• My pages on Google Scholar and Mathematics Genealogy Project.
  • Peer review notice: in addition to peer-reviewed journals, I publish papers at machine learning conferences such as ICML and NIPS, which have double-blind peer reviews, and only accept about 20% of submitted papers.
  • Reproducible Research notice: a “Reproducible” link for each paper below provides source code and data that I used to perform the analysis and make the figures/tables.
  • Software notice: a “Software” link for each paper below provides a software package with a reference implementation of the algorithms described in our paper.
  • Selected publications in bib format.

In progress

  • Hocking TD and Killick R. Changepoint detection algorithms and applications in R. Textbook in preparation.
  • Hocking TD. Chapter “Introduction to machine learning and neural networks” for book “New Advances in Land Carbon Cycle Modeling” edited by Luo Y, in preparation.
  • Liehrmann A, Hocking TD. Optimal multiple changepoint detection models for recognition of histone modification events in ChIP-Seq data. In preparation.
  • Barnwal A, Cho H, Hocking TD. Survival regression with accelerated failure time model in XGBoost. In preparation. Preprint arXiv:2006.04920. Software, Reproducible
  • Hocking TD, Khare A. Learning penalty functions for changepoint detection using elastic-net regularized accelerated failure time models. In preparation. Software, Reproducible
  • Chaves AP, Egbert J, Hocking TD, Doerry E, Gerosa MA. Chatbots language design: the influence of language use on user experience. Under review at ACM Transactions on Computer-Human Interaction.
  • Abraham A, Prys-Jones T, De Cuyper A, Ridenour C, Hempson G, Hocking TD, Clauss M, Doughty C. Improved estimation of gut passage time considerably affects trait-based dispersal models. Under review at Functional Ecology.
  • Hocking TD, Srivastava A. Labeled Optimal Partitioning. Preprint arXiv:2006.13967. Software, Reproducible
  • Fotoohinasab A, Hocking TD, Afghah F. A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS-Complex Detection. Under review at Computers in Biology and Medicine. Preprint arXiv:2004.13558
  • Hocking TD, Vargovich J. Linear time dynamic programming for the exact path of optimal models selected from a finite set. Under review at Journal of Computational and Graphical Statistics. Preprint arXiv:2003.02808, Software, Reproducible
  • Runge V, Hocking TD, Romano G, Afghah F, Fearnhead P, Rigaill G. gfpop: an R Package for Univariate Graph-Constrained Change-point Detection. Under review at Journal of Statistical Software. Preprint arXiv:2002.03646, Software, Reproducible
  • Hocking TD. Regular expressions and reshaping using data tables and the nc package. Under review at R Journal. Software, Reproducible
  • Venuto D, Hocking TD, Spanurattana S, Sugiyama M. Support vector comparison machines. Preprint arXiv:1401.8008. Under review at Machine Learning. Software, Reproducible

Accepted, in press

  • Fotoohinasab A, Hocking TD, Afghah F. A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection. Accepted for Asilomar Conference on Signals, Systems, and Computers. Preprint arXiv:2004.13558
  • Hocking TD, Rigaill G, Fearnhead P, Bourque G. Generalized Functional Pruning Optimal Partitioning (GFPOP) for Constrained Changepoint Detection in Genomic Data. Preprint arXiv:1810.00117. Accepted for publication in Journal of Statistical Software. Software, Reproducible. Presentation at useR 2019: slides, video.

