{"id":4242,"date":"2021-12-16T16:56:04","date_gmt":"2021-12-16T21:56:04","guid":{"rendered":"https:\/\/www.rochester.edu\/coe\/?page_id=4242"},"modified":"2026-03-09T12:18:32","modified_gmt":"2026-03-09T16:18:32","slug":"current-projects","status":"publish","type":"page","link":"https:\/\/www.rochester.edu\/coe\/work-with-us\/current-projects\/","title":{"rendered":"Current CoE-funded Projects"},"content":{"rendered":"<p>The CoE proudly sponsors the research of faculty and partner companies. The Center&#8217;s funding allows researchers to produce innovative and visionary approaches to data science in a variety of areas and industries. We currently are funding these partnerships and are looking forward to seeing the results of the research and the positive economic impact that it will have on New York State and the world of data science.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The CoE proudly sponsors the research of faculty and partner companies. The Center&#8217;s funding allows researchers to produce innovative and visionary approaches to data science in a variety of areas&hellip;<\/p>\n","protected":false},"author":62,"featured_media":0,"parent":11272,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":"","_members_access_role":[],"_members_access_error":""},"class_list":["post-4242","page","type-page","status-publish","hentry"],"acf":{"lead_in":"","display_title":"","subtitle":"","call_to_action":"","hero_image_placement":"no_image","hero_media":"images","hero_pattern_option":"","hero_video":"","hero_image_landing":"","hero_image_landing_two":"","hero_image_landing_three":"","content_modules":[{"acf_fc_layout":"card_grid","anchor":"","css_class":"","title":"<h2>Currently Funded Research Collaborations<\/h2>","description":"","background_color":"#FFFFFF","width":"width-large","columns":"1","cards":[{"card_title":"<img class=\"aligncenter wp-image-17642 size-medium\" src=\"https:\/\/www.rochester.edu\/coe\/wp-content\/uploads\/2026\/02\/RAM-Logo-blue-gradients-large-2-300x78.png\" alt=\"\" width=\"300\" height=\"78\" \/><strong style=\"font-size: 1.25rem;\">\r\nAdvancing Wavefront Sensor Performance via Deep Learning\r\n<\/strong>","card_text":"<img class=\"wp-image-16932 alignleft\" src=\"https:\/\/www.rochester.edu\/coe\/wp-content\/uploads\/2025\/06\/DrewMaywar-300x300.jpeg\" alt=\"\" width=\"214\" height=\"214\" \/>PI Researcher: <a href=\"https:\/\/www.rit.edu\/directory\/dnmiee-drew-maywar\">Drew Maywar\u00a0<\/a>\r\nRochester Institute of Technology\r\n\r\nThe optical wavefront \u2014 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.\r\n\r\n&nbsp;","card_background_color":"#00205b","card_hover_color":"transparent","card_link":"","card_hover_arrow":"","card_image":"","card_image_size":"","card_image_placement":"","card_image_hover_state":"hover_fade","card_image_link":""},{"card_title":"<strong><img class=\"aligncenter wp-image-6332 size-medium\" src=\"https:\/\/www.rochester.edu\/coe\/wp-content\/uploads\/2023\/01\/L3Harris_Technologies_logo.svg_-300x64.png\" alt=\"\" width=\"300\" height=\"64\" \/>Enabling Ontology Development in Graph Databases\r\n<\/strong>","card_text":"<img class=\"size-medium wp-image-17672 alignleft\" src=\"https:\/\/www.rochester.edu\/coe\/wp-content\/uploads\/2026\/02\/CarlosRivero-300x300.jpeg\" alt=\"\" width=\"300\" height=\"300\" \/>\r\n\r\nPI Researcher: <a href=\"https:\/\/www.rit.edu\/directory\/crrvcs-carlos-r-rivero\">Carlos Rivero<\/a>\r\nRochester Institute of Technology\r\n\r\nL3Harris currently deploys ontologies that are experiencing data limitations, which compromise the performance and scalability of the applications. Thus, there is motivation to find an approach to migrate ontologies into a graph database that can provide performance, flexibility, and scalability benefits, particularly for applications involving complex and highly interconnected data. This project aims to investigate methods to enable ontology development into Neo4j, an open-source graph database, thereby empowering users to extract valuable insights from existing ontologies and expanding queries to extensive multi-domain data sets. Furthermore, the project aims to help users retrieve knowledge from existing ontologies, even when the users are not computing experts.","card_background_color":"#f4f4f4","card_hover_color":"transparent","card_link":"","card_hover_arrow":"","card_image":"","card_image_size":"","card_image_placement":"","card_image_hover_state":"hover_fade","card_image_link":""},{"card_title":"<strong>Controllable ASL Generation to Support Communication between Hearing Parents and DHH Children<\/strong>","card_text":"<img class=\"size-full wp-image-17682 alignleft\" src=\"https:\/\/www.rochester.edu\/coe\/wp-content\/uploads\/2026\/02\/bai_zhen.jpg\" alt=\"\" width=\"132\" height=\"163\" \/>\r\n\r\nPI Researcher: <a href=\"https:\/\/www.cs.rochester.edu\/people\/faculty\/bai-zhen\/index.html\">Zhen Bai<\/a>\r\n糖心传媒\r\n\r\n&nbsp;\r\n\r\nOver 90% of Deaf and Hard of Hearing (DHH) children in the US are born to hearing families. This put DHH children at several risk of language deprivation that leads to inreversible and lifelong impact on linguistic, cognitive, and socio-emotional development. This project aims to design and develop advanced American Sign Langauge (ASL) generation technologies that support hearing parents to simultaneously sign and learn ASL during face-to-face interaction. The central innovation is the high controability of ASL video generation that leverages cutting-edge Generative AI technologies and nuanced needs of hearing parents in providing ASL on-the-fly to DHH children.