{"id":5137,"date":"2026-05-13T15:08:00","date_gmt":"2026-05-13T19:08:00","guid":{"rendered":"https:\/\/oge.mit.edu\/msrp\/?post_type=profiles&#038;p=5137"},"modified":"2026-10-09T13:33:20","modified_gmt":"2026-10-09T17:33:20","slug":"michael-owusu","status":"publish","type":"profiles","link":"https:\/\/oge.mit.edu\/msrp\/profiles\/michael-owusu\/","title":{"rendered":"Michael Owusu"},"content":{"rendered":"\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:100%\">\n<div class=\"wp-block-group is-nowrap is-layout-flex wp-container-core-group-is-layout-8f761849 wp-block-group-is-layout-flex\">\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"300\" src=\"https:\/\/oge.mit.edu\/msrp\/wp-content\/uploads\/sites\/2\/2026\/05\/Owusu-Michael-e1791566963604.jpg\" alt=\"Owusu, Michael\" class=\"wp-image-5623\" \/><\/figure>\n\n\n\n<p class=\"wp-container-content-69bc4bdf wp-block-paragraph\"><strong>MIT Department: <\/strong><br>Electrical Engineering and Computer Science<br><br><strong>Faculty Mentor<\/strong>: <br>Marzyeh Ghassemi<br><br><strong>Research Supervisor: <\/strong><br>Kumail Hamoud, Hara Moraitaki<strong><br><\/strong><br><strong>Undergraduate Institution:<\/strong><br>Morehouse College<strong><br><\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Biography<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Michael Owusu is a Computer Science major at Morehouse College from Kumasi, Ghana. He is interested in artificial intelligence and machine learning. This summer, he is conducting research at MIT under the mentorship of Dr. Marzyeh Ghassemi, where he studies multilingual safety in large language models. He enjoys building software, attending research conferences, and learning about the problems other researchers are working on. When he gets the chance, he also enjoys sharing his own work.<\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><strong>Negation Understanding in Vision-Language Models: A Twi (Akan) Extension of NegBench<br><\/strong>Michael Owusu\u00b9, Kumail Alhamoud\u00b2, Hara Moraitaki\u00b2 and Marzyeh Ghassemi\u00b2<br>\u00b9Department of Computer Science, Morehouse College<br>\u00b2Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"has-text-align-left wp-block-paragraph\">Vision-language models power image search, but they ignore negation: asked for \u201ca photo with no cars,\u201d they return cars. Such errors reverse meaning, which matters where absence is the message, e.g., a scan showing \u201cno evidence of pneumonia.\u201d NegBench (Alhamoud et al., 2025) documented this in English and showed that finetuning on synthetic negated captions recovers 28 points; whether the failure or the fix holds elsewhere was untested. Within an ongoing HealthyML project extending NegBench across languages, we built the first negation benchmark for Twi, an unevaluated Ghanaian language: 5,914 human-verified multiple-choice questions and 500 captions translated by a native speaker (first author). Across ten models and five languages, reading a language and understanding negation prove separate abilities. NLLB-CLIP matches 14.0% of Twi captions to their images, against 61.0% in English, yet answers 1.2% of Twi negation questions correctly, below 25% chance. The fix does not transfer: one finetuned model drops from 54.5% in English to 17.5% in Twi, below its untuned baseline. Measuring image-text similarity, the object a caption names matters 8\u201339x more than whether it is negated: a matching shortcut, not missing data. Negation must be addressed in training, and without benchmarks this failure stays invisible.<\/p>\n","protected":false},"featured_media":5623,"template":"","profile_category":[25],"class_list":["post-5137","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\/5137","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":4,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles\/5137\/revisions"}],"predecessor-version":[{"id":6058,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles\/5137\/revisions\/6058"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/media\/5623"}],"wp:attachment":[{"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/media?parent=5137"}],"wp:term":[{"taxonomy":"profile_category","embeddable":true,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profile_category?post=5137"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}