{"id":1105,"date":"2026-07-27T20:07:03","date_gmt":"2026-07-28T00:07:03","guid":{"rendered":"https:\/\/cms.web.umflint.edu\/cit\/research\/smiles\/"},"modified":"2026-07-31T15:43:35","modified_gmt":"2026-07-31T19:43:35","slug":"smiles","status":"publish","type":"page","link":"https:\/\/www.umflint.edu\/cit\/research\/smiles\/","title":{"rendered":"SMILES Lab"},"content":{"rendered":"\n<div class=\"wp-block-cover fb-cover-text-box wp-duotone-black-and-white\" style=\"margin-top:0;margin-bottom:0;padding-top:0;padding-right:0;padding-bottom:0;padding-left:0;min-height:100%;aspect-ratio:unset;\"><img decoding=\"async\" class=\"wp-block-cover__image-background\" alt=\"\" src=\"https:\/\/www.umflint.edu\/cit\/wp-content\/themes\/flintblock\/assets\/images\/sidewalk-art.jpg\" style=\"object-position:50% 95%\" data-object-fit=\"cover\" data-object-position=\"50% 95%\"\/><span aria-hidden=\"true\" class=\"wp-block-cover__background has-background-dim-90 has-background-dim 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class=\"wp-block-navigation-item__label\">SMILES Lab<\/span><\/a><button data-wp-bind--aria-expanded=\"state.isMenuOpen\" data-wp-on--click=\"actions.toggleMenuOnClick\" aria-label=\"SMILES Lab submenu\" class=\"wp-block-navigation__submenu-icon wp-block-navigation-submenu__toggle\" ><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" viewBox=\"0 0 12 12\" fill=\"none\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M1.50002 4L6.00002 8L10.5 4\" stroke-width=\"1.5\"><\/path><\/svg><\/button><ul data-wp-on--focus=\"actions.openMenuOnFocus\" class=\"wp-block-navigation__submenu-container wp-block-navigation-submenu\"><li class=\"wp-block-navigation-item wp-block-navigation-link\"><a class=\"wp-block-navigation-item__content\"  href=\"https:\/\/www.umflint.edu\/cit\/research\/smiles\/members\/\"><span class=\"wp-block-navigation-item__label\">SMILES Lab Members<\/span><\/a><\/li><\/ul><\/li><li class=\"wp-block-navigation-item wp-block-navigation-link\"><a 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c0.315-0.315,0.615-0.51,1.035-0.673c0.317-0.123,0.794-0.27,1.671-0.31C9.312,4.631,9.597,4.622,12,4.622 M12,3 C9.556,3,9.249,3.01,8.289,3.054C7.331,3.098,6.677,3.25,6.105,3.472C5.513,3.702,5.011,4.01,4.511,4.511 c-0.5,0.5-0.808,1.002-1.038,1.594C3.25,6.677,3.098,7.331,3.054,8.289C3.01,9.249,3,9.556,3,12c0,2.444,0.01,2.751,0.054,3.711 c0.044,0.958,0.196,1.612,0.418,2.185c0.23,0.592,0.538,1.094,1.038,1.594c0.5,0.5,1.002,0.808,1.594,1.038 c0.572,0.222,1.227,0.375,2.185,0.418C9.249,20.99,9.556,21,12,21s2.751-0.01,3.711-0.054c0.958-0.044,1.612-0.196,2.185-0.418 c0.592-0.23,1.094-0.538,1.594-1.038c0.5-0.5,0.808-1.002,1.038-1.594c0.222-0.572,0.375-1.227,0.418-2.185 C20.99,14.751,21,14.444,21,12s-0.01-2.751-0.054-3.711c-0.044-0.958-0.196-1.612-0.418-2.185c-0.23-0.592-0.538-1.094-1.038-1.594 c-0.5-0.5-1.002-0.808-1.594-1.038c-0.572-0.222-1.227-0.375-2.185-0.418C14.751,3.01,14.444,3,12,3L12,3z M12,7.378 c-2.552,0-4.622,2.069-4.622,4.622S9.448,16.622,12,16.622s4.622-2.069,4.622-4.622S14.552,7.378,12,7.378z M12,15 c-1.657,0-3-1.343-3-3s1.343-3,3-3s3,1.343,3,3S13.657,15,12,15z M16.804,6.116c-0.596,0-1.08,0.484-1.08,1.08 s0.484,1.08,1.08,1.08c0.596,0,1.08-0.484,1.08-1.08S17.401,6.116,16.804,6.116z\"><\/path><\/svg><span class=\"wp-block-social-link-label screen-reader-text\">Instagram<\/span><\/a><\/li>\n\n<li style=\"color:#00274c\" class=\"wp-social-link wp-social-link-youtube has-brand-blue-color wp-block-social-link\"><a rel=\"noopener nofollow\" target=\"_blank\" href=\"https:\/\/www.youtube.com\/@UM-FlintCIT\" class=\"wp-block-social-link-anchor plausible-event-name=Social+Clicked plausible-event-service=YouTube\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" version=\"1.1\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-hidden=\"true\" focusable=\"false\"><path 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viewBox=\"0 0 24 24\" version=\"1.1\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M13.982 10.622 20.54 3h-1.554l-5.693 6.618L8.745 3H3.5l6.876 10.007L3.5 21h1.554l6.012-6.989L15.868 21h5.245l-7.131-10.378Zm-2.128 2.474-.697-.997-5.543-7.93H8l4.474 6.4.697.996 5.815 8.318h-2.387l-4.745-6.787Z\" \/><\/svg><span class=\"wp-block-social-link-label screen-reader-text\">X<\/span><\/a><\/li><\/ul>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:75%\">\n\n\n<h2 class=\"wp-block-heading has-x-large-font-size\">AI Tools for Cybersecurity &amp; Neurodegenerative Diseases<\/h2>\n\n\n\n<h3 class=\"wp-block-heading has-large-font-size\">Director and PI: Khalid Malik, Director of Cybersecurity Programs, Professor, Computing Division, CIT<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The Secure Modeling and Intelligent Learning in Engineering Systems (SMILES) Lab is a forward-thinking interdisciplinary group of faculty and student researchers who are embracing outside-the-box thinking to develop cutting-edge AI-based solutions to some of the most pressing problems of our time. The translational research put forth by the SMILES team has an impact that extends beyond our community with marketable solutions in cybersecurity and healthcare that will benefit us all.