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X-WR-CALDESC:Events for Northeastern University College of Engineering
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SUMMARY:Huan Wang PhD Dissertation Defense
DESCRIPTION:Announcing:\nPhD Dissertation Defense \nName:\nHuan Wang \nTitle:\nTowards Efficient Deep Learning in Computer Vision via Network Sparsity and Distillation \nDate:\n3/28/2024 \nTime:\n11:00:00 AM \nZoom \nCommittee Members:\nProf. Yun Fu (Advisor)\nProf. Octavia Camps\nProf. Zhiqiang Tao \nAbstract:\nAI\, empowered by deep learning\, has been profoundly transforming the world. However\, the excessive size of these models remains a central obstacle that limits their broader utility. Modern neural networks commonly consist of millions of parameters\, with foundation models extending to billions. The rapid expansion in model size introduces many challenges including training cost\, sluggish inference speed\, excessive energy consumption\, and negative environmental implications such as increased CO2 emissions. \nAddressing these challenges necessitates the adoption of efficient deep learning. The dissertation focuses on two overarching approaches\, network pruning and knowledge distillation\, to enhance the efficiency of deep learning models in the context of computer vision. Network pruning focuses on eliminating redundant parameters in a model while preserving the performance. Knowledge distillation aims to enhance the performance of the target model\, referred to as the “student\,” by leveraging guidance from a stronger model\, known as the “teacher”. This approach leads to performance improvements in the target model without reducing its size. \nIn this defense presentation\, I will start with the background and major challenges of leveraging these techniques to improve the efficiency of deep neural networks. Then\, I shall present the proposed solutions for various vision tasks\, including image classification\, single-image super-resolution\, novel view synthesis / neural rendering / NeRF / NeLF\, text-to-image generation / diffusion models\, and photorealistic head avatars. Extensive results and analyses will justify the efficacy of the proposed approaches\, demonstrating that pruning and distillation make a generic and complete framework for efficient deep learning in various domains. Finally\, a comprehensive summary (with takeaways) and outlook of the future work will conclude the presentation.
URL:https://coe.northeastern.edu/event/human-wang-phd-dissertation-defense/
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SUMMARY:Interdisciplinary Thinking as a Professional Skill
DESCRIPTION:Our next Graduate Greatness webinar is here to broaden your horizons with Interdisciplinary Thinking as a Professional Skill. \n📅 Date: Thursday\, March 28th \n🕒 Time: 12:00 – 1:00 p.m. EDT \n📍 Location: Zoom Webinar \nIn today’s complex world\, the ability to think across disciplines is more valuable than ever. Join us for an engaging session where we’ll explore the power of interdisciplinary thinking as a professional skill. \nLed by David Dawson\, this webinar will explore strategies for integrating diverse perspectives\, fostering creativity\, and solving complex problems. Interdisciplinary thinking can boost your career and drive innovation. \nRegister for the event: http://tinyurl.com/2tfsskat
URL:https://coe.northeastern.edu/event/interdisciplinary-thinking-as-a-professional-skill/
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DTSTART;TZID=America/New_York:20240328T160000
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DTSTAMP:20260516T025645
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LAST-MODIFIED:20240318T183812Z
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SUMMARY:3MT (Three Minute) Thesis
DESCRIPTION:This event is being presented by Graduate Women in Science and Engineering (GWISE) and Northeastern University Library. It’s a competition where PhD/ graduate students can share their thesis research under 3 minutes and compete for Cash prizes. It is a great opportunity for students to practice their communication skills and to share their research with a broader audience.
URL:https://coe.northeastern.edu/event/3mt-three-minute-thesis/
ORGANIZER;CN="GWiSE%3A Graduate Women in Science and Engineering":MAILTO:gwise.neu@gmail.com
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