{"id":5113,"date":"2026-05-13T15:08:35","date_gmt":"2026-05-13T19:08:35","guid":{"rendered":"https:\/\/oge.mit.edu\/msrp\/?post_type=profiles&#038;p=5113"},"modified":"2026-10-09T10:56:32","modified_gmt":"2026-10-09T14:56:32","slug":"grace-julius","status":"publish","type":"profiles","link":"https:\/\/oge.mit.edu\/msrp\/profiles\/grace-julius\/","title":{"rendered":"Grace Julius"},"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\/Julius-Grace-e1791557453218.jpg\" alt=\"Julius, Grace\" class=\"wp-image-5607\" \/><\/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>Paul Liang<br><br><strong>Research Supervisor: <\/strong><br>Awu Chen<br><br><strong>Undergraduate Institution:<\/strong><br>Lincoln University<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\">Born and raised in Nigeria, Grace Julius is a senior computer science major at Lincoln University of Pennsylvania. With a growing motivation to find the uncommon at the intersection of cybersecurity, policy, and human-computer interaction, Grace aspires to earn her doctorate one day, driven not by title but by her never-ending passion for humanity. With rapid technological development, especially in the age of AI, Grace is inspired to advocate for the inclusion of humans in the design process and security of new technology, while working to reduce the digital divide that already exists between nations. She is not all about books and research; she always finds time to put a smile on people&#8217;s faces through jokes, volunteering, or any other means. She loves to cook, watch movies, and go on light jogs.<\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><br><strong>Smelling the Past: Detecting Residual Odor Signatures with Sensor- Based<br>Machine Olfaction<\/strong><br>Grace Julius<sup>1<\/sup>, Paul Liang<sup>2,3<\/sup><br><sup>1<\/sup>Department of Computer Science, Lincoln University of Pennsylvania<br><sup>2<\/sup>Department of Media Arts and Science, Massachusetts Institute of Technology<br><sup>3<\/sup>Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology<\/p>\n\n\n\n<p class=\"has-text-align-left wp-block-paragraph\"><br>Artificial intelligence can now see, talk, and hear, but smell remains unsolved. Current systems detect a substance only while present and not once removed. The human nose, in contrast, can identify food cooked minutes earlier from residual traces. We ask whether a gas sensor array can identify a substance from its post removal signal, and for how long. Prior systems, including SmellNet, sense sources present during measurement and never record removal timing. We built a pipeline that logs removal timestamps and collected data from a custom sensor array across four substances (black pepper, nutmeg, paprika, rosemary), yielding over 70 removal events. Preliminary MEMS gas sensor readings for all four substances stayed within 2% of pre removal levels for five minutes post removal, rather than decaying toward baseline. This flat signal could reflect real residual odor or simply slow sensor reset, a known MOx\/electrochemical behavior. Determining which explanation holds, and whether the signal identifies the substance, is what our current models are built to test. We compare several model architectures on post removal windows and will test physical property correlations to distinguish the two. Results are specific to these four substances, with implications for environmental monitoring, food safety, and forensic sensing.<\/p>\n","protected":false},"featured_media":5607,"template":"","profile_category":[25],"class_list":["post-5113","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\/5113","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":7,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles\/5113\/revisions"}],"predecessor-version":[{"id":6026,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles\/5113\/revisions\/6026"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/media\/5607"}],"wp:attachment":[{"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/media?parent=5113"}],"wp:term":[{"taxonomy":"profile_category","embeddable":true,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profile_category?post=5113"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}