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The bold vision of the SMILES lab has identified pressing needs and put unwavering focus on building and improving AI tools to solve them. Malik and his team have published many journal and conference articles, and they are continually building on that foundation. Through rich relationships with industry and medical experts, the team has been able to meet very specific needs with relevant solutions.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In addition to the external impact of SMILES research, the students working with SMILES projects are gaining a wealth of unique experiences. They are exposed to the latest in AI and cybersecurity tools, while constantly being supported to practice nimble, critical thinking that unlocks life-changing growth. With thoughtful mentorship from Malik, the students are empowered to practice persistence toward important tangible goals. These skills and the relationships they form will be lifelong and prepare them for a world that has much need for creative individuals who know how to bridge the gap between research and practice.<\/p>\n\n\n\n<div class=\"wp-block-group is-layout-flow wp-block-group-is-layout-flow\">\n<h2 class=\"wp-block-heading has-medium-font-size\">ON THIS PAGE<\/h2>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-left is-layout-flex wp-container-core-buttons-is-layout-f0d1fcef wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-link\" data-trailing-icon=\"arrow-down\"><a data-trailing-icon=\"arrow-down\" class=\"wp-block-button__link has-brand-blue-color has-text-color wp-element-button fb-button-has-icon\" href=\"#DeepFake\" style=\"padding-top:0;padding-right:0;padding-bottom:0;padding-left:0\">Deepfake Detector<svg class=\"fb-button-icon\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 24 24\" width=\"20\" height=\"20\" fill=\"currentColor\" aria-hidden=\"true\"><path d=\"M12 20.1l5.8-5.8-1.1-1.1-4 4V4h-1.5v13.2l-4-4-1.1 1.1z\"><\/path><\/svg><\/a><\/div>\n\n\n\n<div class=\"wp-block-button is-style-link\" data-trailing-icon=\"arrow-down\"><a data-trailing-icon=\"arrow-down\" class=\"wp-block-button__link has-brand-blue-color has-text-color wp-element-button fb-button-has-icon\" href=\"#Aneurysm\" style=\"padding-top:0;padding-right:0;padding-bottom:0;padding-left:0\">NeuroAssist<svg class=\"fb-button-icon\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 24 24\" width=\"20\" height=\"20\" fill=\"currentColor\" aria-hidden=\"true\"><path d=\"M12 20.1l5.8-5.8-1.1-1.1-4 4V4h-1.5v13.2l-4-4-1.1 1.1z\"><\/path><\/svg><\/a><\/div>\n\n\n\n<div class=\"wp-block-button is-style-link\" data-trailing-icon=\"arrow-down\"><a data-trailing-icon=\"arrow-down\" class=\"wp-block-button__link has-brand-blue-color has-text-color wp-element-button fb-button-has-icon\" href=\"#WebFiltering\" style=\"padding-top:0;padding-right:0;padding-bottom:0;padding-left:0\">AI-based Web Filtering<svg class=\"fb-button-icon\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 24 24\" width=\"20\" height=\"20\" fill=\"currentColor\" aria-hidden=\"true\"><path d=\"M12 20.1l5.8-5.8-1.1-1.1-4 4V4h-1.5v13.2l-4-4-1.1 1.1z\"><\/path><\/svg><\/a><\/div>\n\n\n\n<div class=\"wp-block-button is-style-link\" data-trailing-icon=\"arrow-down\"><a data-trailing-icon=\"arrow-down\" class=\"wp-block-button__link has-brand-blue-color has-text-color wp-element-button fb-button-has-icon\" href=\"#KnowledgeGraphs\" style=\"padding-top:0;padding-right:0;padding-bottom:0;padding-left:0\">Automated Knowledge Graph Curation<svg class=\"fb-button-icon\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 24 24\" width=\"20\" height=\"20\" fill=\"currentColor\" aria-hidden=\"true\"><path d=\"M12 20.1l5.8-5.8-1.1-1.1-4 4V4h-1.5v13.2l-4-4-1.1 1.1z\"><\/path><\/svg><\/a><\/div>\n\n\n\n<div class=\"wp-block-button is-style-link\" data-trailing-icon=\"arrow-down\"><a data-trailing-icon=\"arrow-down\" class=\"wp-block-button__link has-brand-blue-color has-text-color wp-element-button fb-button-has-icon\" href=\"#DigitalTwins\" style=\"padding-top:0;padding-right:0;padding-bottom:0;padding-left:0\">Automotive Cybersecurity Education<svg class=\"fb-button-icon\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 24 24\" width=\"20\" height=\"20\" fill=\"currentColor\" aria-hidden=\"true\"><path d=\"M12 20.1l5.8-5.8-1.1-1.1-4 4V4h-1.5v13.2l-4-4-1.1 1.1z\"><\/path><\/svg><\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n<h2 id=\"DeepFake\" class=\"wp-block-heading has-x-large-font-size\">Development of an Explainable and Robust Detector of Forged Multimedia and Cyber Threats using Artificial Intelligence<\/h2>\n\n\n\n<h3 class=\"wp-block-heading has-large-font-size\">Funded by the <a href=\"https:\/\/www.nsf.gov\/\" target=\"_blank\" rel=\"noreferrer noopener\">National Science Foundation<\/a> and <a href=\"https:\/\/www.michiganbusiness.org\/services\/entrepreneurial-opportunity\/university-programs\/\" target=\"_blank\" rel=\"noreferrer noopener\">Michigan Translational Research and Commercialization<\/a><\/h3>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"555\" height=\"370\" src=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-mask.png\" alt=\"Deep fake mask that indicates a swap\" class=\"wp-image-1077\" style=\"width:400px\" srcset=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-mask.png 555w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-mask-300x200.png 300w\" sizes=\"auto, (max-width: 555px) 100vw, 555px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Disinformation is a growing concern for society and is fueled by a new weapon: deepfaked multimedia. We have been told all of our lives to believe what we see with our own eyes, and for the first time, we can no longer trust them. AI generated Deepfakes have left the realm of science fiction, and are an unsettling reality that demands our immediate attention.