{"id":228902,"date":"2026-01-13T05:22:51","date_gmt":"2026-01-13T11:22:51","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2026\/01\/from-brain-scans-to-alloys-teaching-ai-to-make-sense-of-complex-research-data"},"modified":"2026-01-13T05:22:51","modified_gmt":"2026-01-13T11:22:51","slug":"from-brain-scans-to-alloys-teaching-ai-to-make-sense-of-complex-research-data","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2026\/01\/from-brain-scans-to-alloys-teaching-ai-to-make-sense-of-complex-research-data","title":{"rendered":"From brain scans to alloys: Teaching AI to make sense of complex research data"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/from-brain-scans-to-alloys-teaching-ai-to-make-sense-of-complex-research-data.jpg\"><\/a><\/p>\n<p>Artificial intelligence (AI) is increasingly used to analyze medical images, materials data and scientific measurements, but many systems struggle when real-world data do not match ideal conditions. Measurements collected from different instruments, experiments or simulations often vary widely in resolution, noise and reliability. Traditional machine-learning models typically assume those differences are negligible\u2014an assumption that can limit accuracy and trustworthiness.<\/p>\n<p>To address this issue, Penn State researchers have developed a new artificial intelligence framework with potential implications for fields ranging from Alzheimer\u2019s disease research to advanced materials design. The approach, called ZENN and detailed in a study that was <a href=\"https:\/\/pnas.org\/doi\/10.1073\/pnas.2511227122\" target=\"_blank\">featured<\/a> as a showcase in the <i>Proceedings of the National Academy of Sciences<\/i>, teaches AI models to recognize and adapt to hidden differences in data quality rather than ignoring them.<\/p>\n<p>ZENN, short for Zentropy-Embedded Neural Networks, was developed by Shun Wang, postdoctoral scholar of materials science and engineering; Wenrui Hao, professor of mathematics, Zi-Kui Liu, professor of materials science and engineering, and Shunli Shang, research professor of materials science and engineering.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Artificial intelligence (AI) is increasingly used to analyze medical images, materials data and scientific measurements, but many systems struggle when real-world data do not match ideal conditions. Measurements collected from different instruments, experiments or simulations often vary widely in resolution, noise and reliability. Traditional machine-learning models typically assume those differences are negligible\u2014an assumption that can [\u2026]<\/p>\n","protected":false},"author":427,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11,6],"tags":[],"class_list":["post-228902","post","type-post","status-publish","format-standard","hentry","category-biotech-medical","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/228902","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/427"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=228902"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/228902\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=228902"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=228902"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=228902"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}