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X-ORIGINAL-URL:https://coe.northeastern.edu
X-WR-CALDESC:Events for Northeastern University College of Engineering
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DTSTART;TZID=America/New_York:20230201T080000
DTEND;TZID=America/New_York:20230215T170000
DTSTAMP:20260508T010201
CREATED:20230202T160913Z
LAST-MODIFIED:20230202T160913Z
UID:35615-1675238400-1676480400@coe.northeastern.edu
SUMMARY:Call for Abstracts! AEESP 2023
DESCRIPTION:We are thrilled to share the news that Northeastern will be hosting the 2023 Association of Environmental Engineering and Science Professors (AEESP) Conference\, the preeminent professional gathering on all things environmental that is hosted biannually at a US university.We will be welcoming 700-800 of our colleagues to campus this June for a 4-day event that will target topics under the theme “Responding Together to Global Challenges”.  This theme is well aligned with the research we do here at Northeastern\, and we would love to see big representation from our student community!\n———————————————————————–\nCALL FOR ABSTRACTS:  Due February 15\, 2023\n———————————————————————–\nAbstracts for oral/poster presentation on any topic of interest to the community will be considered\, with conference sessions organized around major global challenges\, including: \n\nA changing climate\nCurrent and emerging threats to environmental quality and human health\nNew education and workforce demands\nAging infrastructure and risks to lifeline systems\nMarginalization of communities\nEmerging topics (to be added based on submissions received)\n\nFor more information on the conference and abstract submission please see the attached flyer and the conference website:  https://aeesp2023.sites.northeastern.edu/ \nAnd you can send questions to anyone on the Planning Committee – or drop by to chat! \n2023 AEESP Conference Planning Committee \n\nMatt Eckelman\, Science Committee Chair\nm.eckelman@northeastern.edu\nPhil Larese-Casanova\, Conference Chair\nP.LareseCasanova@northeastern.edu\nAmy Mueller\, Conference co-Chair\na.mueller@northeastern.edu
URL:https://coe.northeastern.edu/event/call-for-abstracts-aeesp-2023/
ORGANIZER;CN="Civil & Environmental Engineering":MAILTO:civilinfo@coe.neu.edu
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230202T103000
DTEND;TZID=America/New_York:20230202T113000
DTSTAMP:20260508T010201
CREATED:20230126T154948Z
LAST-MODIFIED:20230126T155022Z
UID:35236-1675333800-1675337400@coe.northeastern.edu
SUMMARY:Amani Al-shawabka's PhD Proposal Review
DESCRIPTION:“Channel-and-Adversary-Resilient Radio Fingerprinting through Data-Driven Approaches at Scale” \nCommittee: \nProf. Tommaso Melodia (Advisor)\nProf. Kaushik Chowdhury\nProf. Francesco Restuccia \nAbstract: \nRadio fingerprinting authenticates wireless devices by leveraging tiny hardware-level imperfections inevitably present in the radio circuitry. This way\, devices can be directly identified at the physical layer– thus avoiding energy-expensive upper-layer cryptography that resource-limited embedded devices may not be able to afford. Recent advances have proven that employing deep learning algorithms can achieve fingerprinting accuracy levels that were impossible to achieve by traditional low-dimensional algorithms. Still\, the wireless research community lacks an exhaustive understanding of the challenges associated with developing robust\, reliable\, and channel-resilient radio fingerprinting through deep-learning approaches for practical applications. Key challenges are the non-stationarity of the wireless channel\, and the dynamic effects introduced by the operational environment\, which significantly limit fingerprinting applications by obscuring the hardware impairments associated with the transmitted waveform.\nIn this thesis\, we (i) develop a full-fledged\, systematic investigation to quantify the impact of the wireless channel by providing a first-of-its-kind evaluation on deep-learning-based fingerprinting algorithms\, examining the worst-case scenario (employing devices with identical radio circuitry) and at scale; (ii) develop large-scale open datasets for radio fingerprinting collected in diverse\, rich\, channel conditions and environments\, and using different technologies\, including WiFi and LoRa; (iii) identify conditions where hardware impairments are still detectable; and (iv) design\, implement\, and benchmark new data-driven algorithms to counter the degradation introduced by the wireless channel. Notably\, we propose a generalized\, real-time channel- and adversary-resilient data-driven approach to authenticate wireless devices at scale in practical scenarios. To the best of our knowledge\, our work for the first time improves the fingerprinting accuracy of the worst-case scenario with up to 4x and 6.3x for WiFi and LoRa technologies\, respectively.
URL:https://coe.northeastern.edu/event/amani-al-shawabkas-phd-proposal-review-2/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230202T103000
DTEND;TZID=America/New_York:20230202T123000
DTSTAMP:20260508T010201
CREATED:20230117T184535Z
LAST-MODIFIED:20230117T184535Z
UID:35110-1675333800-1675341000@coe.northeastern.edu
SUMMARY:Qing Jin's PhD Proposal Review
DESCRIPTION:“Decoupling Efficiency-Performance Optimization for Modern Neural Networks” \nCommittee:\n\nProf. Yanzhi Wang (Advisor)\nProf. David R. Kaeli\nProf. Sunil Mittal\nProf. Jennifer Dy \n\nAbstract:\n\nDeep learning has achieved remarkable success in a variety of modern applications\, but this success is often accompanied by inefficiency in terms of storage and inference speed\, which can hinder their practical use on resource-constrained hardware. Developing highly efficient neural networks that maintain high prediction accuracy is crucial and challenging. This dissertation explores the potential for simultaneously achieving high efficiency and high prediction accuracy in neural networks\, and can be broadly divided into three sections. (1) In Section One\, we explore the implementation of highly efficient generative adversarial networks (GANs) capable of generating high-quality images within a predefined computational budget. The key challenge lies in identifying the optimal architecture for the generative model while simultaneously preserving the quality of the generated images from the compressed model\, despite its reduced computational cost. To achieve this\, we propose a novel neural architecture search (NAS) algorithm and a new knowledge distillation technique. (2) In Section Two\, we explore the challenge of quantizing discriminative models without relying on high-precision multiplications. To address this issue\, we present an innovative approach to determine the optimal fixed-point formats for both weights and activations based on their statistical properties. Our results demonstrate that high accuracy in quantized neural networks can be achieved without the need for high-precision multiplications. (3) In Section Three\, we delve into the challenge of training neural networks for innovative computing platforms\, specifically processing-in-memory (PIM) systems. Through a detailed mathematical derivation of the backward propagation algorithm\, we facilitate the training of quantized models on these platforms. Additionally\, through a thorough theoretical analysis of training dynamics\, we ensure convergence and propose a systematic solution for quantizing neural networks on PIM systems.
URL:https://coe.northeastern.edu/event/qing-jins-phd-proposal-review/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230202T163000
DTEND;TZID=America/New_York:20230202T173000
DTSTAMP:20260508T010201
CREATED:20230117T150214Z
LAST-MODIFIED:20230117T150214Z
UID:35096-1675355400-1675359000@coe.northeastern.edu
SUMMARY:Perfecting Your Poster Presentation
DESCRIPTION:Join the CommLab for an interactive workshop where we will be discussing perfecting your poster and presentation. Bring your poster-even if it is just a draft-and we can help you ensure your story will reach every audience member. We will also provide tips on how to reduce information overload for your audience; helping you to better convey your research.  Register via Zoom here. \n 
URL:https://coe.northeastern.edu/event/perfecting-your-poster-presentation/
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