{"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":"2026-08-13T14:52:25","modified_gmt":"2026-08-13T18:52:25","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":"<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\/Lopes-Ana.jpg\" alt=\"by Corban Swain\" class=\"wp-image-5611\" style=\"aspect-ratio:1;object-fit:cover;width:200px;height:auto\" srcset=\"https:\/\/oge.mit.edu\/msrp\/wp-content\/uploads\/sites\/2\/2026\/05\/Lopes-Ana.jpg 400w, https:\/\/oge.mit.edu\/msrp\/wp-content\/uploads\/sites\/2\/2026\/05\/Lopes-Ana-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>Bowdoin College<\/strong><br>Faculty Advisor: Prof. Paul Liang<br>Department: Media Arts and Sciences<\/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\">Ana Santos Lopes is a rising junior at Bowdoin College pursuing a combined major in<br>Computer Science and Mathematics. Originally from Rio Grande do Norte (Brazil), she is<br>passionate about combining technology and neuroscience to address real-world challenges,<br>expand access to educational opportunities, and improve healthcare systems. During her first<br>research experience, Ana collaborated with Brazilian researchers to develop tools that integrated<br>virtual reality, EEG signals, and assistive technologies to improve rehabilitation for patients<br>with spinal cord injuries. After this experience, she interned at EPFL in Switzerland, where she<br>worked on EMG analysis and processing, applying computational methods to extract patterns<br>from physiological data. Currently, Ana is interested in understanding how LLMs can help people<br>learn more effectively, focusing on improving education while expanding opportunities for<br>underprivileged communities. Beyond her academic and research pursuits, Ana enjoys writing,<br>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>Ana Santos Lopes1 and Paul Liang2<\/strong><br>1Department of Computer Science and Mathematics, Bowdoin College<br>2Department of Media Arts and Sciences, Massachusetts Institute of Technology<\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><br>The ability to smell is important in how humans perceive and interact with their environment,<br>as odor-associated chemical signals can persist after their source has been removed, with<br>potential applications in forensic science and allergy detection. Despite its importance, the<br>detectability of these signatures after source removal remains underexplored. This study<br>investigates whether machine learning (ML) models can classify spices using sensor data<br>collected after source removal. To address this question, sensor data were collected from<br>four ground spices (black pepper, nutmeg, paprika, and rosemary) along with environmental<br>controls using a box-shaped sensor array. Each experiment included an initial 2 minute<br>ambient recording, a 5 minute recording with the substance inside the box, and a post-removal<br>recording lasting either 5 minutes or up to 8 hours. The data collection comprised 104 shortruns<br>and 19 long runs. ML classifiers were trained on short-runs and evaluated in two settings:<br>short-to-short and short-to-long. An Extra Trees classifier achieved the highest overall accuracy<br>in the short-to-short evaluation, reaching 97.5%. The short-to-long examined classification<br>performance as sensor signals evolved after source removal. These findings demonstrate<br>that classification remains possible for a period after source removal, supporting further<br>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":3,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles\/5341\/revisions"}],"predecessor-version":[{"id":5838,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles\/5341\/revisions\/5838"}],"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}]}}