Mark Maloof

Mark Maloof is a Professor in the Department of Computer Science at Georgetown University, where he previously served as Director of Undergraduate Studies and pioneered the institution's graduate computer science programs.

His research focuses on machine learning, data mining, online learning algorithms, and concept drift with applications in computer security. Core innovations include Dynamic Weighted Majority (an ensemble method for drifting concepts), Bayesian approaches to concept drift, and systems for detecting malicious executables and insider threats.

Significant contributions include developing paired learner architectures for concept drift, additive expert ensembles, and machine learning systems for rooftop detection in aerial imagery. Security applications feature prominently, including novel methods for malware detection and systems for identifying trade secret violations.