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A Deepfake is essentially a piece of media that has been either manipulated by or entirely generated by AI to appear as though it&#8217;s an original artifact. With recent developments in Generative AI tools, the capabilities have grown to the point where humans cannot detect a difference anymore without assistance.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Fake multimedia is a growing threat on the global stage. Misinformation is not a new tactic, but the tools today are far more advanced. A well-made AI video of a political or industry leader can spread false narratives about public or corporate policy and have a devastating public impact. Imagine a viral video in which some foreign head of state threatened an impending attack on the U.S. \u2013 but that video is indistinguishable from a real one.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1920\" height=\"1080\" src=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-news.png\" alt=\"Seeing is believing - but for how long? demask logo. Snapshots of recent news articles about the public concerns with Deepfakes: Deepfake Audio is a Political Nightmare, Microsoft's new AI can simulate anyone's voice with 3 seconds of audio, AI Scam: Canadian Couple loses money to Fake Son's voice, AI Generated Deepfake of Japan's Prime Minister Sparks Concern\" class=\"wp-image-1081\" style=\"width:500px\" srcset=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-news.png 1920w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-news-300x169.png 300w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-news-1024x576.png 1024w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-news-768x432.png 768w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-news-1536x864.png 1536w\" sizes=\"auto, (max-width: 1920px) 100vw, 1920px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Using deepfaked audio and video in scams is increasingly possible. On Feb 4, 2024, a finance worker at a multinational firm was tricked with a Deepfake \u2018chief financial officer\u2019 video call and paid out <a href=\"https:\/\/www.cnn.com\/2024\/02\/04\/asia\/deepfake-cfo-scam-hong-kong-intl-hnk\/index.html\" target=\"_blank\" rel=\"noreferrer noopener\">$25 million to a scammer<\/a>.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1920\" height=\"1080\" src=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-destabilize.png\" alt=\"Deepfakes: The Most Dangerous Cyber Weapon, and the destabilizing political impact of deepfakes. A deepfake of Ukrainian President Volodymyr Zelensky calling on his soldiers to lay down their weapons was reportedly uploaded to a hacked Ukrainian news website. Alongside it, an image of President Joe Biden with a news banner about the realities of nuclear war, captioned as Biden announcing that men and women would be drafted to fight in Ukraine.\" class=\"wp-image-1084\" style=\"width:500px\" srcset=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-destabilize.png 1920w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-destabilize-300x169.png 300w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-destabilize-1024x576.png 1024w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-destabilize-768x432.png 768w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-destabilize-1536x864.png 1536w\" sizes=\"auto, (max-width: 1920px) 100vw, 1920px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">If large financial institutions can fall prey to these things, consider the vulnerability of an average citizen. According to a 2022 survey of 16,000 people across eight countries, 71% of people said that they don\u2019t even know what a deepfake is.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As we are discussing the threat of deepfakes to global security, democratic institutions, and scams on an international level, it\u2019s important to note that verified audio and video artifacts are now the norm as evidence in our judicial system. Deepfakes pose a significant threat to the integrity of that process.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Meeting court evidentiary standards is a challenging task, especially in the absence of underlying metadata, like digital watermarks, or if the media is post-processed with anti-forensic intent. In early February 2024, social media platforms like Meta announced that they will require AI-generated content to be labeled as such, but that falls under the category of \u2018locks only keep out the honest.\u2019 Those intent on using these advanced tools for deception will not be putting labels on them.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As the ability to create convincing fake videos has significantly increased, our need to authenticate legitimate digital media artifacts has grown as well. Beyond that, the tools needed to authenticate these media artifacts need to deliver assessments in an accessible way. Our judicial system, for example, is designed around a \u2018jury of peers\u2019 who won\u2019t have deep knowledge of AI and cybersecurity systems.