His research interests include artificial intelligence and machine learning. He teaches courses on these topics at the undergraduate and graduate levels. Dr. Maloof earned his doctorate from George Mason University under the direction of Ryszard Michalski. His dissertation proposed algorithms for learning in changing environments. Prior to coming to Georgetown in 1998, he worked as a post-doctoral researcher with Pat Langley and Tom Binford on developing methods of machine learning for object detection in images.
Dr. Maloof's research contributions include the development of on-line algorithms for learning in non-stationary environments, a setting in which the concept the learner must acquire changes over time. These changes could be due to changes in preference, such as in the case of calendar scheduling, or due to an adversary, such as in the case of junk-mail filtering. He has made contributions to the application of machine learning to problems in computer security. With collaborators, he produced patented methods for detecting the theft of trade secrets and for proactive forensic analysis of documents. He also developed a method for detecting previously unseen malicious executables, technology that Georgetown patented and then licensed to a company that incorporated it into a commercial network appliance.