I am the Director of Undergraduate Studies (DUS) and a professor in the Department of Computer Science at Georgetown University. My research interests include machine learning, data mining, on-line learning algorithms, concept drift, and applications of machine learning and data mining to computer security. I led the effort that established Georgetown's first graduate programs in computer science and served as their first director. In 2004, I shared with Zico Kolter the award for the best application paper at KDD for our work on detecting malicious executables. In 2007, I shared with Greg Stephens and Kate Arndt a Program Innovation Award from the MITRE Corporation for our work on detecting insider threats. I have served as a consultant to industry, government, and nonprofit organizations.
I teach the department's courses on machine learning (COSC-288, COSC-575) and artificial intelligence (COSC-270, COSC-574). I have also taught the department's introductory courses, Computer Science I (COSC-051), Computer Science II (COSC-052), and Data Structures (COSC-160).
This semester I am teaching
Introduction to Machine Learning (COSC-288).
Office Hours: MTWR 3:30–4:30 PM ET via Zoom (or by appointment). Please send me e-mail for the Zoom link.
In Fall 2021, I am teaching
Data Structures (COSC-160).
NewsJuly 17, 2021: Micah Sherr and I taught Introduction to Python Programming for Computer Security as part of Georgetown's Summer College Immersion Program. This class is in support of Georgetown's Scholarship for Service Program that the National Science Foundation funds.
February 9, 2021: I was honored to have a conversation with President DeGioia for the Georgetown Now series about the progress the Department of Computer Science has made over the past twenty years, educating first-year CS students, aspects of machine learning, and the challenges of online instruction.
July 19, 2020: Clay Shields and I taught Introduction to Python Programming for Computer Security as part of Georgetown's Summer College Immersion Program. This class is in support of Georgetown's Scholarship for Service Program that the National Science Foundation funds.
July 19, 2019: Clay Shields and I taught Introduction to Python Programming for Computer Security as part of Georgetown's Summer College Immersion Program. This class is in support of Georgetown's Scholarship for Service Program that the National Science Foundation funds.
April 30, 2019: Georgetown receives a grant from the Mozilla Foundation to incorporate ethics into our computer science curriculum. My course on artificial intelligence is one of three included in the project.
August 24, 2018: Handout for my talk for Prelude '18.
October 19, 2015: Handout for my talk for Philosophy and Star Trek (PHIL-180).
September 30, 2015: Handout for my talk for Governing Emerging Technologies (CCTP-779).
October 20, 2014: Handout for my talk for Philosophy and Star Trek (PHIL-180).
September 15, 2013: For my part of the ITEL project "Improving Computer Science I" with Clay Shields, I created and uploaded a series of video lectures and screencasts to YouTube on object-oriented design and classes, C++ vectors, pointers, and self-referential classes. We also released the code developed in the videos.
February 26, 2013: Everyone should learn how to code. "...software is really about humanity...it's really about helping people..."
February 1, 2013: Quoted in Future Smart Devices Will Extend Our Senses, Voice of America.
December 9, 2010: Paper with Stephen Bach (now at UMD) on a Bayesian approach to concept drift appears at NIPS 2010.
March 2, 2010: Stephen Bach (C '10) is one of two undergraduates in the department selected as a Computing Research Association Outstanding Undergraduate Research Award Winners. Details...
December 8, 2009: Paper (with Deanna Caputo and Greg Stephens) on insider threat appears in IEEE Security & Privacy.
December 15, 2008: Stephen Bach (C '10) presents Paired learners for concept drift at ICDM in Pisa, Italy.
December 31, 2007: Journal of Machine Learning Research publishes Dynamic Weighted Majority: An ensemble method for drifting concepts (with Zico Kolter).
August 25, 2005: Springer publishes Machine Learning and Data Mining for Computer Security.
Copyright © 2019 Mark Maloof. All Rights Reserved. This material may not be published, broadcast, rewritten, or redistributed.