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To meet this essential demand, we have developed a Deep Forgery Detector. This research has been ongoing for over 6 years, backed by nearly $1M in grants from agencies like the National Science Foundation and MTRAC. This funding has enabled us to develop the DFD MVP with the appropriate tools and knowledge and we are working to further develop them into a product that will be usable by companies and individuals without a major background in cybersecurity.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Student researchers associated with this project will gain the opportunity to learn how to use deep learning, Neurosymbolic AI, and Multimodal AI to develop tools to authenticate digital multimedia. The students will also learn how to protect detectors from anti-forensic attacks and gain experience in designing AI-based detectors to be transparent and explainable with accessible outputs. They will get the opportunity to work in interdisciplinary teams and solve problems beyond what they would encounter in a classroom setting.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-journey.png\" alt=\"SMILES Lab Journey timeline: 2018 NSF Testbed for Benchmarking Digital Audio Forensic Algorithms; 2019 NSF REU Supplement to Forensic Examiner; 2020 MSGC\/NSF Towards Development of Deepfake Detection Framework; 2021 NSF REU Supplement to Forensic Examiner; 2022 MTRAC Deep Forgery Detector; 2023 NSF\/MTRAC Explainable and Robust Detector. Four capability circles: Report Generation (visual, textual), Robustness (ensembled decision, multiple modal verification), Explainability (neurosymbolic, interpretable features), and Generalizable (common knowledge, human psychology, multimodal). Also shown: SpoTNet, a spoofing-aware transformer network for effective synthetic speech detection.\" class=\"wp-image-1085\" srcset=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-journey.png 1024w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-journey-300x169.png 300w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-deepfake-journey-768x432.png 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"612\" height=\"387\" src=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-spotnet-architecture.jpg\" alt=\"Flow chart for the SpoTNet framework: a speech signal is data-cleansed and processed (framing and windowing, non-silence speech indices, normalization, band-pass filtering, pre-emphasis, Mel spectrogram) into spectral envelope and contrast graphs, combined into an SP dataset, then passed through a Logical Spoofing Transformer Encoder (convolution and batch-norm filters, token encoding, transformer encoder, attentive audio representation), a multi-layer classifier (dense, batch-norm, dropout layers), and flattened before final dense\/dropout\/sigmoid layers output a real-or-spoof speech verification result.\" class=\"wp-image-1088\" srcset=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-spotnet-architecture.jpg 612w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-spotnet-architecture-300x190.jpg 300w\" sizes=\"auto, (max-width: 612px) 100vw, 612px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">This NSF partnership for innovation (NSF-PFI) and MTRAC-funded project seeks to further improve Deep Forgery Detector (DFD) technology built on NSF lineage award# 1815724: SaTC: CORE: ForensicExaminer: Testbed for Benchmarking Digital Audio Forensic Algorithms and MTRAC project titled \u201cDeep Forgery Detector.\u201d The DFD detects audio-visual forgeries, including various types of Deepfakes, that are used in the manipulation of digital multimedia, but new types are continuously appearing. Improvements to the DFD MVP will help to make it more robust against anti-forensics and also make it more accessible and explainable.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For details, see:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/nsf.gov\/awardsearch\/showAward?AWD_ID=1815724&amp;HistoricalAwards=false\" target=\"_blank\" rel=\"noreferrer noopener\">NSF Award Abstract: ForensicExaminer: Testbed for Benchmarking Digital Audio Forensic Algorithms<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.nsf.gov\/awardsearch\/showAward?AWD_ID=2231092&amp;HistoricalAwards=false\" target=\"_blank\" rel=\"noreferrer noopener\">NSF Award Abstract: Deep Forgery Detection Technology<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.michiganbusiness.org\/press-releases\/2021\/12\/mtrac-innovation-hub-for-advanced-computing-welcomes-third-cohort-of-early-stage-deep-tech-innovation-projects\/\" target=\"_blank\" rel=\"noreferrer noopener\">MEDC Press Release: MTRAC Innovation Hub for Advanced Computing Welcomes Third Cohort of Early-Stage Deep Tech Innovation Projects<\/a><\/li>\n<\/ul>\n\n\n\n<h2 id=\"Aneurysm\" class=\"wp-block-heading has-x-large-font-size\">NeuroAssist: An Intelligent Secure Decision Support System for the Prediction of Brain Aneurysm Rupture<\/h2>\n\n\n\n<h3 class=\"wp-block-heading has-large-font-size\">Funded by the Brain Aneurysm Foundation<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Cerebrovascular accident, or stroke, is the leading cause of disability worldwide and the second leading cause of death. Additionally, stroke is the fifth leading cause of death for all Americans and a leading cause of serious long-term disability. Annually, 15 million people worldwide suffer a stroke, and of these, 5 million die and another 5 million are left permanently disabled.