• 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

  • Tao F, Huang Y, Hungate BA, Manzoni S, Frey SD, Schmidt MWI, Reichstein M, Carvalhais N, Ciais P, Jiang L, Lehmann J, Mishra U, Hugelius G, Hocking TD, Lu X, Shi Z, Viatkin K, Vargas R, Yigini Y, Omuto C, Malik AA, Peralta G, Cuevas-Corona R, Di Paolo LE, Luotto I, Liao C, Liang YS, Saynes VS, Huang X, Luo Y. Microbial carbon use efficiency promoting global soil carbon storage. Under review in Nature.
  • Why does functional pruning yield such fast algorithms for optimal changepoint detection? Invited talk for TRIPODS seminar video, slides, IEEE NJACS, ASU West ML Day.
  • Hocking TD. Chapter “Introduction to machine learning and neural networks” for book “Land Carbon Cycle Modeling: Matrix Approach, Data Assimilation, and Ecological Forecasting” edited by Luo Y, in preparation (expected publication Jan 2022), 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 (gfpop), Software (gfpopgui), Reproducible.
  • Venuto D, Hocking TD, Spanurattana S, Sugiyama M. Support vector comparison machines. Preprint arXiv:1401.8008. Under review at Machine Learning, Software, Reproducible.
  • Mihaljevic JR, Borkovec S, Ratnavale S, Hocking TD, Banister KE, Eppinger JE, Hepp CM, Doerry E. Rapid simulations of spatially explicit and stochastic models of infectious disease. Preprint medRxiv. Under review at Biology Methods & Protocols, Software.
  • Hillman J, Hocking TD. Optimizing ROC Curves with a Sort-Based Surrogate Loss Function for Binary Classification and Changepoint Detection. Under review at Journal of Machine Learning Research, Preprint arXiv:2107.01285, Software, Reproducible.
  • Hocking TD and Killick R. Changepoint detection algorithms and applications in R. Textbook in preparation.
  • Hocking TD. A breakpoint detection error function for segmentation model selection and validation. Preprint arXiv:1509.00368, Software, Reproducible.
  • Hocking TD, Khare A. Learning penalty functions for changepoint detection using elastic-net regularized accelerated failure time models. In preparation, Software, Reproducible.

Accepted, in press




  • Fotoohinasab A, Hocking TD, Afghah F. A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection. Paper in proceedings of Asilomar Conference on Signals, Systems, and Computers (2020), Preprint arXiv:2004.13558.
  • 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 2020), 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 (2020) 25:367-378, PDF, Software.


  • 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.


  • 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, used in dbNSFP.
  • 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.



  • 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.


  • 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, 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.


  • 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.


  • 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.


  • 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.


  • 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.


  • 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.


  • 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.