Devin Cortese is a Data Scientist at Carnegie Mellon University’s Software Engineering Institute with 6+ years’ experience where he develops AI and machine learning solutions for cybersecurity challenges. His work includes research on large language models and cyber physical sensing problems, including automated sUAS detections. He has a strong interest in computer vision and deep learning, particularly in using generative models for real-world applications. He has worked on an anomaly detection system using Variational Autoencoders (VAEs) to monitor poultry farm environments, leveraging audio spectrogram analysis to identify unusual behaviors and potential system failures. Previously, as a Machine Learning Engineer at Booz Allen Hamilton, he developed AI-driven solutions for ADVANA, a Department of Defense (DoD) big data platform, and OSD CAPE, focusing on MLOps, automation, and secure AI deployments. Prior, he worked as a Data Scientist at Dick’s Sporting Goods, where he applied predictive modeling and data mining techniques to improve employee retention, engagement, and business decision-making. Devin is passionate about applying AI to practical problems, from cybersecurity to computer vision and audio analysis.