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DTSTART;TZID=America/New_York:20230618T080000
DTEND;TZID=America/New_York:20230623T170000
DTSTAMP:20260405T044331
CREATED:20230127T165522Z
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UID:35294-1687075200-1687539600@coe.northeastern.edu
SUMMARY:Reconnect Workshop 2023: Risk Assessment
DESCRIPTION:Where: The Omni Hotel\, Providence\, Rhode Island   \nThis year’s workshop is focused on Risk Assessment. Risk assessment is an overall process of identifying\, analyzing\, and preparing for potential risks (e.g.\, natural or man-made disasters)\, which is extremely important to ensure the continuity of organization operations and the well-being of the people involved. Risk assessment goals include (a) a better understanding of the vulnerability and the potential impact on the organization and people involved\, and (b) prescribing control measures and contingency plans to minimize the potential impacts. Reconnect 2023 will review classic methods and tools for risk assessment for various risks and hazards through real-world case studies. Several researchers affiliated with the newly established DHS Center of Excellence\, SENTRY\, will provide an overview of SENTRY’s research mission and offer examples of how they will use risk assessment methods in the center’s research. These include allocating resources for disaster management facing adaptive adversaries\, deploying “virtual sentries” to protect civilian spaces—so-called “soft targets”—around the country\, and several others. Registration fees\, lodging\, meals\, and travel: Accepted participants from US academic institutions: registration\, lodging in a single room\, and meals will be provided at no charge. Limited funds may be available to provide partial support for travel.   \nThe application deadline is March 15\, 2023\, or until all slots are filled. Click here to apply. \nOrganizers: Margaret (Midge) Cozzens\, DIMACS\, Rutgers University; Tamra Carpenter\, Rutgers University Jun Zhuang\, University at Buffalo Primary Speakers: Jun Zhuang\, University at Buffalo; Allison Coffey Reilly\, University of Maryland\, Seth Guikema\, University of Michigan; and Auroop Ganguly\, Northeastern University  
URL:https://coe.northeastern.edu/event/reconnect-workshop-2023-risk-assessment/
ORGANIZER;CN="ALERT":MAILTO:alert-info@coe.neu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230619T140000
DTEND;TZID=America/New_York:20230619T160000
DTSTAMP:20260405T044331
CREATED:20230701T214046Z
LAST-MODIFIED:20230701T214046Z
UID:37315-1687183200-1687190400@coe.northeastern.edu
SUMMARY:CommLab Writing Group
DESCRIPTION:Join us weekly in the Curry Student Center\, room 433. \nSetting and sticking to a consistent writing schedule is key to improving skills and accomplishing your writing tasks (don’t just take our word for it). \nCOE graduate students are invited to join the CommLab writing group to share best practices\, get feedback from your peers\, and work on any writing project (dissertation\, thesis\, manuscript\, fellowship\, poster\, presentation\, or other forms of technical communications). We will meet weekly for 2 hours; you do not have to attend all the sessions or the full 2 hours to participate.
URL:https://coe.northeastern.edu/event/commlab-writing-group-2/2023-06-19/
LOCATION:Curry Student Center\, 360 Huntington Ave.\, Boston\, MA\, 02115\, United States
GEO:42.3394629;-71.0885286
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END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230620
DTEND;VALUE=DATE:20230624
DTSTAMP:20260405T044331
CREATED:20230518T135356Z
LAST-MODIFIED:20230518T135448Z
UID:37050-1687219200-1687564799@coe.northeastern.edu
SUMMARY:2023 AEESP Conference Hosted at Northeastern University
DESCRIPTION:Northeastern University is proud to present the 2023 AEESP Research and Education Conference\, organized in collaboration with a large team from universities in New England including Massachusetts Institute of Technology\, Tufts University\, University of Connecticut\, University of Maine\, University of Massachusetts-Amherst\, University of New Hampshire\, and University of Rhode Island.  This conference will be welcoming participants from across the country and internationally\, as well as representatives from industry\, for 4 days of sharing\, learning\, and planning. The conference activities will commence on Tuesday\, June 20 and continue through Friday\, June 23\, under the theme “Responding Together to Global Challenges”. The conference and its attendees will reflect the many contributions environmental scientists and engineers are making to address concerning global\, historical\, and emerging challenges to living in a healthy\, safe\, and just society such as a changing climate\, emerging environmental quality and human health threats\, aging infrastructure and networks\, marginalization of communities\, and evolving education demands. \nThe Association of Environmental Engineering and Science Professors (AEESP) is a global organization of professors in academic programs who strive to provide education in the sciences and technologies of environmental protection. Founded in 1963\, the Association has grown to universities around the world\, facilitating improving education and research programs and encouraging graduate education. “This conference is an important meeting to exchange novel ideas in environmental research and education. We are proud to host academics\, students\, and friends from far and wide who will be sharing their recent advances in their fields of study\,” said Philip Larese-Casanova\, Associate Professor at Northeastern University\, who also serves as the Conference Chair. In addition\, the conference also serves the profession by providing information to government agencies and the public and provides direct benefits to its members. \nUnique elements of this year’s conference include a career fair and outreach activities on Conference Day 1\, a gala dinner on Day 2 at the New England Aquarium\, and a series of field trips in and around Boston on Friday June 23 to round out the week! The conference schedule includes the conventional and highly sought-after workshops and technical presentations. Students\, post-docs\, and faculty will find ample organized opportunities for networking\, career development\, and community outreach at the conference. \nRegistration is open through June 5 for the 2023 AEESP Conference!
