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SUMMARY:Graduate Admitted Student Webinar
DESCRIPTION:Join us for Northeastern’s Admitted Student Webinar \nAn exclusive invite only (via email) event to the Graduate School of Engineering Admitted Student Information Session with Kelly Egorova\, assistant dean of graduate admissions\, on Thursday\, May 28\, 2026\, at 9:30 a.m. ET. \nDuring the webinar\, During this webinar\, you can learn more about the following topics: \n\nDeposit timelines\nForeign Credential Evaluation\nI-20 information\nFunding your degree\nAmbassador roadmap\nCourse registration\norientation communication\nand more
URL:https://coe.northeastern.edu/event/graduate-admitted-student-webinar/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
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UID:56498-1779969600-1779973200@coe.northeastern.edu
SUMMARY:Closing the Loop: AI-Driven Predictive Modeling for Printed Electronics
DESCRIPTION:Northeastern University College of Engineering faculty are bringing cutting-edge advances in AI-powered manufacturing to a national audience through the NextFlex AI Webinar Series\, a leading Flexible and Hybrid Electronics initiative accelerating the adoption of artificial intelligence across the electronics manufacturing ecosystem. \nECE Assistant Professor Benyamin Davaji will present “Closing the Loop: AI-Driven Predictive Modeling for Printed Electronics” on May 28th\, in a free\, publicly accessible webinar hosted by NextFlex. The talk highlights ongoing research from Northeastern’s Autonomous Integrated Microsystems (AIMS) Laboratory\, where Prof. Davaji and his team are developing an end-to-end AI-driven framework designed to move printed electronics manufacturing beyond reactive inspection and toward predictive\, closed-loop control. The framework integrates multi-modal sensing\, combining ultrasound\, vision\, and acoustic emission\, with digital twin models and machine learning pipelines that enable real-time print proximity correction\, electrical impedance adjustment\, and process parameter tuning across multiple printing platforms and print heads. \nThe work also extends closed-loop intelligence upstream into design\, bridging ECAD and MCAD environments through a computer-vision-based slicing approach that generates print-ready files anticipating known process behaviors. Results are benchmarked across dimensional accuracy\, impedance prediction\, and process yield\, reflecting both the promise and the practical realities of AI integration in advanced manufacturing workflows. \nAbstract and Registration
URL:https://coe.northeastern.edu/event/closing-the-loop-ai-driven-predictive-modeling-for-printed-electronics/
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