{"id":48037,"date":"2024-04-24T10:07:35","date_gmt":"2024-04-24T14:07:35","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/?post_type=tribe_events&#038;p=48037"},"modified":"2024-04-24T10:07:51","modified_gmt":"2024-04-24T14:07:51","slug":"ams-weekly-seminar-jeremias-sulam","status":"publish","type":"tribe_events","link":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-weekly-seminar-jeremias-sulam\/","title":{"rendered":"AMS Weekly Seminar | Assistant Professor Jeremias Sulam"},"content":{"rendered":"<p><strong>Location:<\/strong> Olin 305<\/p>\n<p><strong>When:<\/strong>\u00a0April 25th\u00a0at 1:30 p.m.<\/p>\n<p class=\"xmsonormal\"><strong>Title:<\/strong> <i>Yay, my deep network works! But.. what did it learn?<\/i><\/p>\n<p class=\"xmsonormal\"><strong>Abstract:<\/strong> Modern machine learning methods are revolutionizing what we can do with data \u2014 from tiktok video recommendations to biomarker discovery in cancer research. Yet, the complexity of these deep models makes it harder to understand <i>what functions<\/i> these data-dependent models are computing, and which features they learn to be important for a given task. In this talk, I will review two approaches for turning general deep learning models more interpretable, both in an unsupervised setting in the context of imaging inverse problems, as well as in supervised classification problems for computer vision.<\/p>\n<p class=\"xmsonormal\"><strong>Bio: <\/strong>Jeremias Sulam received his bioengineering degree from Universidad Nacional de Entre R\u00edos, Argentina, in 2013, and his PhD in Computer Science from the Technion \u2013 Israel Institute of Technology, in 2018. He joined the Biomedical Engineering Department at Johns Hopkins University in 2018 as an assistant professor, and he is also a core faculty at the Mathematical Institute for Data Science (MINDS) and the Center for Imaging Science at JHU. He is the recipient of the Best Graduates Award of the Argentinean National Academy of Engineering, and the Early CAREER award of the National Science Foundation. His research interests include inverse problems, sparse representation modeling and machine learning.<\/p>\n<p><strong>Zoom link:<\/strong> https:\/\/wse.zoom.us\/j\/94601022340<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Location: Olin 305 When:\u00a0April 25th\u00a0at 1:30 p.m. Title: Yay, my deep network works! But.. what did it learn? Abstract: Modern machine learning methods are revolutionizing what we can do with&hellip;<\/p>\n","protected":false},"author":69,"featured_media":0,"template":"","meta":{"_acf_changed":false,"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[260],"class_list":["post-48037","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-seminars-and-endowed-lectures","cat_seminars-and-endowed-lectures"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AMS Weekly Seminar | Assistant Professor Jeremias Sulam | Department of Applied Mathematics and Statistics<\/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:\/\/engineering.jhu.edu\/ams\/event\/ams-weekly-seminar-jeremias-sulam\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AMS Weekly Seminar | Assistant Professor Jeremias Sulam | Department of Applied Mathematics and Statistics\" \/>\n<meta property=\"og:description\" content=\"Location: Olin 305 When:\u00a0April 25th\u00a0at 1:30 p.m. Title: Yay, my deep network works! 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