{"id":750,"date":"2021-09-03T20:39:07","date_gmt":"2021-09-03T20:39:07","guid":{"rendered":"http:\/\/quentin-duchemin.alwaysdata.net\/wiki\/?page_id=750"},"modified":"2026-03-31T12:47:43","modified_gmt":"2026-03-31T12:47:43","slug":"informatic-computer-science","status":"publish","type":"page","link":"https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/index.php\/informatic-computer-science\/","title":{"rendered":"Informatic &#038; Computer Science"},"content":{"rendered":"\n<h4>Softwares<br><\/h4>\n\n\n\n<div class=\"wp-block-media-text alignwide is-stacked-on-mobile is-image-fill\" style=\"grid-template-columns:32% auto\"><figure class=\"wp-block-media-text__media\" style=\"background-image:url(https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2026\/03\/logo-1024x636.png);background-position:50% 50%\"><img loading=\"lazy\" width=\"1024\" height=\"636\" src=\"https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2026\/03\/logo-1024x636.png\" alt=\"\" class=\"wp-image-950\" srcset=\"https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2026\/03\/logo-1024x636.png 1024w, https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2026\/03\/logo-300x186.png 300w, https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2026\/03\/logo-768x477.png 768w, https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2026\/03\/logo.png 1312w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<p style=\"font-size:25px\"><a href=\"https:\/\/github.com\/cbg-ethz\/scClone2DR\/\" data-type=\"URL\" data-id=\"https:\/\/github.com\/cbg-ethz\/scClone2DR\/\">Clone-level multi-modal prediction of tumour drug response<\/a><\/p>\n<\/div><\/div>\n\n\n\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-media-text alignwide is-stacked-on-mobile\" style=\"grid-template-columns:60% auto\"><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" width=\"1024\" height=\"241\" src=\"https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2026\/03\/logo-1-1024x241.png\" alt=\"\" class=\"wp-image-951\" srcset=\"https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2026\/03\/logo-1-1024x241.png 1024w, https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2026\/03\/logo-1-300x71.png 300w, https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2026\/03\/logo-1-768x181.png 768w, https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2026\/03\/logo-1.png 1536w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<p style=\"font-size:25px\"><a href=\"https:\/\/github.com\/cbg-ethz\/scClone2DR\/https:\/\/github.com\/quentin-duchemin\/DisTreebution\/\" data-type=\"URL\" data-id=\"https:\/\/github.com\/cbg-ethz\/scClone2DR\/https:\/\/github.com\/quentin-duchemin\/DisTreebution\/\">Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts<\/a><\/p>\n<\/div><\/div>\n\n\n\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-media-text alignwide is-stacked-on-mobile is-image-fill\" style=\"grid-template-columns:32% auto\"><figure class=\"wp-block-media-text__media\" style=\"background-image:url(http:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2021\/09\/invivores.png);background-position:50% 6%\"><img loading=\"lazy\" width=\"527\" height=\"465\" src=\"http:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2021\/09\/invivores.png\" alt=\"\" class=\"wp-image-751\" srcset=\"https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2021\/09\/invivores.png 527w, https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2021\/09\/invivores-300x265.png 300w\" sizes=\"(max-width: 527px) 100vw, 527px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<p style=\"font-size:25px\"><a href=\"https:\/\/quentin-duchemin.github.io\/MRF-CRBLoss\/build\/index.html\" data-type=\"URL\" data-id=\"https:\/\/quentin-duchemin.github.io\/MRF-CRBLoss\/build\/index.html\">Parameter estimation in MRI using deep learning<\/a><\/p>\n<\/div><\/div>\n\n\n\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-media-text alignwide is-stacked-on-mobile is-image-fill\" style=\"grid-template-columns:32% auto\"><figure class=\"wp-block-media-text__media\" style=\"background-image:url(http:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2022\/08\/sigle.png);background-position:50% 