Yan Zhou

D.Sc. - Washington University in St. Louis

Assistant Professor
School of Computer & Information Sciences
University of South Alabama
Mobile, AL 36688

 
Office: Faculty Court East 12
Tel: (251) 460-7555
Fax: (251) 460-7274
Email:
  PGP Pub Key   Fingerprint
 
Spring 2008 Office Hours:
MW: 4:00-6:00pm, Tu: 11:00am-noon and 2:00-3:00pm
 
 

Classes

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 few 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:
    • Pacific Rim International Conferences on Artificial Intelligence 2008-2009 (PRICAI)
    • Pacific-Asia Conference on Knowledge Discovery and Data Mining 2007-2009
    • Conference on Artificial Intelligence 2007 (AAAI) (PAKDD)
    • International Conference on the Principles and Practice of Programming in Java 2007 (PPPJ)
  • Reviewer for:
    • Journal of Artificial Intelligence Research
    • Pattern Recognition
    • Knowledge and Information Systems
    • IEEE Transactions on Knowledge and Data Engineering
    • International Conference on Machine Learning
    • Annual Conference on Computational Learning Theory

Recent Publications

  • Malware Detection Using Adaptive Data Compression, accepted by ACM CCS AISec Workshop, 2008 (with M. Inge).
  • Countering Good Word Attacks on Statistical Spam Filters with Instance Differentiation and Multiple Instance Learning, invited book chapter in Tools in Artificial Intelligence, I-Tech Eduacation and Publishing, Vienna, Austria, to appear. (with Z. Jorgensen and M. Inge)
  • A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters, Journal of Machine Learning Research (JMLR) 9(Jun):1115--1146, 2008. (with Z. Jorgensen and M. Inge) (Journal version of the ICTAI conference paper)
  • Combating Good Word Attacks on Statistical Spam Filters with Multiple Instance Learning, in the 19th IEEE International Conference on Tools with Artificial Intelligence, 2007 (with Z. Jorgensen and M. Inge)
  • Smartacking: Improving TCP Performance from the Receiving End, in Journal of Interent Engineering, 2007. (with S. Gorinsky, D. Blandford, S. Goldman, and D. Dooly)
  • Adaptive Spam Filtering Using Dynamic Feature Spaces, in International Journal of Artificial Intelligence Tools, 16(4), 627--646, 2007 (with M.S. Mulekar and P. Nerellapalli)
  • Minimum Spanning Tree Based Clustering Algorithms, in the 18th IEEE International Conference on Tools with Artificial Intelligence, 2006 (with O. Grygorash and Z. Jorgensen)
  • Adaptive Spam Filtering Using Dynamic Feature Space, in the 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2005) (with Madhuri Mulekar and Praveen Nerellapalli). This paper presents a compression-based spam filtering strategy.
  • 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).
  • Enhancing Supervised Learning with Unlabeled Data, in Proceedings of the 17th International Conference on Machine Learning. Standord University, June29-July 2, ICML, 2000 (with Sally Goldman).

Computer Science Conference Rankings

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

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