URL:https://coe.northeastern.edu/event/2023-aeesp-conference-hosted-at-northeastern-university/
ORGANIZER;CN="Civil & Environmental Engineering":MAILTO:civilinfo@coe.neu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230620T080000
DTEND;TZID=America/New_York:20230620T170000
DTSTAMP:20260405T044331
CREATED:20230624T181028Z
LAST-MODIFIED:20230624T181028Z
UID:37261-1687248000-1687280400@coe.northeastern.edu
SUMMARY:Alfred P. Navato's PhD Dissertation Defense
DESCRIPTION:Title:\nEnabling Anomaly Detection in Complex Chemical Mixtures Through Multimodal Data Fusion \nDate:\n6/26/2023 \nTime:\n10:00:00 AM \nLocation:\nSH 210\, \nCommittee Members:\nProf. Mueller (Advisor)\nProf. Erdogmus\nProf. Ioannidis\nProf. Onnis-Hayden \nAbstract:\nRecently innovations in machine learning and data processing are increasingly tied to ensuring useability and interpretability when these methods are applied within end-user domains.  One societally important example of such a domain is management and operations of water infrastructure in cities\, where data collection is currently costly and limited\, enabling analytics have the potential to generate real impact for urban communities\, and correctness of results is critical to protect human and environmental health.  This dissertation holistically considers issues of generalizability\, transferability\, and applicability of a range of data fusion and machine learning approaches across end-user domains within the context of solution building for improved real-time management of wastewater infrastructure.  The first chapter provides an overview of the challenges associated with anomaly detection within the wastewater field and reviews the performance of various anomaly detection techniques implemented in other disciplines.  The second chapter discusses the barriers and opportunities in cross-disciplinary pollination of data fusion techniques.  The third chapter presents development of an unsupervised approach facilitating quantitative characterization of the complex background which is wastewater\, necessary to be able to implement any automated operational interventions.  The fourth chapter develops an approach for cost-minimization/information-maximization design of a sensor to facilitate specifically detection of chemical anomalies (defined as inflow events that might compromise wastewater treatment facilities) by using machine learning and feature selection techniques to minimize the number of input signals needed to achieve reasonable accuracies.  Together the third and fourth chapters provide a clear\, explainable\, actionable pathway forward in envisioning next generation wastewater infrastructure\, demonstrating novel and impactful use of data fusion and machine learning techniques in a real-world context.
URL:https://coe.northeastern.edu/event/alfred-p-navatos-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230620T130000
DTEND;TZID=America/New_York:20230620T140000
DTSTAMP:20260405T044331
CREATED:20230522T172041Z
LAST-MODIFIED:20230522T172041Z
UID:37070-1687266000-1687269600@coe.northeastern.edu
SUMMARY:Chang Liu's PhD Dissertation Defense
DESCRIPTION:“Unleashing the Potential of Transfer Learning for Visual Applications” \nCommittee Members:\nProf. Raymond Fu (Advisor)\nProf. Sarah Ostadabbas\nProf. Zhiqiang Tao \nAbstract:\nThe recent flourish of deep learning in various tasks is largely accredited to the rich and accessible labeled data. Nonetheless\, massive supervision remains a luxury for many real-world applications. Further\, the domain shift problem has also seriously impeded large-scale deployments of deep-learning models. As a remedy\, Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way\, the dependence on a large number of target domain data can be reduced for constructing target learners. \nIn this dissertation research\, I investigate two major problems in transfer learning\, domain adaptation (DG) and domain adaptation (DA)\, on various visual applications. (1) The challenge of DG lies in an over-simplified assumption\, that is\, the source and target data are independent and identically distributed (i.i.d.) while ignoring out-of-distribution (OOD) scenarios commonly encountered in practice. This issue is common in visual applications such as object recognition\, hyperparameter optimization\, and face recognition. We propose algorithms that are specifically designed for each task\, such as metric learning\, adversarial regularization\, feature disentanglement\, and meta-learning. (2) DA can be considered a special case of DG with unlabeled target data available. The major challenge is how to align the labeled source and unlabeled target data. We delve into the applications of image recognition and video recognition and propose algorithms to ensure domain-wise discriminativeness and class-wise closeness across domains. Experiments show that the proposed algorithms outperform the state-of-the-art methods on the commonly-used benchmark datasets.