50%\"><img loading=\"lazy\" width=\"640\" height=\"480\" src=\"http:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2022\/08\/sigle.png\" alt=\"\" class=\"wp-image-892\" srcset=\"https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2022\/08\/sigle.png 640w, https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2022\/08\/sigle-300x225.png 300w\" sizes=\"(max-width: 640px) 100vw, 640px\" \/><\/figure><div class=\"wp-block-media-text__content\">\n<p style=\"font-size:25px\"><a href=\"https:\/\/quentin-duchemin.github.io\/SIGLE\" data-type=\"URL\" data-id=\"https:\/\/quentin-duchemin.github.io\/SIGLE\">SIGLE for Post-Selection Inference in the logistic model<\/a><\/p>\n<\/div><\/div>\n\n\n\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-media-text alignwide is-stacked-on-mobile\" style=\"grid-template-columns:30% auto\"><figure class=\"wp-block-media-text__media\"><img src=\"http:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2021\/09\/msbm.png\" alt=\"\" class=\"wp-image-753\"\/><\/figure><div class=\"wp-block-media-text__content\">\n<p style=\"font-size:25px\"><a href=\"https:\/\/github.com\/quentin-duchemin\/inference-markovian-SBM\">Community detection using SDP algorithms<\/a><\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-media-text alignwide is-stacked-on-mobile\" style=\"grid-template-columns:30% auto\"><figure class=\"wp-block-media-text__media\"><img src=\"http:\/\/quentin-duchemin.alwaysdata.net\/wiki\/wp-content\/uploads\/2021\/09\/s7.png\" alt=\"\" class=\"wp-image-754\"\/><\/figure><div class=\"wp-block-media-text__content\">\n<p style=\"font-size:25px\"><a href=\"https:\/\/github.com\/quentin-duchemin\/Markovian-random-geometric-graph\" data-type=\"URL\" data-id=\"https:\/\/github.com\/quentin-duchemin\/Markovian-random-geometric-graph\">Non parametric estimation in Random Geometric Graphs<\/a><\/p>\n<\/div><\/div>\n\n\n\n<h4>Data Science: Teaching material<\/h4>\n\n\n\n<p>During fall 2021, I taught a course to second year student of the engineering school &#8220;Ecole des Ponts ParisTech&#8221; titled : Statistics &amp; Data Analysis&#8221;.<br>I made a <a href=\"https:\/\/quentin-duchemin.github.io\/ENPC-SDA\/\">website<\/a> for this course with <strong>several examples of applications of testing procedures to real data<\/strong>.<br>I also wrote some exercises for the class. For example, <a href=\"https:\/\/quentin-duchemin.github.io\/ENPC-SDA\/content\/Exercises\/shapiro_wilk\/shapiro-wilk.html\">this one<\/a> on the Shapiro Wilk Test and <a href=\"https:\/\/quentin-duchemin.github.io\/ENPC-SDA\/content\/Exercises\/ROCcurve.pdf\" data-type=\"URL\" data-id=\"https:\/\/quentin-duchemin.github.io\/ENPC-SDA\/content\/Exercises\/ROCcurve.pdf\">this one<\/a> on the ROC curve.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Softwares Clone-level multi-modal prediction of tumour drug response Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Parameter estimation in MRI using deep learning SIGLE for Post-Selection Inference in the logistic model Community detection using SDP algorithms Non parametric estimation in Random Geometric Graphs Data Science: Teaching material During fall 2021, I taught<\/p>\n<div class=\"more-link\">\n             <a href=\"https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/index.php\/informatic-computer-science\/\" class=\"read-more\">Read More<i class=\"fa fa-caret-right\"><\/i><\/a>\n        <\/div>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/index.php\/wp-json\/wp\/v2\/pages\/750"}],"collection":[{"href":"https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/index.php\/wp-json\/wp\/v2\/comments?post=750"}],"version-history":[{"count":14,"href":"https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/index.php\/wp-json\/wp\/v2\/pages\/750\/revisions"}],"predecessor-version":[{"id":952,"href":"https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/index.php\/wp-json\/wp\/v2\/pages\/750\/revisions\/952"}],"wp:attachment":[{"href":"https:\/\/quentin-duchemin.alwaysdata.net\/wiki\/index.php\/wp-json\/wp\/v2\/media?parent=750"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}