Michael Houle

Senior University Lecturer, Computer Science
About Me
I received my PhD degree in 1989 from McGill University in Canada, in the area of computational geometry, and my research interests have been shifting ever since. After graduation, I spent almost 3 years in Japan, and then 9 years in Australia at the University of Newcastle and the University of Sydney, during which time it appeared that I had found my niche in algorithmics and relational visualization. In 2001 I was back in Japan, where at IBM's Tokyo Research Laboratory my main focus was on approximate similarity search and shared-neighbor clustering methods for data mining applications. From 2004 to 2021, at the National Institute of Informatics, Tokyo, my theoretical interests were concentrated on dimensionality and scalability, while targeting applications in the context of fundamental AI-ML-DM tasks such as search, clustering, classification, and outlier detection. During the pandemic, I returned to Canada, setting up home in the Vancouver area where I conduct my teaching and research for NJIT, with occasional visits to the main campus.
Education
Ph.D.; McGill University; Computer Science; 1989
B.Sc.; McGill University; Mathematics and Computer Science; 1984
Experience
New Jersey Institute of Technology
Senior University Lecturer, January 2023 -
The University of Melbourne
Principal Fellow, November 2021 -
Sokendai (Graduate University of Advanced Studies)
Visiting Professor, April 2009 - June 2021
National Institute of Informatics
Visiting Professor, February 2004 - June 2021
IBM Tokyo Research Laboratory
Visiting Scientist, April 2001 - December 2003
The University of Sydney
Senior Lecturer, September 1999 - March 2001
The University of Newcastle
Senior Lecturer, January 1998 - August 1999
The University of Newcastle
Lecturer, June 1992 - December 1997
The University of Tokyo
Research Associate, September 1990 - April 1992
Kyushu University
Research Associate, September 1989 - August 1990
McGill University
Casual Lecturer, September 1985 - August 1988
Awards & Honors
2024 Best Research Paper Award, SIAM International Conference on Data Mining (SDM 2024)
2021 Best Paper Award, 14th International Conference on Similarity Search and Applications (SISAP 2021)
2018 Best Paper Award, 8th International Conference on Web Intelligence, Mining and Semantics (WIMS 2018)
2014 Best Paper Award, 7th International Conference on Similarity Search and Applications (SISAP 2014)
2010 Best Research Paper Award, 10th IEEE International Conference on Data Mining (ICDM 2010)
Website
https://scholar.google.com/citations?user=24wEYXoAAAAJ
2026 Fall Courses
DS 675 - MACHINE LEARNING
Teaching Interests
Machine Learning, Data Mining, Data Structures & Algorithms
Past Courses
DS 675: MACHINE LEARNING
Research Interests
Machine Learning, Data Mining, Similarity Search, Algorithmics, Extreme Value Theory
Conference Paper
"Dimensionality-Aware Outlier Detection"
2024 SIAM International Conference on Data Mining (SDM), April (2nd Quarter/Spring), 2024.
"LDReg: Local Dimensionality Regularized Self-Supervised Learning"
2024 International Conference on Learning Representations (ICLR), February, 2024.
"Relationships Between Local Intrinsic Dimensionality and Tail Entropy"
2021 International Conference on Similarity Search and Applications (SISAP), October (4th Quarter/Autumn), 2021.
"The Effect of Random Projection on Local Intrinsic Dimensionality"
2021 International Conference on Similarity Search and Applications (SISAP), October (4th Quarter/Autumn), 2021.
"A Dimensionality-Driven Approach for Unsupervised Out-of-distribution Detection"
2021 SIAM International Conference on Data Mining (SDM), April (2nd Quarter/Spring), 2021.
"Local Intrinsic Dimensionality III: Density and Similarity"
2020 International Conference on Similarity Search and Applications (SISAP), October (4th Quarter/Autumn), 2020.
Journal Article
James Bailey, Michael E. Houle, Xingjun Ma. 2023. "Relationships between Tail Entropies and Local Intrinsic Dimensionality and their Use for Estimation and Feature Representation." Information Systems, vol. 118, no. 102245.
James Bailey, Michael E. Houle, Xingjun Ma. 2022. "Local Intrinsic Dimensionality, Entropy and Statistical Divergences." Entropy, vol. 24, no. 9, pp. 1220.
Laurent Amsaleg, James Bailey, Amélie Barbe, Sarah M. Erfani, Teddy Furon, Michael E. Houle, Miloš Radovanović, Xuan Vinh Nguyen. 2021. "High Intrinsic Dimensionality Facilitates Adversarial Attack: Theoretical Evidence." IEEE Transactions on Information Forensics and Security (TIFS), vol. 16, pp. 854 - 865.
Chapter
Michael E. Houle, Marie Kiermeier, Arthur Zimek. "Clustering High-Dimensional Data." In Lior Rokach, Oded Maimon, Erez Shmueli, eds., "Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook," pp. 219-237. Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook (Springer), 2023.
Vladimir Estivill-Castro, Michael Houle. "Approximating Proximity to Fast and Robust Distance-Based Clustering." "Data Mining," pp. 1-21. IGI Global, 2002.