URL:https://coe.northeastern.edu/event/chang-lius-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230623T100000
DTEND;TZID=America/New_York:20230623T110000
DTSTAMP:20260405T044331
CREATED:20230606T153237Z
LAST-MODIFIED:20230606T153237Z
UID:37194-1687514400-1687518000@coe.northeastern.edu
SUMMARY:Cooper Loughlin's PhD Dissertation Defense
DESCRIPTION:“Deep Generative Models for High Dimensional Spatial and Temporal Data Analysis” \nCommittee Members:\nProf. Vinay Ingle (Advisor)\nDr. Dimitris Manolakis\nProf. Purnima Ratilal-Makris \nAbstract:\nData analysis and exploitation in practical applications is challenging when observations are the result of many interacting natural and man-made phenomena. We address two important problems for which traditional methods of analysis are insufficient. One problem of practical interest is the identification of particular materials from remotely sensed hyperspectral imagery. This is traditionally accomplished by comparing image pixel spectra to those from a known material library. Such techniques are limited by spectral variability\, background interference\, and imperfect compensation of atmospheric components. Established methods address these limitations with statistical techniques. Simple probability models result in tractable methods; however\, analyses are limited by errors due\, in particular\, due to false alarms. \nAnalysis of complex time series is another challenging problem\, particularly when data are high dimensional. This arises in air quality monitoring\, where atmospheric concentration measurements of multiple pollutants are taken over time. Two analysis goals in this context are forecasting and anomaly detection. Both tasks are enabled by an accurate model for the temporal dynamics and interaction between pollutants. Air quality data are complex due to long term temporal dependencies\, non-linear dependence between pollutants\, and missing observations. Traditional multivariate time series analysis approaches\, such as the vector autoregression and linear dynamical system models\, fail to capture those characteristics necessary for a sufficient probabilistic model. \nWe use deep generative models to develop practical solutions that address these problems. This is made possible through the application of deep latent variable models. The modeling approach follows the philosophy that complex data can typically be explained by simpler underlying factors of variation. Variational autoencoders (VAEs) are deep latent variable models that emulate data generation by transforming simple\, low dimensional\, latent random vectors through a deep neural network. VAEs are trained to produce samples that resemble the training data\, thus capturing a manifold on which complex data are distributed. This philosophy is extended to time series data\, where we consider sequences of latent vectors. \nWe utilize VAEs develop a flexible generative model for hyperspectral imagery. Based on that model\, we develop a novel material identification framework which localizes target material spectra along the manifold. Through experiments on real data\, we show that the \ac{VAE} approach is better able to reject false alarms from materials with similar spectra when compared to established methods alone. We additionally develop a novel dynamical \ac{VAE} model for time series of air quality data. Using that model\, we develop practical methods for computing forecast distributions using Monte Carlo integration. We evaluate forecast distributions against real air quality data and demonstrate the ability to predict temporal dynamics and forecast uncertainty. The primary contribution of this work is to develop practical solutions to challenging data analysis problems through the use of deep generative models.