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-aneurysm-unmet-needs.png\" alt=\"Subarachnoid Hemorrhage Prediction: Problems and Unmet Needs in Healthcare. Left: challenges of AI in medicine, including limited performance and high training cost, demand for human-in-the-loop methods, data privacy and integrity, demand for multimodal representation learning, and data scarcity with small, non-IID datasets. Right: four unmet needs \u2014 lack of multimodal and large training samples, lack of privacy-preserving decentralized solutions, lack of explainability and human-in-the-loop AI, and lack of an interdisciplinary approach.\" class=\"wp-image-1089\" srcset=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-aneurysm-unmet-needs.png 1024w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-aneurysm-unmet-needs-300x169.png 300w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-aneurysm-unmet-needs-768x432.png 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">In order to prevent these deaths and disabilities, neurologists and neurosurgeons must be able to diagnose the root causes early and improve their clinical management. They also need to determine an individual\u2019s overall risk across multiple complex considerations, including cerebral aneurysms, arteriovenous malformations (AVM), and Cerebral Occlusive Disease (COD).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Clinical management of diseases causing stroke is very complex. To illustrate the complexity, take the factor of Unruptured Intracranial Aneurysms by itself. Treating them is a complex decision-making process because the risk of rupture is not solely determined by the size of the aneurysm. Location\/artery matters a great deal; small aneurysms on certain arteries may rupture, while larger ones on other arteries may not.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Beyond the isolated case of the aneurysms themselves, various degrees of arteriovenous malformations and plaque accumulation inside the carotid arteries can add other risk factors to the overall stroke risk. Our current assessments are not enough to meet this complexity.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Lack of proper data often leads to a decision-making process that could aptly be described as \u2018better safe than sorry.\u2019 It is certainly true that surgical intervention is a successful method for eliminating the risk of stroke. However, these surgeries are invasive and may result in severe iatrogenic complications or neurological deficits so treating all aneurysms\/AVMs\/COD is not always worth that risk.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-aneurysm-location.png\" alt=\"Diagram comparing Location-Specific vs. Global aneurysm risk models: datasets flow through under-sampling into location-specific and global trained models producing rule sets; a parallel path filters datasets through an Apriori algorithm (50% confidence, 20% support) into additional rule sets; both combine with five years of modeling experience and domain-expert weight optimization to produce one improved rule set.\" class=\"wp-image-1092\" srcset=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-aneurysm-location.png 1024w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-aneurysm-location-300x169.png 300w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-aneurysm-location-768x432.png 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">On the other hand, delayed intervention when combining factors increases the risk of a stroke, the consequence can be death or permanent disability. When the overall risk is high, it is imperative to perform the correct treatment right away.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Without a dependable clinical risk\/severity score available, neurosurgeons must rely on heuristics compiled from unreliable data and their previous experience.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-aneurysm-knowledge-infused.png\" alt=\"Knowledge Infused Model for Aneurysm Detection and Segmentation: preprocessing of aneurysm images feeds a knowledge-infused deep neural network and a knowledge extraction process involving medical expert training and feedback, ROI extraction and categorization, infusion level selection, adaptive weight exploitation, adaptive layer infusion, and level-specific deep feature extraction, converging on detection and segmentation outputs. Citation: DeepInfusion: A dynamic infusion-based neuro-symbolic AI model for segmentation of intracranial aneurysms, Neurocomputing 551 (2023): 126510.\" class=\"wp-image-1095\" srcset=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-aneurysm-knowledge-infused.png 1024w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-aneurysm-knowledge-infused-300x169.png 300w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-aneurysm-knowledge-infused-768x432.png 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Between those two extremes are many cases where the risk warrants monitoring over time, not action. Doctors struggle with the decision of when to treat and when to watch, and every year thousands of unnecessary procedures are performed because they just aren\u2019t sure. Quantifying the overall stroke risks based on a group of risk factors in similar patients can help make this crucial decision much easier for neurosurgeons.