{"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":"2026-08-10T12:22:46","modified_gmt":"2026-08-10T16:22:46","slug":"grace-julius","status":"publish","type":"profiles","link":"https:\/\/oge.mit.edu\/msrp\/profiles\/grace-julius\/","title":{"rendered":"Grace Julius"},"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\/Julius-Grace.jpg\" alt=\"\" class=\"wp-image-5607\" style=\"aspect-ratio:1;object-fit:cover;width:200px;height:auto\" srcset=\"https:\/\/oge.mit.edu\/msrp\/wp-content\/uploads\/sites\/2\/2026\/05\/Julius-Grace.jpg 400w, https:\/\/oge.mit.edu\/msrp\/wp-content\/uploads\/sites\/2\/2026\/05\/Julius-Grace-200x300.jpg 200w\" sizes=\"auto, (max-width: 400px) 100vw, 400px\" \/><figcaption class=\"wp-element-caption\">by Corban Swain<\/figcaption><\/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>Lincoln University<\/strong><br>Faculty Advisor: Prof. Paul Liang<br>Research Supervisor: Awu Chen<br>Deparment: Electrical Enginnering and Computer Science<\/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\">Born and raised in Nigeria, Grace Julius is a senior computer science major at Lincoln<br>University of Pennsylvania. With a growing motivation to find the uncommon at the<br>intersection of cybersecurity, policy, and human-computer interaction, Grace aspires to earn<br>her doctorate one day, driven not by title but by her never-ending passion for humanity. With<br>rapid technological development, especially in the age of AI, Grace is inspired to advocate for<br>the inclusion of humans in the design process and security of new technology, while working<br>to reduce the digital divide that already exists between nations. She is not all about books and<br>research; she always finds time to put a smile on people&#8217;s faces through jokes, volunteering, or<br>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 Julius1, Paul Liang2,3<br>1Department of Computer Science, Lincoln University of Pennsylvania<br>2Department of Media Arts and Science, Massachusetts Institute of Technology<br>3Department of Electrical Engineering and Computer Science, Massachusetts Institute<br>of Technology<\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><br>Artificial intelligence can now see, talk, and hear, but smell remains unsolved. Current systems<br>detect a substance only while present and not once removed. The human nose, in contrast,<br>can identify food cooked minutes earlier from residual traces. We ask whether a gas sensor<br>array can identify a substance from its post removal signal, and for how long. Prior systems,<br>including SmellNet, sense sources present during measurement and never record removal<br>timing. We built a pipeline that logs removal timestamps and collected data from a custom<br>sensor array across four substances (black pepper, nutmeg, paprika, rosemary), yielding over<br>70 removal events. Preliminary MEMS gas sensor readings for all four substances stayed<br>within 2% of pre removal levels for five minutes post removal, rather than decaying toward<br>baseline. This flat signal could reflect real residual odor or simply slow sensor reset, a known<br>MOx\/electrochemical behavior. Determining which explanation holds, and whether the signal<br>identifies the substance, is what our current models are built to test. We compare several<br>model architectures on post removal windows and will test physical property correlations<br>to distinguish the two. Results are specific to these four substances, with implications for<br>environmental monitoring, food safety, and forensic sensing.<\/p>\n","protected":false},"featured_media":5448,"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":4,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles\/5113\/revisions"}],"predecessor-version":[{"id":5718,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles\/5113\/revisions\/5718"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/media\/5448"}],"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}]}}