Michael Houle

Michael Houle is a Senior University Lecturer in the Department of Computer Science at New Jersey Institute of Technology (NJIT). He holds a PhD from McGill University (1989) in computational geometry and has held academic and research positions at institutions including the University of Melbourne, National Institute of Informatics (Tokyo), IBM Tokyo Research Laboratory, and universities in Australia and Japan. His research focuses on dimensionality reduction, scalability in AI, machine learning, data mining, and outlier detection. He has made significant contributions to local intrinsic dimensionality estimation and its applications in clustering, classification, and adversarial vulnerability analysis.

Education:

• PhD, McGill University, Computer Science, 1989
• BSc, McGill University, Mathematics and Computer Science, 1984

Research interests include algorithmics, relational visualization, and theoretical foundations of data analysis. He has been recognized with multiple awards for best research papers at conferences such as SIAM ICDM, SISAP, and WIMS. His work bridges theoretical insights and practical applications in high-dimensional data processing and AI systems.

Key areas of contribution include the development of shared-neighbor clustering methods, analysis of adversarial attack vulnerability linked to high dimensionality, and methodologies for estimating intrinsic dimensionality in local data regions. He also explores applications of these concepts in anomaly detection and machine learning model interpretability.