Scott Freitas is a Principal Applied Scientist at Microsoft Security Research who combines a Machine Learning PhD from Georgia Tech with nine years of experience building graph-based, explainable ML systems for cybersecurity. He has led cross-team efforts to fuse research and product—shipping ML-driven platforms like TITAN and integrating M365D and Sentinel into Microsoft Defender XDR—while architecting LLM-based agents to close detection and disruption gaps. His work spans graph mining, deep learning, and adversarial robustness, and has produced practical incident-correlation and prioritization solutions that saved customers millions of investigation hours. Scott’s background includes fellowships and collaborative research at IBM, Amazon, Intel, and the Naval Air Warfare Center, reflecting deep industry-research partnerships. Notably, he brings hands-on experience turning theoretical graph representation learning into deployed defenses, blending rigorous academic training with large-scale production impact. Based in Phoenix, he focuses on creating explainable, efficient, and robust next-generation cybersecurity systems.
9 years of coding experience
9 years of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at Arizona State University
Doctor of Philosophy - PhD Machine Learning, Doctor of Philosophy - PhD Machine Learning at Georgia Institute of Technology
Contributions:8 commits, 7 pushes, 1 branch in 1 year 10 months
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Scott Freitas - Principal Applied Scientist at Microsoft