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What if a child’s first encounter with AI didn’t come from YouTube, but from building and training their own models in the classroom? What if a $10 camera could detect industrial faults in milliseconds—without touching the cloud? This talk explores the reality of Edge AI at human scale: intelligent systems engineered to operate entirely on-device, under real-world constraints. \u003C/span>\u003Cstrong>OmniBuds\u003C/strong>\u003Cspan style=\"background-color:rgb(255,255,255);color:rgb(33,33,33);\"> is a clinically driven, ear-worn digital health platform that enables continuous, passive, and context-aware monitoring of hypertension and respiratory health, using multimodal sensing and microcontroller-optimised inference. \u003C/span>\u003Cstrong>Bella\u003C/strong>\u003Cspan style=\"background-color:rgb(255,255,255);color:rgb(33,33,33);\"> is a foundational AI education platform that introduces computing and artificial intelligence to children aged 6–12 through interactive, explainable, and offline model training—crafted specifically for safety, engagement, and accessibility in diverse classroom environments. \u003C/span>\u003Cstrong>CaaS\u003C/strong>\u003Cspan style=\"background-color:rgb(255,255,255);color:rgb(33,33,33);\"> is an edge-native camera platform for industrial automation, delivering low-latency, task-specific visual intelligence on low-power microcontrollers, without relying on external compute or connectivity. These platforms required targeted innovations in few-shot learning, self-supervised techniques, compiler-level optimisation, and event-driven inference—all while balancing efficiency, interpretability, and real-world resilience. I will conclude by outlining core principles for translating AI research into deployable, trustworthy, and domain-specific systems that scale—advancing the vision of pervasive intelligence across health, education, and industry.\u003C/span>\u003C/p>","2025-05-12T18:29:46.210Z","2025-06-21T23:18:58.634Z","2025-05-12T18:29:47.962Z","116",[739],{"id":464,"name":740,"committee":16,"position":16,"affiliation":741,"email":16,"biography":742,"createdAt":743,"updatedAt":744,"url_path_id":745,"contactPhoto":746,"socialLinks":784,"url_path":785},"Fahim Kawsar","Director, Device Software Research, Bell Labs, Cambridge | Bell Labs Fellow Professor of Mobile Systems, Computing Science, University of Glasgow","\u003Cp style=\"text-align:justify;\">Prof. Fahim Kawsar is a Bell Labs Fellow and Director of Device Research at Nokia Bell Labs, Cambridge, and Professor of Mobile Systems at the University of Glasgow. His research spans Mobile Systems, Edge AI, and Human-Centred Computing, with a focus on multi-sensory devices that learn, infer, and augment human behaviour. With over 20 years of experience, he has led major academic-industry collaborations—including a joint research centre with the University of Cambridge—and pioneered the field of Earable Computing. His applied work has resulted in two successful Bell Labs spin-outs in digital health and AI education. Prof. Kawsar has authored over 200 publications, holds 50+ patents, and has secured more than €6 million in research funding. He is Editor-in-Chief of IEEE Pervasive Computing and a regular keynote speaker, shaping the global discourse on embedded intelligence and real-world AI systems.\u003C/p>","2025-05-12T18:27:49.045Z","2025-06-03T17:40:35.625Z","111",{"id":747,"name":748,"alternativeText":16,"caption":16,"width":749,"height":750,"formats":751,"hash":780,"ext":753,"mime":756,"size":781,"url":782,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":783,"updatedAt":783},55,"image001.jpg",1080,1002,{"large":752,"small":761,"medium":767,"thumbnail":773},{"ext":753,"url":754,"hash":755,"mime":756,"name":757,"path":16,"size":758,"width":759,"height":760},".jpg","https://confcats-siteplex.s3.us-east-1.amazonaws.com/sas25/large_image001_6bd3b94c68.jpg","large_image001_6bd3b94c68","image/jpeg","large_image001.jpg",64.61,1000,928,{"ext":753,"url":762,"hash":763,"mime":756,"name":764,"path":16,"size":765,"width":68,"height":766},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sas25/small_image001_6bd3b94c68.jpg","small_image001_6bd3b94c68","small_image001.jpg",17.86,464,{"ext":753,"url":768,"hash":769,"mime":756,"name":770,"path":16,"size":771,"width":60,"height":772},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sas25/medium_image001_6bd3b94c68.jpg","medium_image001_6bd3b94c68","medium_image001.jpg",39.49,696,{"ext":753,"url":774,"hash":775,"mime":756,"name":776,"path":16,"size":777,"width":778,"height":779},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sas25/thumbnail_image001_6bd3b94c68.jpg","thumbnail_image001_6bd3b94c68","thumbnail_image001.jpg",3.06,168,156,"image001_6bd3b94c68",70.2,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sas25/image001_6bd3b94c68.jpg","2025-06-03T17:40:10.721Z",[],"-70","-75",{"id":199,"session":788},{"id":120,"title":789,"teaser":790,"body":52,"createdAt":791,"updatedAt":792,"publishedAt":793,"url_path_id":794,"contacts":795,"url_path":819},"Magnetic