2020

  • Fotoohinasab A, Hocking TD, Afghah F. A Graph-constrained Changepoint Detection Approach for ECG Segmentation. In proceedings of 42th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Xplore
  • Hocking TD, Rigaill G, Fearnhead P, Bourque G. Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection in Genomic Data. Journal of Machine Learning Research 21(87):1−40, 2020. JMLR, Preprint arXiv:1703.03352, Software, Reproducible.
  • Hocking TD, Bourque G. Machine Learning Algorithms for Simultaneous Supervised Detection of Peaks in Multiple Samples and Cell Types. Pacific Symposium on Biocomputing 25:367-378. PDF, Software

2019

  • Hocking TD. Comparing namedCapture with other R packages for regular expressions. R Journal (2019). doi:10.32614/RJ-2019-050 Software, Reproducible
  • Jewell S, Hocking TD, Fearnhead P, Witten D. Fast Nonconvex Deconvolution of Calcium Imaging Data. Biostatistics (2019), doi: 10.1093/biostatistics/kxy083. pubmed

2018

  • Depuydt P, Koster J, Boeva V, Hocking TD, Speleman F, Schleiermacher G, De Preter K. Meta-mining of copy number profiles of high-risk neuroblastoma tumors. Scientific Data (2018).
  • Alirezaie N, Kernohan KD, Hartley T, Majewski J, Hocking TD. ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants. American Journal of Human Genetics (2018). doi:10.1016/j.ajhg.2018.08.005 AJHG, Software.
  • Sievert C, Cai J, VanderPlas S, Ferris K, Khan FUF, Hocking TD. Extending ggplot2 for linked and animated web graphics. Journal of Computational and Graphical Statistics (2018). doi:10.1080/10618600.2018.1513367. JCGS, Software, Reproducible, Interactive Figures.
  • Depuydt P, Boeva V, Hocking TD, Cannoodt R, Ambros IM, Ambros PF, Asgharzadeh S, Attiyeh EF, Combaret V, Defferrari R, Fischer M, Hero B, Hogarty MD, Irwin MS, Koster J, Kreissman S, Ladenstein R, Lapouble E, Laureys G, London WB, Mazzocco K, Nakagawara A, Noguera R, Ohira M, Park JR, Pötschger U, Theissen J, Tonini GP, Valteau-Couanet D, Varesio L, Versteeg R, Speleman F, Maris JM, Schleiermacher G, De Preter K. Genomic Amplifications and Distal 6q Loss: Novel Markers for Poor Survival in High-risk Neuroblastoma Patients. Journal of the National Cancer Institute (2018). DOI:10.1093/jnci/djy022. JNCI

2017

  • Drouin A, Hocking TD, Laviolette F. Maximum margin interval trees. Neural Information Processing Systems (NIPS) 2017. Software, Reproducible, arXiv, NIPS, video
  • Hocking TD, Goerner-Potvin P, Morin A, Shao X, Pastinen T, Bourque G. Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning. Bioinformatics (2017) 33 (4): 491-499. pubmed, Software, Reproducible
  • Introduction to optimal changepoint detection algorithms. Hocking TD and Killick R. useR2017 conference tutorial, video, Reproducible.

2016

  • Shimada K, Shimada S, Sugimoto K, Nakatochi M, Suguro M, Hirakawa A, Hocking TD, Takeuchi I, Tokunaga T, Takagi Y, Sakamoto A, Aoki T, Naoe T, Nakamura S, Hayakawa F, Seto M, Tomita A, Kiyoi H. Development and analysis of patient-derived xenograft mouse models in intravascular large B-cell lymphoma. Leukemia 2016. pubmed
  • Chicard M, Boyault S, Colmet-Daage L, Richer W, Gentien D, Pierron G, Lapouble E, Bellini A, Clement N, Iacono I, Bréjon S, Carrere M, Reyes C, Hocking TD, Bernard V, Peuchmaur M, Corradini N, Faure-Conter C, Coze C, Plantaz D, Defachelles A-S, Thebaud E, Gambart M, Millot F, Valteau-Couanet D, Michon J, Puisieux A, Delattre O, Combaret V, Schleiermacher G. Genomic copy number profiling using circulating free tumor DNA highlights heterogeneity in neuroblastoma. Clinical Cancer Research 2016. journal
  • Maidstone R, Hocking TD, Rigaill G, Fearnhead P. On optimal multiple changepoint algorithms for large data. Statistics and Computing (2016). doi:10.1007/s11222-016-9636-3 journal, Software, Reproducible
  • The animint2 manual, online “book” with multiple chapters, Reproducible.
  • Understanding and Creating Interactive Graphics. Hocking TD, Ekstrøm CT. useR2016 conference tutorial, Reproducible.