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This means the tool needs to be a trusted one that clinicians can use to explain the individual situation. The patient and family are imagining the worst outcomes. They are worried about a devastating stroke and the financial burden of treatment. Being able to clearly explain why the best option is to wait and monitor would be a wonderful benefit to those families.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-strokenet.jpg\" alt=\"StrokeNet: An Automated Approach for Segmentation and Rupture Prediction of Intracranial Aneurysm. Step 1, aneurysm segmentation from scan imagery; Step 2, aneurysm rupture prediction combining deep features, geometrical features, blood flow pattern, and Fourier descriptor through weighting, feature selection, and classification into mild, moderate, severe, and critical risk categories. Citation: StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm, Computerized Medical Imaging and Graphics 108 (2023): 102271.\" class=\"wp-image-1096\" srcset=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-strokenet.jpg 1024w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-strokenet-300x169.jpg 300w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-strokenet-768x432.jpg 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">To meet this need, we have developed tools with a decentralized and highly explainable AI-based approach. These tools use a wide array of techniques: Multimodal AI on Digital Subtraction Angiography, Magnetic Resonance Angiography, and Computed Tomography Angiography image modalities along with clinical text, federated learning, RAG-based Neuro-symbolic AI, computational fluid dynamics, and multimodal explainable AI.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ultimately, this project will deliver tools that will reduce fatalities and long-term disabilities, defray high costs for patients and our healthcare system, and alleviate much psychological stress for patients. It will also help to develop and share more robust data with other researchers to advance our understanding of brain aneurysms going forward.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For details, see: <a href=\"https:\/\/www.bafound.org\/blog\/meet-research-grant-recipient-khalid-malik-phd\/\" target=\"_blank\" rel=\"noreferrer noopener\">Brain Aneurysm Foundation: Meet Research Grant Recipient: Khalid Malik, PhD<\/a><\/p>\n\n\n\n<h2 id=\"WebFiltering\" class=\"wp-block-heading has-x-large-font-size\">Neuro-symbolic AI-based Web Filtering<\/h2>\n\n\n\n<h3 class=\"wp-block-heading has-large-font-size\">Sponsored by Netstar Inc.<\/h3>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"246\" height=\"202\" src=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-lab-logo.png\" alt=\"SMILES Lab logo\" class=\"wp-image-1066\" style=\"width:200px\"\/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading has-large-font-size\">Explainable Multimodal Neurosymbolic Edge AI Models for Web Filtering<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Web filtering solutions are a vital component of cybersecurity. They block access to malicious websites, prevent malware from infecting our machines, and protect sensitive data from going out of organizations. They offer a secure, efficient, and controlled online experience across various sectors, addressing concerns related to security, productivity, and content appropriateness. The growing trends in Internet usage for data and knowledge-sharing calls for dynamic classification of web contents, particularly at the edge of the Internet.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-netstar-needs.png\" alt=\"Problem: Industrial Relevance and Novelty. NetSTAR needs: accurate trustworthy multimodal AI URL filtering for dynamic contents, multimodal representation learning, multilingual small datasets, human-in-the-loop methods, and data privacy. SMILES Lab solutions: neuro-symbolic AI for diverse contents, multimodal learning, knowledge infusion, multilingual representation learning, and federated learning.\" class=\"wp-image-1097\" srcset=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-netstar-needs.png 1024w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-netstar-needs-300x169.png 300w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-netstar-needs-768x432.png 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Companies today need these solutions to have multilingual capabilities and protect the data privacy of their employees. To meet these challenges requires a reliable solution that can effectively classify the URLs into correct classes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To meet these needs, UM-Flint has partnered with leading Japanese URL Filtering company, Netstar Inc., to develop a machine learning-based solution. The team consists of multiple PhD and postdoc students of Secure Modeling and Intelligent Learning in Engineering System (SMILES) Lab and employees of Netstar.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Students involved in this project will learn advanced techniques of Natural Language Processing, multilingual content processing, and development of knowledge graphs. They will gain experience with neurosymbolic and multimodal AI that is explainable and offers reasoning. They will also have opportunities to gain the many soft skills required for collaboration with a global corporation.