\r\n\r\n&nbsp;","card_background_color":"#00205b","card_hover_color":"transparent","card_link":"","card_hover_arrow":"","card_image":"","card_image_size":"","card_image_placement":"","card_image_hover_state":"hover_fade","card_image_link":""},{"card_title":"<strong><img class=\"wp-image-17652  alignleft\" src=\"https:\/\/www.rochester.edu\/coe\/wp-content\/uploads\/2026\/02\/sateglogo_v3-300x210.png\" alt=\"\" width=\"186\" height=\"130\" \/><\/strong><strong style=\"font-size: 1.125rem;\">AI-Driven Modeling and Data Analytics for Optimizing Thermoelectric Generators in Weather-Resilient and Maritime Applications<\/strong>\r\n\r\n&nbsp;","card_text":"<img class=\"wp-image-17942 size-medium alignleft\" src=\"https:\/\/www.rochester.edu\/coe\/wp-content\/uploads\/2026\/03\/20230412_zhihengXu_zxxeieLR-200x300.jpg\" alt=\"\" width=\"200\" height=\"300\" \/>\r\n\r\nPI Researcher: <a href=\"https:\/\/www.rit.edu\/directory\/zxxeie-zhiheng-xu\">Zhiheng Xu<\/a>\r\nRochester Institute of Technology\r\n\r\nThis project will develop AI-driven tools to optimize the deployment and utilization of thermoelectric generators (TEGs) developed by SATEG Corp. By integrating experimental device outputs, environmental datasets, and calibrated computational models, we will build predictive frameworks that capture the coupling between device size, ambient conditions, and power output. Machine learning methods will enable two complementary tools: a sizing module that streamlines customer project development and a data analytics platform that transforms deployment data into insights on sea ice, ocean currents, and weather patterns. These tools will strengthen SATEG\u2019s commercialization and advance resilient energy solutions in New York.","card_background_color":"#f4f4f4","card_hover_color":"transparent","card_link":"","card_hover_arrow":"","card_image":"","card_image_size":"","card_image_placement":"","card_image_hover_state":"hover_fade","card_image_link":""},{"card_title":"<img class=\"aligncenter wp-image-17662 \" src=\"https:\/\/www.rochester.edu\/coe\/wp-content\/uploads\/2026\/02\/rewriterguitarlogo-295x300.png\" alt=\"\" width=\"178\" height=\"181\" \/> \u00a0<strong> Enhancing Guitar Practice and Songwriting with Automatic Guitar Tablature Transcription<\/strong>","card_text":"<img class=\"wp-image-6042 size-medium alignleft\" src=\"https:\/\/www.rochester.edu\/coe\/wp-content\/uploads\/2022\/10\/zhiyaoduanheadshot-240x300.jpeg\" alt=\"Headshot Photo of Zhiyao Duan\" width=\"240\" height=\"300\" \/>PI Researcher: <a href=\"https:\/\/www.hajim.rochester.edu\/ece\/people\/faculty\/duan_zhiyao\/index.html\">Zhiyao Duan<\/a>\r\n糖心传媒\r\n\r\n&nbsp;\r\n\r\n&nbsp;\r\n<p class=\"p1\">Guitar is among the most popular musical instruments thanks to its versatility, convenience, and affordability. Guitarists often prefer to use guitar tablature when reading, writing, learning, and sharing music. This project aims to develop highly accurate automatic Guitar Tablature Transcription (GTT) algorithms that can convert guitar recordings into guitar tablature. The key idea is to train advanced neural network models on massive amounts of synthetic guitar data to overcome current bottlenecks data scarsity and model simplicity. The developed algorithms will be deployed to the industry partner ReWriter\u2019s product to enhance guitar practice and songwriting experiences.<\/p>\r\n&nbsp;","card_background_color":"#00205b","card_hover_color":"transparent","card_link":"","card_hover_arrow":"","card_image":"","card_image_size":"","card_image_placement":"","card_image_hover_state":"hover_fade","card_image_link":""}]}]},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Current CoE-funded Projects - Center of Excellence in Data Science and Artificial Intelligence<\/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.rochester.edu\/coe\/work-with-us\/current-projects\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Current CoE-funded Projects - Center of Excellence in Data Science and Artificial Intelligence\" \/>\n<meta property=\"og:description\" content=\"The CoE proudly sponsors the research of faculty and partner companies. 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