URL:https://coe.northeastern.edu/event/cooper-loughlins-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230626
DTEND;VALUE=DATE:20230629
DTSTAMP:20260405T044331
CREATED:20230612T135208Z
LAST-MODIFIED:20230612T135208Z
UID:37215-1687737600-1687996799@coe.northeastern.edu
SUMMARY:2023 ASEE Conference- Baltimore\, MA
DESCRIPTION:Northeastern College of Engineering will attend ASEE 2023 Annual Conference this year at the Baltimore Convention Center\, MA. Join us to learn about Northeastern’s graduate engineering programs from Sunday\, June 25th to Wednesday\, June 28th! Our booth number is 98. \n  \n 
URL:https://coe.northeastern.edu/event/2023-asee-conference-baltimore-ma/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230626T083000
DTEND;TZID=America/New_York:20230626T093000
DTSTAMP:20260405T044331
CREATED:20230624T180848Z
LAST-MODIFIED:20230624T180848Z
UID:37265-1687768200-1687771800@coe.northeastern.edu
SUMMARY:Deniz Unal's PhD Proposal Review
DESCRIPTION:Title:\nSoftware-Defined Underwater Acoustic Networks \nCommittee Members:\nProf. Tommaso Melodia (Advisor)\nProf. Stefano Basagni\nProf. Kaushik Chowdhury\nDr. Emrecan Demirors \nAbstract:\nThe exploration\, monitoring\, and understanding of oceans play a crucial role in addressing climate change\, overseeing underwater pipelines\, and preventing maritime warfare attacks. To achieve these significant objectives\, it is vital to utilize networks of cost-effective and flexible underwater devices capable of efficiently collecting and transmitting information to the shore. However\, the progress of underwater networks heavily relies on underwater acoustic modems\, which currently face limitations such as low data rates and inflexible hardware designs\, limiting their usability to specific scenarios. To overcome these limitations\, we propose a modular software-defined acoustic networking platform built on the Zynq system-on-chip architecture that can be easily deployed in a compact form factor. Our platform distinguishes itself from existing solutions in several ways. Firstly\, it possesses the capability to adapt to varying conditions by adjusting protocol parameters at all layers of the networking stack. Secondly\, it achieves high data rate connections\, particularly over short distances. Additionally\, it seamlessly integrates with other sub-sea platforms\, including underwater drones. We demonstrate the capabilities and the performance of our platform with tasks\, such as channel estimation and characterization\, establishing high data rate Orthogonal Frequency-Division Multiplexing (OFDM) links\, and running third-party software to implement JANUS standard. In addition\, we introduce the enabling technologies for the development and implementation of underwater networks. These technologies facilitate the establishment of connectivity between underwater networks and the shore\, as well as the integration of modems with underwater vehicles. Lastly\, we provide a demonstration of the algorithmic development conducted on our platform. We mainly consider high-rate\, wideband\, adaptive links and perform experimental evaluations at sea. In particular\, we demonstrate multicarrier communications with mobile platforms with the presence of Doppler and compare the performance of forward error correction methods\, and demonstrate dataset recording for artificial intelligence research.
URL:https://coe.northeastern.edu/event/deniz-unals-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230629T170000
DTEND;TZID=America/New_York:20230629T173000
DTSTAMP:20260405T044331
CREATED:20230626T173009Z
LAST-MODIFIED:20230626T173036Z
UID:37275-1688058000-1688059800@coe.northeastern.edu
SUMMARY:Zifeng Wang's PhD Dissertation Defense
DESCRIPTION:Title: Effective and Efficient Continual Learning \nCommittee Members:\nProf. Jennifer Dy (Advisor)\nProf. Stratis Ioannidis\nProf. Yanzhi Wang \nAbstract:\nContinual Learning (CL) aims to develop models that mimic the human ability to learn continually without forgetting knowledge acquired earlier. While traditional machine learning methods focus on learning with a certain dataset (task)\, CL methods adapt a single model to learn a sequence of tasks continually. \nIn this thesis\, we target developing effective and efficient CL methods under different challenging and resource-limited settings. Specifically\, we (1) leverage the idea of sparsity to achieve cost-effective CL\, (2) propose a novel prompting-based paradigm for parameter-efficient CL\, and (3) utilize task-invariant and task-specific knowledge to enhance existing CL methods in a general way. \nWe first introduce our sparsity-based CL methods. The first method\, Learn-Prune-Share (LPS)\, splits the network into task-specific partitions\, leading to no forgetting\, while maintaining memory efficiency. Moreover\, LPS integrates a novel selective knowledge sharing scheme\, enabling adaptive knowledge sharing in an end-to-end fashion. Taking a step further\, we present Sparse Continual Learning (SparCL)\, a novel framework that leverages sparsity to enable cost-effective continual learning on edge devices. SparCL achieves both training acceleration and accuracy preservation through the synergy of three aspects: weight sparsity\, data efficiency\, and gradient sparsity. \nSecondly\, we present a new paradigm\, prompting-based CL\, that aims to train a more succinct memory system that is both data and memory efficient. We first propose a method that learns to dynamically prompt (L2P) a pre-trained model to learn tasks sequentially under different task transitions\, where prompts are small learnable parameters maintained in a memory space. We then improve L2P by proposing DualPrompt\, which decouples prompts into complementary “General” and “Expert” prompts to learn task-invariant and task-specific instructions\, respectively. \nFinally\, we propose DualHSIC\, a simple and effective CL method that generalizes the idea of leveraging task-invariant and task-specific knowledge. DualHSIC consists of two complementary components that stem from the so-called Hilbert Schmidt independence criterion (HSIC): HSIC-Bottleneck for Rehearsal (HBR) lessens the inter-task interference and HSIC Alignment (HA) promotes task-invariant knowledge sharing. \nComprehensive experimental results demonstrate the effectiveness and efficiency of our methods over the state-of-the-art methods on multiple CL benchmarks.
