Meet the Instructor
Dave DeBarr has more than 18 years of experience using machine learning to implement high-performance solutions for a variety of commercial and government applications. DeBarr is a principal applied researcher at Microsoft, currently focusing on authentication and account security. In his time at Microsoft, he has constructed models for user, device and network reputation; expression and activity recognition; sentiment classification; click-through rate estimation; keyword and URL recommendation for behavioral targeting segments; and sensitivity analysis for customer satisfaction. Previously, DeBarr worked as a principal computer scientist at MITRE, a federally funded research and development center. At MITRE, he worked on object detection and classification for unmanned aerial vehicles, assessment of treatment effect using doubly robust estimation, abusive tax avoidance transaction detection, and intrusion detection. He has a Ph.D. in computer science from George Mason University, where he introduced enhanced machine learning methods and representations to improve performance for computer security applications.