Portrait of Toby Dylan Hocking

Toby Dylan Hocking

Associate Academic Member
Associate Professor, Université Sherbrooke, Department of Computer Science
Research Topics
Computational Biology
Computer Vision
Data Mining
Deep Learning
Medical Machine Learning
Optimization
Website
Google Scholar

Biography

A Berkeley-educated California native, Toby Dylan Hocking received his PhD in mathematics (machine learning) from École Normale Supérieure de Cachan (Paris, France) in 2012. He worked as a postdoc in Masashi Sugiyama’s machine learning lab at Tokyo Tech in 2013, and in Guillaume Bourque’s genomics lab in McGill University (2014-2018).

In 2018-2024 he was a tenure-track Assistant Professor at Northern Arizona University, and since 2024, he is a tenured Associate Professor at Université de Sherbrooke, where he directs the LASSO research lab (Learning Algorithms, Statistical Software, Optimization). Toby is also an Associate Academic member at Mila - Quebec Artificial Intelligence Institute.

He has authored dozens of R packages, and has published 50+ peer-reviewed research papers on machine learning and statistical software. He has mentored 30+ students in research projects, as well as another 30+ open-source software contributors with R Project in Google Summer of Code.

Publications

Functional Labeled Optimal Partitioning
Jacob M. Kaufman
Alyssa J. Stenberg
Toby Dylan Hocking
2024-01-05
Journal of Computational And Graphical Statistics (published)
doi.org
Deep Learning Approach for Changepoint Detection: Penalty Parameter Optimization
Tung L. Nguyen
Toby Dylan Hocking
Changepoint detection, a technique for identifying significant shifts within data sequences, is crucial in various fields such as finance, g… (see more)enomics, medicine, etc. Dynamic programming changepoint detection algorithms are employed to identify the locations of changepoints within a sequence, which rely on a penalty parameter to regulate the number of changepoints. To estimate this penalty parameter, previous work uses simple models such as linear models or decision trees. This study introduces a novel deep learning method for predicting penalty parameters, leading to demonstrably improved changepoint detection accuracy on large benchmark supervised labeled datasets compared to previous methods.
2024-01-01
arXiv.org (preprint)
doi.org