{"id":18202,"date":"2026-03-20T12:43:15","date_gmt":"2026-03-20T16:43:15","guid":{"rendered":"https:\/\/www.rochester.edu\/coe\/?p=18202"},"modified":"2026-03-20T12:59:15","modified_gmt":"2026-03-20T16:59:15","slug":"advancing-wavefront-sensor-performance-via-deep-learning","status":"publish","type":"post","link":"https:\/\/www.rochester.edu\/coe\/advancing-wavefront-sensor-performance-via-deep-learning\/","title":{"rendered":"Advancing Wavefront Sensor Performance via Deep Learning"},"content":{"rendered":"<p>PI Researcher: Drew Maywar, Rochester Institute of Technology<br \/>\nCompany Partner: RAM Photonics<\/p>\n<p>The optical wavefront the transverse spatial variation of the optical phase \u2014 plays a significant role in the propagation of optical beams and in the characterization of optical components &amp; systems. Wavefront sensors are therefore important instruments for the worldwide optics community and especially for our regional optics ecosystem. This project seeks to apply deep learning to advance the performance of the RAM Photonics QuantoPhase wavefront sensor, harvesting &amp; learning aspects of raw shearing interferograms including in the realm of pre-formed interferograms to allow for previously prohibited sensor design.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>PI Researcher: Drew Maywar, Rochester Institute of Technology Company Partner: RAM Photonics The optical wavefront the transverse spatial variation of the optical phase \u2014 plays a significant role in the&hellip;<\/p>\n","protected":false},"author":122,"featured_media":16932,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[222],"tags":[232],"class_list":["post-18202","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research-partnerships","tag-2026-funded"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - 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