{"id":5045,"date":"2026-05-13T15:09:45","date_gmt":"2026-05-13T19:09:45","guid":{"rendered":"https:\/\/oge.mit.edu\/msrp\/?post_type=profiles&#038;p=5045"},"modified":"2026-08-10T11:43:46","modified_gmt":"2026-08-10T15:43:46","slug":"trey-davis","status":"publish","type":"profiles","link":"https:\/\/oge.mit.edu\/msrp\/profiles\/trey-davis\/","title":{"rendered":"Trey Davis"},"content":{"rendered":"<div class=\"wp-block-image\">\n<figure class=\"alignleft size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"400\" height=\"599\" src=\"https:\/\/oge.mit.edu\/msrp\/wp-content\/uploads\/sites\/2\/2026\/05\/Davis.-Trey.jpg\" alt=\"by Corban Swain\" class=\"wp-image-5589\" style=\"aspect-ratio:1;object-fit:cover;width:200px;height:auto\" srcset=\"https:\/\/oge.mit.edu\/msrp\/wp-content\/uploads\/sites\/2\/2026\/05\/Davis.-Trey.jpg 400w, https:\/\/oge.mit.edu\/msrp\/wp-content\/uploads\/sites\/2\/2026\/05\/Davis.-Trey-200x300.jpg 200w\" sizes=\"auto, (max-width: 400px) 100vw, 400px\" \/><\/figure>\n<\/div>\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<p class=\"wp-block-paragraph\"><strong>University of Michigan<\/strong><br>Faculty Advisor: Prof. Andreea Bobu<br>Research Supervisor: Nathan Dennler<br>Department: Aeronautics and Astronautics<\/p>\n<\/div><\/div>\n\n\n\n<div style=\"height:0px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Biography<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Trey Davis studies how robots can work with creatives. As an artist working primarily<br>with ceramics and other sculptural media, he uses his robotics major at the University of<br>Michigan to better understand the animosity his community feels toward AI development. At<br>Michigan, he works with Dr. Patr\u00edcia Alves-Oliveira to create robotic assistants that leverage<br>creative psychology to scaffold thinking through thoughtful questions. At MIT, he is studying<br>robot learning using multimodal inputs, with an eye toward applying similar algorithms to<br>create robots that adapt to highly variable artist preferences. He believes that in understanding<br>how robots can fit into the creative process, we come closer to understanding how we can<br>retain our humanity when working with technology. In his PhD, he aims to challenge his<br>understanding of these questions and prompt his discipline to think more critically about the<br>proper role of robotics in future society.<\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><br><strong>Voice Affect Co-Personalization for Robot Preference Learning<\/strong><br>Trey Davis1,2, Nathaniel Dennler2 and Andreea Bobu2<br>1Department of Robotics, University Michigan \u2013 Ann Arbor<br>2Department of Aeronautics and Astronautics, Massachusetts Institute of Technology<\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><br>In order for robots to work effectively with humans, they need to adapt to a range of human<br>preferences. Yet, real human feedback is limited and ambiguous, making efficient adaptation<br>a core challenge for preference learning. The emotional, or affective, component of speech is<br>an implicit and useful feedback signal for robot learning. Yet, integrating voice affect into an<br>online robot learning framework remains under-explored. Through the analysis of voice data<br>in a previous robot learning experiment, we found that voice affect is situational, dependent<br>on the user and task framing. We further hypothesize that humans adapt their affect throughout<br>a task to elicit better performance from the robot. Bringing those insights into voice-based<br>learning systems, we formulate two algorithms that incorporate vocal affect into a collaborative<br>robot task. We test these algorithms and our hypothesis in a pilot user study. While the existing<br>understanding of voice affect frames it as a hidden ground truth to be uncovered by robots,<br>we expect to see more efficient use of vocal feedback by conceptualizing the decoding of this<br>signal as a collaborative human-robot effort to construct a shared communication channel.<\/p>\n","protected":false},"featured_media":5400,"template":"","profile_category":[25],"class_list":["post-5045","profiles","type-profiles","status-publish","has-post-thumbnail","hentry","profile_category-2026-interns"],"acf":[],"_links":{"self":[{"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles\/5045","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles"}],"about":[{"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/types\/profiles"}],"version-history":[{"count":3,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles\/5045\/revisions"}],"predecessor-version":[{"id":5686,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles\/5045\/revisions\/5686"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/media\/5400"}],"wp:attachment":[{"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/media?parent=5045"}],"wp:term":[{"taxonomy":"profile_category","embeddable":true,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profile_category?post=5045"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}