelligence, ML, and cloud security at scale, and for publishing research visible on Google Scholar that underpins his applied work. Gaspar’s career reflects a sustained focus on turning cutting-edge research into operational detection and response capabilities for large organizations.
11 years of coding experience
19 years of employment as a software developer
M.Sc. Information Security, M.Sc. Information Security at Royal Holloway, University of London
Doctor of Philosophy (Ph.D.) Computer Engineering, Doctor of Philosophy (Ph.D.) Computer Engineering at Purdue University
B.Sc. Electrical-Electronic Engineering, B.Sc. Electrical-Electronic Engineering at Universidad Tecnológica de Panamá
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Gaspar Modelo-howard - Principal Machine Learning Engineer at Cisco