{"id":49330,"date":"2024-10-09T13:14:52","date_gmt":"2024-10-09T11:14:52","guid":{"rendered":"https:\/\/www.h-its.org\/de\/event\/hits-colloquium-guillermo-cabrera-vives-machine-learning-astronomy-unique-challenges-for-a-unique-field\/"},"modified":"2026-01-26T11:17:14","modified_gmt":"2026-01-26T10:17:14","slug":"hits-colloquium-guillermo-cabrera-vives-machine-learning-astronomy-unique-challenges-for-a-unique-field","status":"publish","type":"tribe_events","link":"https:\/\/www.h-its.org\/de\/event\/hits-colloquium-guillermo-cabrera-vives-machine-learning-astronomy-unique-challenges-for-a-unique-field\/","title":{"rendered":"HITS Colloquium Guillermo Cabrera-Vives: Machine Learning &amp; Astronomy: Unique Challenges for a Unique Field"},"content":{"rendered":"<p>By <a href=\"http:\/\/www.inf.udec.cl\/~guille\/\">Guillermo Cabrera-Vives<\/a>, Department of Computer Science, Universidad de Concepci\u00f3n,\u00a0 Chile<\/p>\n<p>Currently Klaus Tschira Guest Professor at HITS<\/p>\n<p>&nbsp;<\/p>\n<p>Astronomy, like many other fields, is experiencing an explosion of data, with vast amounts of information pouring in from the universe. This influx has driven the development of new machine learning (ML) tools, enabling new discoveries while also presenting unique challenges. Unlike standard ML tools, those used in astronomy must adapt to data collected from distant sources, often captured under irregular and constrained observational conditions. In this talk, I will discuss the different challenges of applying ML in astronomy, including handling observational biases, irregular time series sampling, and the need for robust uncertainty estimation, among others.<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p><strong>REGISTRATION:<\/strong><\/p>\n<article>The talk will be hybrid.<\/article>\n<article>\n<article>If you would like to participate online, please register in advance <a href=\"https:\/\/kta-email.zoom.us\/j\/96553647151?pwd=fx0OFrjf9rCOfzGv7CmF5Fk3YQO93b.1\">here<\/a>.<\/article>\n<article><\/article>\n<article><\/article>\n<article><\/article>\n<article>After registering, you will receive a confirmation email containing information about joining the meeting.<\/article>\n<article><\/article>\n<article><\/article>\n<article>In case you are not able to attend, you can watch the talk afterwards on the HITS YouTube channel:\u00a0<a href=\"https:\/\/www.youtube.com\/user\/TheHITSters\" target=\"_blank\" rel=\"noopener noreferrer\" data-fancybox-group=\"fb_gallery_0_0\">https:\/\/www.youtube.com\/user\/TheHITSters<\/a>.<\/article>\n<\/article>\n<article><\/article>\n","protected":false},"excerpt":{"rendered":"<p>By Guillermo Cabrera-Vives, Department of Computer Science, Universidad de Concepci\u00f3n,\u00a0 Chile Currently Klaus Tschira Guest Professor at HITS &nbsp; Astronomy, &#8230;<\/p>\n","protected":false},"author":42,"featured_media":49332,"template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"hits-research-group":[],"tribe_events_cat":[1345,1408],"class_list":["post-49330","tribe_events","type-tribe_events","status-publish","has-post-thumbnail","hentry","tribe_events_cat-oeffentliche-veranstaltungen","tribe_events_cat-kolloquien","cat_oeffentliche-veranstaltungen","cat_kolloquien"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>HITS Colloquium Guillermo Cabrera-Vives: Machine Learning &amp; Astronomy: Unique Challenges for a Unique Field - HITS gGmbH<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.h-its.org\/de\/event\/hits-colloquium-guillermo-cabrera-vives-machine-learning-astronomy-unique-challenges-for-a-unique-field\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"HITS Colloquium Guillermo Cabrera-Vives: Machine Learning &amp; 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