Toby Dylan Hocking, PhD
Statistical machine learning researcher, focusing on fast, accurate and interpretable optimization algorithms for big data
toby.hocking@nau.edu
(928)523-5209
School of Informatics, Computing, and Cyber Systems
Northern Arizona University
Building 90, Room 210
1295 S. Knoles Dr.
Flagstaff, AZ 86011
Jobs
My lab is recruiting masters and PhD students who are interested in working on new statistical models, optimization algorithms, interactive systems, and software for machine learning. If you are interested in joining, please first read my mentorship plan to see what I will expect of you, then read the application instructions.
Brief CV
- 2018-present: Assistant Professor, Northern Arizona University, School of Informatics, Computing, and Cyber Systems.
- 2014-2018: Postdoc, McGill University, Human Genetics Department, with Guillaume Bourque.
- 2013: Postdoc, Tokyo Institute of Technology, Computer Science Department, with Masashi Sugiyama.
- 2009-2012: PhD in Mathematics from Ecole Normale Supérieure de Cachan, with Francis Bach and Jean-Philippe Vert.
- 2008-2009: Masters student, Université Paris 6, Statistics Department, research internship at INRA Jouy-en-Josas with Mathieu Gautier and Jean-Louis Foulley.
- 2006-2008: research assistant at Sangamo BioSciences.
- 2002-2006: undergraduate, UC Berkeley, Double major in Molecular & Cell Biology and Statistics, honors thesis with Terry Speed.
- Full CV, Short Bio.
Research interests: fast, accurate, and interpretable algorithms for learning from large data, using continuous optimization (clustering, regression, ranking, classification) and discrete optimization (changepoint detection, dynamic programming). The main application domains for these algorithms are genomics, neuroscience, medicine, microbiome, cybersecurity, robotics, satellite/sonar imagery, climate/carbon modeling.
I think reproducible research is important, so in addition to every paper I write, I also provide
- A reference implementation of the algorithm(s) described in the paper, typically as an R package.
- Source code for doing the analyses and creating the figures, typically in a GitHub repo.
For more info, see my publications and software.
If you want to send me encrypted messages, or verify my signed messages, you can use my GPG public key (updated April 2023, fingerprint 2AD6 F45A 31FF CF13 C3F7 0515 680A A3B7 3AA1 9C4F).
My ORCID is 0000-0002-3146-0865.
news
Jan 5, 2024 | To support our NSF POSE funded project about expanding the open-source software ecosystem around R data.table, we plan to present a talk in session Big-ish Data in R: Efficient tools for large in-memory datasets Tuesday, Aug 6: 2:00 PM - 3:50 PM, at JSM’2024 in Portland, Oregon. |
Jan 5, 2024 | Our paper Functional Labeled Optimal Partitioning has been published in Journal of Computational and Graphical Statistics. |
Sep 5, 2023 | To support our NSF POSE funded project about expanding the open-source ecosystem around data.table, we will be presenting a 3 hour tutorial, Using and contributing to the data.table package for efficient big data analysis, at the Latin-R conference in Montevideo, Uruguay, 18 October 2023, 9-12:30. |
Jul 12, 2023 | Our paper Predicting Neuromuscular Engagement to Improve Gait Training with a Robotic Ankle Exoskeleton has been published in IEEE Robotics and Automation Letters. |
May 24, 2023 | Our paper Microbial carbon use efficiency promotes global soil carbon storage has been published in Nature. |