{"id":4576,"date":"2025-11-03T10:27:01","date_gmt":"2025-11-03T15:27:01","guid":{"rendered":"https:\/\/oge.mit.edu\/msrp\/?post_type=profiles&p=4576"},"modified":"2025-12-09T12:08:12","modified_gmt":"2025-12-09T17:08:12","slug":"jessica-slaughter-2","status":"publish","type":"profiles","link":"https:\/\/oge.mit.edu\/msrp\/profiles\/jessica-slaughter-2\/","title":{"rendered":"Jessica Slaughter"},"content":{"rendered":"
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MIT Department:<\/strong> Materials Science and Engineering
Faculty Mentor<\/strong>: Prof. Polina Anikeeva
Research Supervisor:<\/strong> Yeji Kim
Undergraduate Institution:<\/strong> University of Maryland, Baltimore County
Website<\/strong>:<\/p>\n<\/div><\/div>\n\n\n\n

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Biography<\/strong><\/h4>\n\n\n\n

Jessica Slaughter is a junior computer engineering major and Meyerhoff Scholar at theUniversity of Maryland, Baltimore County. Passionate about improving global healthcare, she focuses on developing advanced yet affordable biomedical devices. At MIT\u2019s Bioelectronics Group,Jessica is training a machine learning model to analyze mouse behavior in response to wireless neural modulation from magnetoelectric nanodiscs. Last summer, in the Furst Lab at MIT, she published a method to improve the long-term stability of DNA-based biosensors.At UMBC\u2019sMartenLab, Jessica develops bioinformatic tools to derive meaningful biological insights from system-scale, dynamic omics data. Beyond research, she is dedicated to community building andSTEM education. She founded UMBC\u2019s BMES chapter, serves as IEEE vice president, is a NSBE senator, and volunteers with STEMcx and UMB CURE to mentor K\u201312 students in Baltimore.Jessica aspires to pursue a Ph.D. focused on designing low-cost, ML-integrated medical devices that improve patient outcomes globally.<\/p>\n\n\n\n

Abstract<\/strong><\/h4>\n\n\n\n

Computational Pipeline for Automated Analysis on Place Preference Assays in Neuromodulation Studies<\/strong><\/p>\n\n\n\n

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Jessica Slaughter1<\/sup>, Ye Ji Kim2,3<\/sup>, and Polina Anikeeva<\/strong><\/p>\n\n\n\n

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1<\/sup>Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County<\/p>\n\n\n\n

2<\/sup>Department of Materials Science and Engineering, Massachusetts Institute of Technology<\/p>\n\n\n\n

3<\/sup>Research Laboratory of Electronics, Massachusetts Institute of Techbology<\/p>\n\n\n\n

4<\/sup>McGovern Institute for Brain Research, Massachusetts Institute of Technology<\/p>\n\n\n\n

5<\/sup>Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology<\/p>\n\n\n\n

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Place preference (PP) assays assess reward or aversion responses to neuromodulation based on the time an animal spends in two stimulation chambers separated by a neutral zone. While tools such as BehaviorCloud and ANY-maze automate the analysis, they struggle to trace animals under low-light and low-contrast conditions, which are necessary to increase experiment flexibility and broaden the scope of neuromodulation research. For example, neuromodulation using magnetic nanoparticles (MNPs) is an emerging method due to its wireless, minimally invasive nature. Still, magnetic coils required for stimulation reduce video quality due to the low-light intensity. Therefore, we developed a computer-vision method for tracking animals under low-light conditions. A deep learning model was trained to identify visually distinct features of mice across varied poses and light conditions. A custom Python script then computed the time spent in each chamber. We validated this pipeline using a PP assay where mice received wireless neuromodulation via MNPs targeting a genetically defined, non-reward-related pathway. Our model accurately tracked and classified chamber occupancy. The absence of significant preference shifts confirmed the specificity of the intervention, as the targeted feeding pathway is distinct from reward circuits. This pipeline offers a high-throughput, unbiased method for evaluating the specificity of neuromodulation systems.<\/p>\n","protected":false},"featured_media":4577,"template":"","profile_category":[23],"class_list":["post-4576","profiles","type-profiles","status-publish","has-post-thumbnail","hentry","profile_category-2025-interns"],"acf":[],"_links":{"self":[{"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles\/4576","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\/4576\/revisions"}],"predecessor-version":[{"id":4856,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles\/4576\/revisions\/4856"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/media\/4577"}],"wp:attachment":[{"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/media?parent=4576"}],"wp:term":[{"taxonomy":"profile_category","embeddable":true,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profile_category?post=4576"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}