2015

  • Hocking TD, Rigaill G, Bourque G. PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data. International Conference on Machine Learning (ICML), 2015. PMLR, video, Software, Reproducible
  • Hocking TD. A breakpoint detection error function for segmentation model selection and validation. Preprint arXiv:1509.00368, Software, Reproducible
  • Hocking TD, Bourque G. PeakSegJoint: fast supervised peak detection via joint segmentation of multiple count data samples. Preprint arXiv:1506.01286, Software, Reproducible
  • Animint: Interactive Web-Based Animations Using Ggplot2’s Grammar of Graphics. VanderPlas SR and Sievert C and Hocking TD. Presentation at JSM2015.

2014

  • Suguro M, Yoshida N, Umino A, Kato H, Tagawa H, Nakagawa M, Fukuhara N, Karnan S, Takeuchi I, Hocking TD, Arita K, Karube K, Tsuzuki S, Nakamura S, Kinoshita T, Seto M. Clonal heterogeneity of lymphoid malignancies correlates with poor prognosis. Cancer Sci. 2014 Jul;105(7):897-904. pubmed
  • Hocking TD, Boeva V, Rigaill G, Schleiermacher G, Janoueix-Lerosey I, Delattre O, Richer W, Bourdeaut F, Suguro M, Seto M, Bach F, Vert J-P. SegAnnDB: interactive Web-based genomic segmentation. Bioinformatics (2014) 30 (11): 1539-1546. DOI:10.1093/bioinformatics/btu072 pubmed, Software, Reproducible

2013

  • Hocking TD, Wutzler T, Ponting K and Grosjean P. Sustainable, extensible documentation generation using inlinedocs. Journal of Statistical Software (2013), 54(6), 1-20. DOI:10.18637/jss.v054.i06 journal, Software, Reproducible
  • Hocking TD, Schleiermacher G, Janoueix-Lerosey I, Boeva V, Cappo J, Delattre O, Bach F, Vert J-P. Learning smoothing models of copy number profiles using breakpoint annotations. BMC Bioinfo. 2013, 14:164. DOI:10.1186/1471-2105-14-164 journal, Software, Reproducible
  • Hocking TD, Rigaill G, Bach F, Vert J-P. Learning sparse penalties for change-point detection using max-margin interval regression. International Conference on Machine Learning (ICML), 2013. PMLR, video, Software, Reproducible

2012

  • Hocking TD, Rigaill G. SegAnnot: an R package for fast segmentation of annotated piecewise constant signals, Preprint hal-00759129. Software, Reproducible
  • Hocking TD. Learning algorithms and statistical software, with applications to bioinformatics. PhD thesis, Ecole Normale Superieure de Cachan, France. tel-00906029, Reproducible

2011

  • Hocking TD, Joulin A, Bach F, Vert J-P. Clusterpath: an Algorithm for Clustering using Convex Fusion Penalties. International Conference on Machine Learning (ICML), 2011. pdf, video Software, Reproducible

2010

  • Gautier M, Hocking TD, Foulley JL. A Bayesian outlier criterion to detect SNPs under selection in large data sets. PloS ONE 5 (8), e11913 (2010). journal

2008

  • Doyon Y, McCammon JM, Miller JC, Faraji F, Ngo C, Katibah GE, Amora R, Hocking TD, Zhang L, Rebar EJ, Gregory PD, Urnov FD, Amacher SL. Heritable targeted gene disruption in zebrafish using designed zinc-finger nucleases. Nature biotechnology 26 (6), 702-70 (2008). pubmed