{"id":2,"date":"2020-12-04T15:11:06","date_gmt":"2020-12-04T15:11:06","guid":{"rendered":"http:\/\/quentin-duchemin.alwaysdata.net\/wiki\/?page_id=2"},"modified":"2026-07-02T14:39:57","modified_gmt":"2026-07-02T14:39:57","slug":"research","status":"publish","type":"page","link":"https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/index.php\/research\/","title":{"rendered":"Research"},"content":{"rendered":"\n

PhD dissertation<\/a>, Universit\u00e9 Gustave Eiffel. (Defended on June 20, 2022) Slides of the defense<\/a>.<\/p>\n\n\n\n

Preprints<\/strong><\/h4>\n\n\n\n

Clone-level multi-modal prediction of tumour drug response<\/a>. Q. Duchemin<\/strong>, D. Trejo Banos, A. Bertolini, P.F. Ferreira, R. Schill, M. Leinhard, R. Wegmann, B. Snijder, D. Stekhoven, N. Beerenwinkel, F. Singer, G. Obozinski, J. Kuipers. 2026

SIGLE: a valid procedure for Selective Inference with the Generalized Linear Lasso<\/a>, Q. Duchemin<\/strong> & Y. De-Castro. 2026
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Publications<\/h4>\n\n\n\n

<\/a>Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts<\/a>, Q. Duchemin<\/strong> & Guillaume Obozinski. Journal of Computational and Graphical Statistics, <\/strong>2026

A data-driven model to estimate time-variable catchment transit time distributions<\/a>, Q. Duchemin<\/strong>, R. Miazza, JW. Kirchner, P. Benettin. Water Resources Research<\/strong>, 2026<\/p>\n\n\n\n

Data-Driven Estimation of the hydrologic response via Generalized Additive Models<\/a>, Q. Duchemin<\/strong>,  M-G. Zanoni, MG. Floriancic, H. Seybold, G. Obozinski, JW. Kirchner, and P. Benettin. Geoscientific Model Development<\/strong>, 2025

Rapid quantitative magnetization transfer imaging:Utilizing the hybrid state and the generalized Bloch model,<\/a> Magnetic Resonance in Medecine<\/strong>, 2023.

Reliable Time Prediction in the Markov Stochastic Block Model,<\/a> Q. Duchemin<\/strong>. ESAIM: Probability & Statistics<\/strong>, <\/strong>2022. Python Notebook<\/strong><\/a><\/p>\n\n\n\n

The Random Geometric Graph: Recent developments and perspectives<\/a>, Q. Duchemin<\/strong>, Y. De-Castro.  High Dimensional Probability volume 9<\/strong>, 2022<\/p>\n\n\n\n

M<\/a>arkov Random Geometric Graph (MRGG): A Growth Model for Temporal Dynamic Networks<\/a>,<\/a> Q. Duchemin<\/strong>, Y. De-Castro. Electronic Journal of Statistics<\/strong>, 2022. Python Notebook<\/strong><\/a><\/p>\n\n\n\n

Concentration inequality for U-statistics of order two for uniformly ergodic Markov chains,<\/a> Q. Duchemin<\/strong>, Y. De-Castro & C. Lacour.. Bernoulli, <\/strong>2022.<\/p>\n\n\n\n

Three rates of convergence or separation via U-statistics in a dependent framework<\/a>, Q. Duchemin<\/strong>, Y. De-Castro and C. Lacour. Journal of Machine Learning Research<\/strong>, 2022<\/p>\n\n\n\n

Cram\u00e9r-Rao bound-informed training of neural networks for quantitative MRI<\/a>, X. Zhang, Q. Duchemin<\/strong>, K. Liu, S. Flassbeck, C. Gultekin, C. Fernadez-Granda and J. Assl\u00e4nder. Magnetic Resonance in Medicine<\/strong>, 2022<\/p>\n\n\n\n

Conference papers<\/strong><\/h4>\n\n\n\n

De-identification of free-text documents for the responsible use of Artificial Intelligence in cancer registration.<\/a> A. Katinskaia & Q. Duchemin <\/strong>Annual Meeting of the Group for Cancer Epidemiology and Registration in Latin Language Countries (GRELL)<\/em> 2026

Rapid quantitative magnetization transfer imaging: utilizing the hybrid state and the generalized Bloch model<\/a>, J. Assl\u00e4nder, C. Gultekin, X. Zhang, Q. Duchemin<\/strong>, K. Liu, R. WE Charlson, T. Shepherd, C. Fernandez-Granda, and S. Flassbeck. Proc. 31th Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) 2022 (selected for oral presentation<\/em><\/strong>)<\/p>\n\n\n\n

Optimized dimensionality reduction for parameter estimation in MR fingerprinting via deep learning<\/a> Q. Duchemin<\/strong>, K. Liu, C. Fernandez-Granda, J. Asslaender. Proc. 28th Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) 2020 (selected for oral presentation<\/em><\/strong>)
Watch the video presenting our work<\/span><\/a> and read the slides of the presentation.<\/span><\/a><\/p>\n\n\n\n

Concentration inequality for U-statistics for uniformly ergodic Markov chains, and applications, Q.Duchemin<\/strong>, Y. De Castro & C. Lacour. Bernoulli-IMS 10th World Congress in Probability and Statistics 2021 (selected for oral presentation<\/em><\/strong>) Watch the video presenting our work<\/span><\/a>, read the slides of the presentation<\/span><\/a> and read our poster<\/a>.<\/span><\/p>\n\n\n\n

Awards<\/strong><\/h4>\n\n\n\n