Dr. Matthew V. Mahoney
Matthew V. Mahoney, MSEE, MSCS, Ph.D. is Adjunct Instructor in
Science at Florida Tech and
chief scientist at
Matt’s current research interests are data compression and the social impact of artificial intelligence. He helped develop the PAQ series of compressors, which are top ranked on many benchmarks, using a new algorithm called context mixing. He also maintains the large text benchmark which he hopes will promote research in natural language modeling.
He authored Computer Security: A Survey of Attacks and Defenses, A Model for Recursively Self Improving Programs, Text Compression as a Test for Artificial Intelligence, Fast Text Compression with Neural Networks, and Adaptive Weighing of Context Models for Lossless Data Compression, and coauthored Learning Nonstationary Models of Normal Network Traffic for Detecting Novel Attacks, Fusion of Information Retrieval Engines (FIRE), Modeling Multiple Time Series for Anomaly Detection, and PHAD: Packet Header Anomaly Detection for Indentifying Hostile Network Traffic.
Matt earned his A.S. at Cape Cod Community College in 1982. He earned his BSEE in Computer Engineering at the University of Massachusetts Dartmouth in 1984. He earned his MSEE in Computer Engineering at Florida Tech in 1988 with the thesis “Grid Logic: Programmable Logic that Implements Neural Networks.” He earned his MSCS at Florida Tech in 1998 with the thesis Complexity of Adaptive Spatial Indexing for Robust Distributed Data. He earned his Ph.D. in Computer Science at Florida Tech in 2003 with the dissertation topic A Machine Learning Approach to Detecting Attacks by Identifying Anomalies in Network Traffic.