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-text-color has-brand-maize-color has-alpha-channel-opacity has-brand-maize-background-color has-background is-style-wide\"\/>\n\n\n\n<h2 id=\"KnowledgeGraphs\" class=\"wp-block-heading has-x-large-font-size\">Automated Neuro Knowledge Graph Curation<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">To develop Neurosymbolic AI systems, it\u2019s essential to have knowledge graphs that represent all the entities of the domains and the relationships between them.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The rapid growth of Knowledge Graphs (KG) in recent years has been indicative of a resurgence in knowledge engineering. The use of KGs in the published literature to distill usable information that neuro-symbolic models and expert-based systems could use is one of the most promising approaches to the data consumption problem; and also, it provides more explanations for AI techniques such as machine learning and deep learning.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Most companies today recognize that data is their most valuable asset, but it can come in many different forms and formats. Making that data usable for ML and AI tools is challenging. Currently, knowledge graph creation and curation are mostly manual or somewhat semi-automated, and thus it is a labor-intensive process. In many cases, this manual process takes a person with a high level of expertise away from investing that time in the core product or scientific work they could be doing.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The automated curation of knowledge graphs from voluminous unstructured data can extract actionable information that is machine-readable and can potentially help knowledge discovery from Big data. To get actionable information, it\u2019s necessary to identify sources and meanings of and relationships between entities of the given domains.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Furthermore, automatically extracting reliable and consistent knowledge particularly from structured and unstructured sources at scale is a formidable challenge. Very few attempts have been made on the automated construction of health knowledge graphs. The ones that have been tried limited their focus to the creation of triplets by having only one type of relationship.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-kg-framework.png\" alt=\"Automated Knowledge Graph Curation Framework, conceptual view: text preprocessing (tokenization, normalization, tagging), categorization and clustering, and PICO classification feed into knowledge modeling, which performs concept extraction (ontology-based information extraction), relationship extraction (BioBERT, CNN-BiLSTM), and knowledge graph generation via triple extraction. Legend: PICO = Patient\/Population, Intervention, Comparison, and Outcomes; OBIE = Ontology-Based Information Extraction; BioBERT = Biomedical BERT.\" class=\"wp-image-1099\" srcset=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-kg-framework.png 1024w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-kg-framework-300x169.png 300w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-kg-framework-768x432.png 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-kg-functional.png\" alt=\"Automated Knowledge Graph Curation Framework, functional view: data preprocessing (sentence detection, tokenization, cleaning, lemmatization) feeds neuro-symbolic clustering (training data generation, cluster model, knowledge infusion) into a PICO classifier, which routes clusters through taxonomic and non-taxonomic relationship extraction, then triple extraction, initial knowledge graph generation, knowledge graph completion, and ambiguity removal to produce the fused knowledge graph.\" class=\"wp-image-1101\" srcset=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-kg-functional.png 1024w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-kg-functional-300x169.png 300w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-kg-functional-768x432.png 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Traditional models do not consider semantic, correlative, and causal relationships among domain concepts in knowledge graphs. None of the existing approaches have focused on building hierarchical relationships among extracted concepts. Additionally, concept extraction using either word embedding, or ontology-based information extraction does not give reliable accuracy, and this also affects the accuracy of relationship extraction. Lastly, efforts have not been made to develop predictive knowledge that should be interpretable to both machines and humans to enable true symbiotic human-machine and machine-machine interactions.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This project attempts to solve the above-mentioned challenges by proposing an automated domain-specific knowledge graph construction by making use of structured and unstructured data. This process is being repeated across multiple industries in the Deepfake Detector MVP, NeuroassistAI, and Netstar AI-based web filtering projects.