Michael Houle

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.

Ph.D.; McGill University; Computer Science; 1989

B.S.; McGill University; Mathematics and Computer Science; 1984

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)

https://scholar.google.com/citations?user=24wEYXoAAAAJ

DS 675 - MACHINE LEARNING

Machine Learning, Data Mining, Data Structures & Algorithms

Machine Learning, Data Mining, Similarity Search, Algorithmics, Extreme Value Theory

LDReg: Local Dimensionality Regularized Self-Supervised Learning
Computing Research Repository (arXiv), February 2024

Bailey, James, & Houle, Michael E. , & Ma, Xingjun (2023). Relationships between tail entropies and local intrinsic dimensionality and their use for estimation and feature representation. Information Systems, 118(102245),

Houle, Michael E., & Kiermeier, Marie, & Zimek, Arthur (2023). Clustering High-Dimensional Data, Lior Rokach, Oded Maimon, Erez Shmueli (Eds.), Springer. (pp. 219-237). Springer