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SUMMARY:National Engineers Week: Graduate Engineering Programs Overview
DESCRIPTION:The College of Engineering invites you to join us in celebrating National Engineers Week\, taking place February 23–28\, 2026. To kick off the celebration\, the Graduate School of Engineering is hosting a special webinar designed for prospective students: \nGraduate Engineering Programs Overview\nTuesday\, February 24\, 2026\, at 9:30 a.m. ET \nJoin Associate Dean of Graduate Admissions Kelly Egorova as she shares: \n\nAn overview of Northeastern’s graduate engineering programs.\nKey application requirements and process guidance.\nAvailable resources to support applicants throughout admissions.\n\n 
URL:https://coe.northeastern.edu/event/national-engineers-week-graduate-engineering-programs-overview/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
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SUMMARY:Crafting Data Visuals to Tell a Scientific Story: CommLab Drop-In Hours
DESCRIPTION:Looking to illustrate your data? Join our Data Visualization Drop-In sessions Tuesdays from 10-11am on Zoom to discuss strategies or receive feedback on your data visualization process.
URL:https://coe.northeastern.edu/event/crafting-data-visuals-to-tell-a-scientific-story-commlab-drop-in-hours/2026-02-24/
LOCATION:https://northeastern.zoom.us/j/99770601100?pwd=mbD3JHc7u0fjb558MDmqIHoSNBMrsS.1
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DTSTART;TZID=America/New_York:20260224T110000
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SUMMARY:CV/LinkedIn/Resume CommLab Drop-In Hours
DESCRIPTION:Graduate students\, do you need to increase your on-line presence or update your CV or Resume?  Join the CommLab’s LinkedIn\, CV\, and Resume drop-in hours any Tuesday from 11 am to 12 pm ET. This collaborative space offers valuable advice and peer feedback to enhance your online profile and professional presence. Join this drop-in workshop in person in room 334 CSC or through Zoom.
URL:https://coe.northeastern.edu/event/cv-linkedin-resume-commlab-drop-in-hours/2026-02-24/
LOCATION:https://northeastern.zoom.us/meeting/register/tJEof-quqzwiGNCi3nAuNVzIyX1jgXA03KYO
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SUMMARY:Digital Twins for Printed Electronics: How Can AI Learn FHE Printing
DESCRIPTION:Benyamin Davaji\, Assistant Professor in the College of Engineering\, alongside Haiyang Yun\, Senior PhD Student\, will instruct a professional course titled “Digital Twins for Printed Electronics: How Can AI Learn FHE Printing” on February 24\, 2026\, from 1:30–4:30 p.m. MT. at FLEX 2026\, the premier international conference for Flexible and Hybrid Electronics (FHE)\, taking place in Phoenix\, Arizona. \nDigital Twin is a virtual representation of the structure\, context\, and behavior of physical systems or a process\, with a live link to a physical system serving as a key enabler for predictive and data-driven optimization. In Printed and Flexible Hybrid Electronics (FHE)\, manufacturing involves multiple interdependent variables—different printing technologies\, inks\, substrates\, and process conditions—each introducing its own complexity. In practice\, additional challenges such as equipment drift\, batch-to-batch variations\, and environmental fluctuations further impact process consistency and yield. Changing a process or transferring it between tools is often difficult\, as each setup is highly customized and sensitive to local conditions. To address these challenges\, Digital Twin frameworks connect data from design\, fabrication\, and metrology into continuously learning digital models. They enable early detection of process drifts\, virtual experimentation for process development\, and data-driven optimization that reduces time\, cost\, and waste. \nThis course introduces Digital Twin frameworks for FHE\, focusing on Deep Neural Network (DNN)-based predictive models. Participants will learn how to integrate design\, fabrication\, and metrology data into continuously learning virtual twins that detect process drifts\, enable virtual experimentation\, and optimize manufacturing. The program covers the full workflow—from image processing and virtual metrology to AI model training\, validation\, and hyperparameter tuning—using real datasets. A hands-on “Build Your Own Digital Twin” module in Google Colab will provide practical experience in training and refining models for printed electronics applications\, equipping attendees with both theoretical insight and applied skills for process optimization and performance prediction. \nFor more information\, visit the FLEX 2026 course page.
URL:https://coe.northeastern.edu/event/digital-twins-for-printed-electronics-how-can-ai-learn-fhe-printing/
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