Research interests
My areas of research interest are machine learning and data mining, more specifically, adversarial learning, compression-based classification, online learning, multiple instance learning, semi-supervised learning, and graph-based clustering. In the past three years, my primary focus has been on developing advanced machine learning solutions to combating e-mail spam and adversarial attacks on existing spam filters.
Professional Activities
- Program committee member for:
- Conference on Artificial Intelligence 2007 (AAAI)
- International Conference on the Principles and Practice of Programming in Java 2007 (PPPJ)
- Pacific-Asia Conference on Knowledge Discovery and Data Mining 2007-2008
(PAKDD)
- Reviewer for:
- Pattern Recognition
- Knowledge and Information Systems
- IEEE Transactions on Knowledge and Data Engineering
- International Conference on Machine Learning
- Annual Conference on Computational Learning Theory
- Americas Conference on Information Systems
Recent Publications
-
Smartacking: Improving TCP Performance from the Receiving End, in Journal of Interent Engineering, 2007. (with S. Gorinsky, D. Blandford, S. Goldman, and D. Dooly)
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Adaptive E-Mail Spam Filtering Using Dynamic Feature Spaces (invited paper), to appear in Special Issue of International Journal of Artificial Intelligence Tools, 2007 (with M.S. Mulekar and P. Nerellapalli)
-
Democratic Co-Learning, in the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004), (with Sally Goldman).
-
Protecting Privacy in Person Specific Data Using Decision Trees,
in the workshop on Privacy and Security Issues in Data Mining of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (with Tom Johnsten and A. R. Kunduru).
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Neural Network Control for A Fire-Fighting Robot, with Dawn Wilkins and Robert P. Cook, Software -- Concepts and Tools 19(3), pp. 146-152, 1999.
Machine Learning & Data Mining Resources
WEKA
Knowledge Discovery Keys
KDD Research.org
MLC++ - Machine Learning Library in C++
UCI Machine Learning Repository
Online Machine Learning Resources
Machine Learning Resources
(David Aha)
Machine Learning and Case-Based Reasoning
(people in ML and CBR)
Machine Learning Index
Machine Learning Slides
Machine Learning in Practice
Java
Programs for Machine Learning
Data Engineering for Inductive Learning
Tom Mitchell's ML Lecture
Machine Learning
in Purdue
Machine Learning in MIT
Papers and Talks by
Wray Buntine
Machine Learning Tools
DIAMOND and Ice: Visual
Exploratory Data Analysis Tools
Artificial Intelligence
AI
Software for Linux
JPL Robotics
Robotics Resources
AI Subjects
Informative AI
Journal of AI Research
Networking Tools
Network Simulator
Tutorial for the Network Simulator "ns"
NS
Documentation
NS Source Files