<\/p>\n\n\n\n<h2 id=\"DigitalTwins\" class=\"wp-block-heading has-x-large-font-size\">Automotive Cybersecurity Education<\/h2>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"246\" height=\"202\" src=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-lab-logo.png\" alt=\"SMILES Lab logo\" class=\"wp-image-1066\" style=\"width:200px\"\/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading has-large-font-size\">Integrity Verification of Vehicle\u2019s Sensor using Digital Twin and Multimodal AI<\/h3>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1567\" height=\"903\" src=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-dtwins-image2.png\" alt=\"Diagram of the automotive cybersecurity digital twin system, showing a physical vehicle's sensing and feedback layers connected to a digital vehicle model through in-vehicle network, sensor, external network, and driving system modules, mediated by a perceptive cognitive layer and reviewed by a cybersecurity subject matter expert.\" class=\"wp-image-1102\" srcset=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-dtwins-image2.png 1567w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-dtwins-image2-300x173.png 300w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-dtwins-image2-1024x590.png 1024w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-dtwins-image2-768x443.png 768w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-dtwins-image2-1536x885.png 1536w\" sizes=\"auto, (max-width: 1567px) 100vw, 1567px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Training and recruiting cybersecurity is one of the most pressing issues in workforce development today. The global <a href=\"https:\/\/www.theiet.org\/\" target=\"_blank\" rel=\"noreferrer noopener\">Institution of Engineering and Technology<\/a> has released Automotive Cyber Security, a thought leadership review of risk perspectives for connected vehicles, which explains that our trajectory toward more connected vehicles has greatly increased the need for cybersecurity professionals in the automotive industry.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Filling those roles has a challenge, though: the learning curve. Cybersecurity education as it\u2019s done today can be a little dry and theoretical, making it seem more inaccessible than it actually is, but it doesn\u2019t have to be that way.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-dtwins-map.png\" alt=\"Diagram of a cloud-based interconnection system linking physical input from a real vehicle (sensors, steering, drivetrain) to a digital twin module and an AR\/VR gaming engine that delivers a virtual-reality driving experience, reviewed by a cybersecurity expert.\" class=\"wp-image-1103\" srcset=\"https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-dtwins-map.png 1024w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-dtwins-map-300x169.png 300w, https:\/\/www.umflint.edu\/cit\/wp-content\/uploads\/sites\/53\/2026\/07\/smiles-dtwins-map-768x432.png 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Our research group is developing various tools such as virtual \u2018digital twins\u2019 and a visual question-answer system to teach the complexity of interdisciplinary subjects such as cybersecurity in automobiles. This process will enable students to have a VR experience of the complex ways that IoT sensors, the driving systems, and the networks of systems and software in and out of the vehicle interact in a physical vehicle.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In summary, using neuro symbolic logic, AI and the flipped classroom, we\u2019re working to redesign classes from the ground up, starting with a new offering in automotive cybersecurity. The class will feature hands-on exercises on the digital twin of a real car system and will also offer 24\/7 assistance with a chatbot based on a large language model like ChatGPT.<\/p>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>AI Tools for Cybersecurity &amp; Neurodegenerative Diseases Director and PI: Khalid Malik, Director of Cybersecurity Programs, Professor, Computing Division, CIT The Secure Modeling and Intelligent Learning in Engineering Systems (SMILES) Lab is a forward-thinking interdisciplinary group of faculty and student researchers who are embracing outside-the-box thinking to develop cutting-edge AI-based solutions to some of the [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"parent":783,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-1105","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.umflint.edu\/cit\/wp-json\/wp\/v2\/pages\/1105","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.umflint.edu\/cit\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.umflint.edu\/cit\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.umflint.edu\/cit\/wp-json\/wp\/v2\/users\/12"}],"replies":[{"embeddable":true,"href":"https:\/\/www.umflint.edu\/cit\/wp-json\/wp\/v2\/comments?post=1105"}],"version-history":[{"count":3,"href":"https:\/\/www.umflint.edu\/cit\/wp-json\/wp\/v2\/pages\/1105\/revisions"}],"predecessor-version":[{"id":1728,"href":"https:\/\/www.umflint.edu\/cit\/wp-json\/wp\/v2\/pages\/1105\/revisions\/1728"}],"up":[{"embeddable":true,"href":"https:\/\/www.umflint.edu\/cit\/wp-json\/wp\/v2\/pages\/783"}],"wp:attachment":[{"href":"https:\/\/www.umflint.edu\/cit\/wp-json\/wp\/v2\/media?parent=1105"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}