{"id":5341,"date":"2026-05-13T14:59:06","date_gmt":"2026-05-13T18:59:06","guid":{"rendered":"https:\/\/oge.mit.edu\/msrp\/?post_type=profiles&#038;p=5341"},"modified":"2026-10-09T16:55:04","modified_gmt":"2026-10-09T20:55:04","slug":"ana-santos-lopes","status":"publish","type":"profiles","link":"https:\/\/oge.mit.edu\/msrp\/profiles\/ana-santos-lopes\/","title":{"rendered":"Ana Santos Lopes"},"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\/Lopes-Ana-e1791579115563.jpg\" alt=\"Santos Lopes, Ana\" class=\"wp-image-5611\" \/><\/figure>\n\n\n\n<p class=\"wp-container-content-69bc4bdf wp-block-paragraph\"><strong>MIT Department: <\/strong><br>Media Arts and Sciences<br><br><strong>Faculty Mentor<\/strong>: <br>Paul Liang<br><br><strong>Undergraduate Institution:<\/strong><br>Bowdoin 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\">Ana Santos Lopes is a rising junior at Bowdoin College pursuing a combined major in Computer Science and Mathematics. Originally from Rio Grande do Norte (Brazil), she is passionate about combining technology and neuroscience to address real-world challenges, expand access to educational opportunities, and improve healthcare systems. During her first research experience, Ana collaborated with Brazilian researchers to develop tools that integrated virtual reality, EEG signals, and assistive technologies to improve rehabilitation for patients with spinal cord injuries. After this experience, she interned at EPFL in Switzerland, where she worked on EMG analysis and processing, applying computational methods to extract patterns from physiological data. Currently, Ana is interested in understanding how LLMs can help people learn more effectively, focusing on improving education while expanding opportunities for underprivileged communities. Beyond her academic and research pursuits, Ana enjoys writing, reading, and having fun with tech development. She hopes to build a career in Human-Centered AI.<\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><br><strong>Machine Learning Classification of Spices From Sensor Data After Odor<br>Source Removal<br><\/strong>Ana Santos Lopes<sup>1<\/sup> and Paul Liang<sup>2<\/sup><br><sup>1<\/sup>Department of Computer Science and Mathematics, Bowdoin College<br><sup>2<\/sup>Department of Media Arts and Sciences, Massachusetts Institute of Technology<\/p>\n\n\n\n<p class=\"has-text-align-left wp-block-paragraph\"><br>The ability to smell is important in how humans perceive and interact with their environment, as odor-associated chemical signals can persist after their source has been removed, with potential applications in forensic science and allergy detection. Despite its importance, the detectability of these signatures after source removal remains underexplored. This study investigates whether machine learning (ML) models can classify spices using sensor data collected after source removal. To address this question, sensor data were collected from four ground spices (black pepper, nutmeg, paprika, and rosemary) along with environmental controls using a box-shaped sensor array. Each experiment included an initial 2 minute ambient recording, a 5 minute recording with the substance inside the box, and a post-removal recording lasting either 5 minutes or up to 8 hours. The data collection comprised 104 shortruns and 19 long runs. ML classifiers were trained on short-runs and evaluated in two settings: short-to-short and short-to-long. An Extra Trees classifier achieved the highest overall accuracy in the short-to-short evaluation, reaching 97.5%. The short-to-long examined classification performance as sensor signals evolved after source removal. These findings demonstrate that classification remains possible for a period after source removal, supporting further investigation of odor signature persistence.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"featured_media":5611,"template":"","profile_category":[25],"class_list":["post-5341","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\/5341","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\/5341\/revisions"}],"predecessor-version":[{"id":6077,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles\/5341\/revisions\/6077"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/media\/5611"}],"wp:attachment":[{"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/media?parent=5341"}],"wp:term":[{"taxonomy":"profile_category","embeddable":true,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profile_category?post=5341"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}