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X-WR-CALNAME:Northeastern University College of Engineering
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:20240729T090000
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DTSTAMP:20260513T212344
CREATED:20240820T182120Z
LAST-MODIFIED:20240820T182120Z
UID:45099-1722243600-1722249000@coe.northeastern.edu
SUMMARY:Ruyi Ding PhD Proposal Review
DESCRIPTION:Name:\nRuyi Ding \nTitle:\nTowards Robust and Secure Deep Learning: From Training through Deployment to Inference \nDate:\n7/29/2024 \nTime:\n9:00:00 AM \nCommittee Members:\nProf. Yunsi Fei (Advisor)\nProf. Aidong Ding\nProf. Lili Su \nAbstract:\nIn recent years\, deep learning has experienced rapid advancement\, leading to the development of numerous commercial deep neural network (DNN) models across diverse fields such as autonomous driving\, healthcare\, and recommendation systems. However\, this wide adoption has intensified concerns about AI security throughout a neural network’s lifecycle — from training to deployment\, and inference. Various vulnerabilities have emerged\, threatening confidentiality\, privacy\, and intellectual property (IP) rights: poisoned training datasets facilitate privacy leakage and backdoor injection; after deployment\, models may be misused through unauthorized transfer learning\, a new form of IP infringement\, and weights and parameters are subject to side-channel assisted model extraction attacks; during inference\, adversarial attacks may compromise DNN functionality\, causing misclassifications.\nThis dissertation addresses new security challenges across the neural network lifecycle through several novel contributions. We identify a new poisoning vulnerability in graph neural networks\, where injecting poisoned nodes exacerbates link privacy leakage\, allowing attackers to steal adjacent information from private training data\, highlighting the necessity of robust AI training. To prevent model misuse after deployment\, we introduce EncoderLock and Non-transferable Pruning\, employing innovative training schemes and pruning methods to restrict the malicious use of pre-trained models through transfer learning\, effectively implementing applicability authorization. Towards secure deep learning implementations\, we adopt a software-hardware co-design approach to address DNN vulnerabilities. Specifically\, we leverage the electromagnetic emanations from DNN accelerators in a new approach called EMShepherd\, which detects adversarial examples (AE) on edge devices in a ‘black-box’ manner. To protect deployed DNNs against side-channel-based weight-stealing attacks\, we develop PixelMask\, which leverages the characteristics of DNN for side-channel defense by masking out unimportant inputs and dropping related operations to obfuscate side-channel signals. Lastly\, we explore the use of Trusted Execution Environments (TEE) to safeguard model weights and data privacy against model stealing and membership inference attacks.\nThis proposal identifies key challenges of robust and secure deep learning\,  tackles vulnerabilities at various stages of the AI lifecycle\, and provides comprehensive protection mechanisms\, from securing the training process to safeguarding deployed models\, paving the way for more resilient and reliable AI technologies in real-world applications.
URL:https://coe.northeastern.edu/event/ruyi-ding-phd-proposal-review/
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DTSTART;TZID=America/New_York:20240729T100000
DTEND;TZID=America/New_York:20240729T120000
DTSTAMP:20260513T212344
CREATED:20240517T125557Z
LAST-MODIFIED:20240731T141132Z
UID:43876-1722247200-1722254400@coe.northeastern.edu
SUMMARY:CommLab Drop-In Writing Hours
DESCRIPTION:Graduate students\, are you looking for a place for focused research writing time?  Join the CommLab drop-in writing hours any Mondays from 10 am-12 pm ET.  Drop in any Monday and stay for a short time or the whole two hours.  CommLab Fellows will be available to provide feedback on your writing.  We will be meeting in 13 International Village.
