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X-WR-CALDESC:Events for Northeastern University College of Engineering
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DTSTART;TZID=America/New_York:20240723T113000
DTEND;TZID=America/New_York:20240723T123000
DTSTAMP:20260515T183059
CREATED:20240820T182406Z
LAST-MODIFIED:20240820T182406Z
UID:45093-1721734200-1721737800@coe.northeastern.edu
SUMMARY:Andrea Lacava PhD Proposal Review on 7/23
DESCRIPTION:Name:\nAndrea Lacava \nTitle:\nEnabling Intelligent nextG Cellular Networks through the Open RAN  Architecture \nDate:\n7/23/2024 \nTime:\n11:30:00 AM \nLocation:\nEXP 501 \nCommittee Members:\nProf. Tommaso Melodia (Advisor)\nProf. Francesca Cuomo (Advisor)\nProf. Stefano Basagni\nProf. Ioannis Chatzigiannakis \nAbstract:\nThe 5th generation (5G) and beyond of cellular networks will support heterogeneous use cases at an unprecedented scale\, thus demanding automated control and optimization of network functionalities\, customized to the needs of individual users. However\, achieving such fine-grained control over the Radio Access Network (RAN) is unfeasible with the current cellular architecture. \nTo bridge this gap\, the Open RAN paradigm and its specification introduce an “open” architecture with abstractions that facilitate closed-loop control and enable data-driven\, intelligent optimization of the RAN at the user-level. This thesis focuses on the design and development of system-level solutions to enable intelligent control in the next generation of cellular networks through the Open RAN architecture. The main research areas explored in this thesis include (i) the design and evaluation of platforms for the creation\, datasets generation and testing of the Open RAN architecture solutions; (ii) the development of Artificial Intelligence (AI)/Machine Learning (ML) models for various deployments and networking scenarios; and (iii) innovative methodologies for agile spectrum\, infrastructure\, and AI management within Open RAN. Among the significant contributions of this thesis are ns-O-RAN\, the first open-source simulation platform that integrates a functional 5G protocol stack in Network Simulator 3 (ns-3) with an O-RAN-compliant E2 interface\, and the pioneering architectural design and implementation of the dApps\, the real-time controllers for the O-RAN architecture. Furthermore\, the solutions proposed in this thesis are leveraged to investigate various network optimization use cases deemed critical in cellular networks. The results demonstrate that our approach outperforms traditional Radio Resource Management (RRM) heuristics\, enhancing overall RAN conditions at scale in both simulations and state-of-the-art experimental testbeds. \n 
URL:https://coe.northeastern.edu/event/andrea-lacava-phd-proposal-review-on-7-23/
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DTSTART;TZID=America/New_York:20240723T160000
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DTSTAMP:20260515T183059
CREATED:20240517T125640Z
LAST-MODIFIED:20240603T184607Z
UID:44082-1721750400-1721754000@coe.northeastern.edu
SUMMARY:LeetCode Mock Interviews – CommLab Drop-In Workshops
DESCRIPTION:Join the CommLab any Tuesday from 4-5 PM for our weekly LeetCode Mock Interview Workshop via Zoom. This workshop is tailored towards programming jobs and prior coding knowledge is expected. Boost your LeetCode problem-solving confidence for interviews by building your speaking skills while solving programming problems.
URL:https://coe.northeastern.edu/event/leetcode-mock-interviews-commlab-drop-in-workshops/2024-07-23/
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DTSTART;TZID=America/New_York:20240723T210000
DTEND;TZID=America/New_York:20240723T220000
DTSTAMP:20260515T183059
CREATED:20240820T182749Z
LAST-MODIFIED:20240820T182749Z
UID:45087-1721768400-1721772000@coe.northeastern.edu
SUMMARY:Zhenglun Kong PhD Dissertation Defense
DESCRIPTION:Name:\nZhenglun Kong \nTitle:\nTowards Efficient Deep Learning for Vision and Language Applications \nDate:\n7/23/2024 \nTime:\n9:00:00 PM \nCommittee Members:\nProf. Yanzhi Wang (Advisor)\nProf. David Kaeli\nProf. Dakuo Wang\nProf. Weiyan Shi \nAbstract:\nMachine learning and AI have been advancing rapidly in recent years\, leading to numerous applications across diverse fields such as autonomous vehicles\, entertainment\, science\, healthcare\, and assistive technologies—significantly enhancing daily life. However\, this advancement has been accompanied by a significant increase in the size of deep neural network (DNN) models\, which poses considerable economic challenges. The substantial costs associated with the training\, inference\, and deployment of large vision and language models require extensive computational resources and time\, proving especially taxing for smaller entities and individuals. This also complicates deployment on resource-constrained devices and in areas with limited infrastructure. \nA major challenge is deploying AI models on devices with limited capacity\, such as wearables\, sensors\, and mobile phones. These edge devices\, often operating offline and requiring real-time processing\, are critical for many applications but struggle to support large models. My dissertation research addresses these pressing issues with the aim of enabling the practical implementation of AI. We ensure the effectiveness of AI models while adapting them for use in constrained environments by tackling fundamental AI challenges from four angles: \n1. Managing Massive Computation: We introduce a novel token pruning framework that reduces the latency of Vision Transformers (ViT) by up to 41% compared to existing works on mobile devices. Additionally\, we propose a quantization framework for large language models (LLMs)\, achieving an on-device speedup of up to 2.55x compared to FP16 counterparts across multiple edge devices. \n2. Mitigating Training Costs: We develop fast\, accurate\, and memory-efficient training methods by utilizing a hierarchical data redundancy reduction scheme\, which achieves up to a 40% speedup in ViT pre-training with minimal accuracy loss. \n3. Merging Multiple Models: We propose an efficient way to merge multiple LLMS\, yielding a more advanced and robust LLM while maintaining the model  size\, as well as  reducing knowledge interference. \n4. Co-designing Speed-aware Deep Neural Networks: We consider memory access cost\, the degree of parallelism\, and practical latency in the design of 2D and 3D object detection models for practical deployment.
 By addressing these areas\, my research aims to enable the effective and efficient use of AI models in constrained environments\, ensuring their practical implementation across various applications. \n 
URL:https://coe.northeastern.edu/event/zhenglun-kong-phd-dissertation-defense/
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