Sensing for Next-Generation Human-Machine Interfaces","\u003Cp style=\"text-align:justify;\">Wearable neural interfaces are emerging as a transformative technology for human-machine interaction, yet bandwidth, comfort, and energy efficiency limitations constrain current solutions. This keynote introduces the pioneering work of Neuranics Limited, a deep-tech spinout from the Universities of Glasgow and Edinburgh, which is developing the world’s most sensitive Tunnel MagnetoResistance (TMR)-based magnetic sensors for seamless neural interfacing. The lecture explores Neuranics’ end-to-end sensor solution, integrating ultra-sensitive TMR sensors and custom low-power microelectronics design. It will further provide insights into sensor design innovations, scalable semiconductor manufacturing, and biosignal measurements using compact, wearable devices. We highlight the company’s scalable semiconductor fabrication processes, expanding patent portfolio, performance benchmarks, and real-world applications in magnetomyography (MMG) and magnetocardiography (MCG). This keynote offers a forward-looking perspective on how magnetic sensing is poised to redefine the landscape of semiconductor design for bioelectronics and XR hardware.\u003C/p>","2025-05-12T18:29:23.214Z","2025-06-03T17:47:58.706Z","2025-05-12T18:29:24.300Z","115",[796],{"id":446,"name":797,"committee":16,"position":16,"affiliation":798,"email":16,"biography":799,"createdAt":800,"updatedAt":801,"url_path_id":802,"contactPhoto":803,"socialLinks":817,"url_path":818},"Hadi Heidari","CTO of Neuranics Limited and Professor of Nanoelectronics, James Watt School of Engineering, University of Glasgow, UK","\u003Cp style=\"text-align:justify;\">Hadi Heidari (PhD, SMIEEE, FHEA, FRSA, MIET, CEng) is CTO and co-founder of Neuranics, a deep-neurotech spinout developing advanced magnetic sensors for wearable neural interfaces. He is Professor of Nanoelectronics at the University of Glasgow, where he\u003Cbr>leads the Electronics and Nanoscale Engineering Division and holds an EPSRC Open Fellowship. He directs the Microelectronics Lab (meLAB), pioneering micro/nanoelectronics for medical and quantum applications.\u003C/p>\u003Cp style=\"text-align:justify;\">An active contributor to the IEEE community, he has served on the IEEE Sensors Council (2020–23) and the IEEE Circuits and Systems Society Board of Governors (2018–20). He is Associate Editor-in-Chief of IEEE JETCAS and serves on editorial boards for several major journals. He has published over 280 peer-reviewed papers and received multiple accolades, including the IEEE Sensors Council Young Professional Award (2019) and IET Healthcare Technologies Early Career Award (2020).\u003C/p>","2025-05-12T18:27:23.234Z","2025-06-21T23:43:07.990Z","110",{"id":804,"name":805,"alternativeText":16,"caption":16,"width":806,"height":806,"formats":807,"hash":813,"ext":753,"mime":756,"size":814,"url":815,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":816,"updatedAt":816},60,"2944ulj0_400x400.jpg",400,{"thumbnail":808},{"ext":753,"url":809,"hash":810,"mime":756,"name":811,"path":16,"size":812,"width":779,"height":779},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sas25/thumbnail_2944ulj0_400x400_4c8ad9e1ec.jpg","thumbnail_2944ulj0_400x400_4c8ad9e1ec","thumbnail_2944ulj0_400x400.jpg",5.63,"2944ulj0_400x400_4c8ad9e1ec",21.81,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/sas25/2944ulj0_400x400_4c8ad9e1ec.jpg","2025-06-21T23:43:02.819Z",[],"-69","-74",{"id":180,"session":821},{"id":267,"title":822,"teaser":823,"body":52,"createdAt":824,"updatedAt":825,"publishedAt":826,"url_path_id":827,"contacts":828,"url_path":871},"Building time-of-flight sensors from scratch to global adoption","\u003Cp>\u003Cspan style=\"background-color:rgb(255,255,255);color:rgb(0,32,82);\">An engineer’s perspective on some key break throughs in the field of time-of-flight sensing, that helped turn a side project into a ubiquitous technology and multi-billion dollar commercial success. \u003C/span>\u003C/p>","2025-06-02T15:19:52.501Z","2025-06-02T15:19:57.464Z","2025-06-02T15:19:57.457Z","124",[829],{"id":563,"name":830,"committee":16,"position":16,"affiliation":831,"email":16,"biography":832,"createdAt":833,"updatedAt":834,"url_path_id":835,"contactPhoto":836,"socialLinks":869,"url_path":870},"Stuart McLeod","ST Microelectronics","\u003Cp style=\"text-align:justify;\">Stuart McLeod is Senior Principal System Architect in the Imaging Division of ST Microelectronics in Edinburgh. 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