URL:https://coe.northeastern.edu/event/commlab-drop-in-writing-hours/2024-07-29/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240729T120000
DTEND;TZID=America/New_York:20240729T133000
DTSTAMP:20260513T212344
CREATED:20240820T181948Z
LAST-MODIFIED:20240820T181948Z
UID:45103-1722254400-1722259800@coe.northeastern.edu
SUMMARY:Shijie Yan PhD Proposal Review
DESCRIPTION:Name:\nShijie Yan \nTitle:\nEfficient Monte Carlo light transport algorithms in complex scattering media \nDate:\n7/29/2024 \nTime:\n12:00:00 PM \nCommittee Members:\nProf. Qianqian Fang (Advisor)\nProf. Steven Jacques\nProf. David Kaeli\nProf. Edwin Marengo \nAbstract:\nModeling light-tissue interactions is crucial for many optical imaging modalities\, for which the Monte Carlo (MC) method has been widely recognized as the gold-standard. Despite dramatic speed improvements gained via the use of graphics processing units (GPUs)\, MC simulations remain computationally intensive. Efficient and accurate MC algorithms are needed to further consider physiologically realistic tissue models\, especially for emerging optical imaging techniques. Voxel-based MC (VMC) and mesh-based MC (MMC) are two major MC methods for modeling complex tissues with their respective strengths and weaknesses. While VMC offers higher computational efficiency due to the simple data structure\, its accuracy suffers from the terraced boundary shape especially in low-scattering medium; on the other side\, MMC offers improved boundary fidelity but can be slow and memory-intensive\, particularly at high mesh density. Furthermore\, emerging wide-field diffuse optical imaging systems using structured light require more efficient modeling to handle numerous illumination patterns. Additionally\, niche applications such as polarized light imaging could also benefit from many of the recent advances from modern MC simulations such as GPU acceleration and handling of complex heterogeneous media. \nThis proposal is aimed to push the frontiers of modern MC simulation algorithms to fundamentally enhance their utilities in diverse applications. To reduce the staircase effect in VMC\, we have developed a hybrid MC algorithm\, named split-voxel MC (SVMC)\, where sub-voxel oblique surfaces are extracted using a marching-cubes algorithm and are incorporated into a memory-efficient voxelated data structure. SVMC allows VMC to handle curved surfaces while remaining computationally efficient. A GPU-accelerated marching-cubes algorithm was also developed to further accelerate SVMC domain preprocessing. On the other hand\, to further improve MMC computational efficiency\, a dual-grid MMC (DMMC) algorithm was developed to perform fast ray-tracing inside a coarse tetrahedral mesh while saving fluence data over a dense voxelated grid\, simultaneously achieving improved speed and output accuracy. To accommodate increasing needs of modeling wide-field pattern based sources\, we have developed a “photon sharing’’ MC algorithm that performs simulations of all illumination and detection patterns in parallel\, improving computational speed by an order of magnitude. Additionally\, we have developed a GPU-accelerated massively-parallel algorithm capable of modeling Mie scattering of sphere particles in three-dimensional media for polarized light imaging\, achieving nearly 1000$\times$ speed acceleration compared to sequential implementation. \nLastly\, we have also investigated a hardware-accelerated MMC algorithm using the NVIDIA OptiX ray-tracing framework\, leveraging modern GPU ray-tracing (RT) cores extensively optimized for graphics rendering. Preliminary results demonstrate comparable accuracy and significantly improved simulation speed compared to conventional tetrahedral MMC. \n 
URL:https://coe.northeastern.edu/event/shijie-yan-phd-proposal-review/
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DTSTART;TZID=America/New_York:20240729T150000
DTEND;TZID=America/New_York:20240729T170000
DTSTAMP:20260513T212344
CREATED:20240820T182030Z
LAST-MODIFIED:20240820T182030Z
UID:45101-1722265200-1722272400@coe.northeastern.edu
SUMMARY:Yunus Bicer PhD Dissertation Defense
DESCRIPTION:Name:\nYunus Bicer \nTitle:\nNovel Methods for Electromyographic Hand Gesture Recognition: Expressive Gestures Sets with Minimal Calibration \nDate:\n7/29/2024 \nTime:\n3:00:00 PM \nLocation:\nISEC 632 –\nCommittee Members:\nProf. Deniz Erdogmus (Advisor)\nProf. Mathew Yarossi (Co-Advisor)\nProf. Eugene Tunik\nProf. Tales Imbiriba \nAbstract:\nGesture recognition\, the process of interpreting hand gestures through computational algorithms and devices\, is essenatial for enhancing human-computer interaction(HCI). This thesis focuses on surface electromyography (sEMG)-based gesture recognition\, where the signals generated by muscles are analyzed to identify hand gestures. sEMG systems provides more natural and intuitive interactions compared to traditional input methods and hold significant potential in assistive technology\, prosthetics\, and immersive environments such as virtual and augmented reality. Despite these advantages\, sEMG-based methods face challenges including user-specific variability in signals\, limited gesture expressivity\, and the need for extensive calibration time. This research aims to address these issues by proposing novel methods for minimizing calibration time and expanding expressivity of gesture recognition capabilities. Key innovations include a real-time probability feedback mechanism to facilitate user adaptation and techniques to recognize a wider range of gestures with minimal training data. This work seeks to enhance the usability and versatility of sEMG-based systems\, making them more accessible and effective for various applications.
URL:https://coe.northeastern.edu/event/yunus-bicer-phd-dissertation-defense/
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