URL:https://coe.northeastern.edu/event/zifeng-wangs-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230703T150000
DTEND;TZID=America/New_York:20230703T160000
DTSTAMP:20260405T044331
CREATED:20230701T213948Z
LAST-MODIFIED:20230701T213948Z
UID:37327-1688396400-1688400000@coe.northeastern.edu
SUMMARY:CommLab Linked In Workshop
DESCRIPTION:COE graduate students are invited to join the CommLab’s interactive LinkedIn workshop where we’ll be optimizing your profile and learning how to effectively connect with people on the platform to build a strong professional network.  This workshop meets every other Monday on Zoom or in 306 Egan.  Drop-in’s welcome!
URL:https://coe.northeastern.edu/event/commlab-linked-in-workshop/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230706T150000
DTEND;TZID=America/New_York:20230706T160000
DTSTAMP:20260405T044331
CREATED:20230701T213916Z
LAST-MODIFIED:20230701T213916Z
UID:37330-1688655600-1688659200@coe.northeastern.edu
SUMMARY:Using LaTeX for CV/Resumes: A CommLab Workshop Series
DESCRIPTION:Looking to create a standout CV or resume that showcases your skills and experience in a professional way? COE graduate students are invited to join the CommLab’s LaTeX workshop! Our workshop provides support in CV/resume building using LaTeX\, and will give you a better and clearer understanding of different aspects of the LaTeX code. You’ll learn how to customize templates\, incorporate graphics and images\, and create tables and bibliographies. Plus\, you’ll have the opportunity to connect with a community of like-minded individuals who are interested in LaTeX and its potential applications. Don’t miss this chance to support\, learn\, and grow with The CommLab! Register to join us virtually on Zoom or in person in 306 Egan.
URL:https://coe.northeastern.edu/event/using-latex-for-cv-resumes-a-commlab-workshop-series-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230711T150000
DTEND;TZID=America/New_York:20230711T160000
DTSTAMP:20260405T044331
CREATED:20230710T134655Z
LAST-MODIFIED:20230710T134655Z
UID:37404-1689087600-1689091200@coe.northeastern.edu
SUMMARY:NSF Graduate Research Fellowship Program Writing Group
DESCRIPTION:The CommLab and the Khoury College Graduate Program are running a 7-week summer writing group for graduate students applying for the NSF Graduate Research Fellowship Program (GRFP). The virtual Zoom meetings begin Tuesday\, July 11 at 3pm ET and conclude on Tuesday\, August 22. Find out more and RSVP here: https://forms.gle/WLc4ZKBaGSEJLxah6
URL:https://coe.northeastern.edu/event/nsf-graduate-research-fellowship-program-writing-group/2023-07-11/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230712T173000
DTEND;TZID=America/New_York:20230712T183000
DTSTAMP:20260405T044331
CREATED:20230424T144427Z
LAST-MODIFIED:20230424T144427Z
UID:36806-1689183000-1689186600@coe.northeastern.edu
SUMMARY:Gordon Institute Virtual Information Session
DESCRIPTION:Learn how you can earn a Graduate Certificate in Engineering Leadership as a stand-alone certificate or in combination with one of twenty plus Master of Science degrees offered through Northeastern’s College of Engineering\, College of Science\, or Khoury College of Computer Sciences.  \nThe National Academy of Engineering recognized The Gordon Institute of Engineering Leadership (GIEL) for its innovative curriculum that combines technical education\, leadership capabilities\, and the “Challenge Project”: an opportunity for students to receive master’s level credit while working in industry.  \nBy aligning technical proficiency with leadership capabilities\, GIEL accelerates the development of high-potential engineers and prepares them to lead complex projects early in their careers. Upon completing the program\, more than 88% of the 2021 class reported increased leadership responsibility\, while more than 50% of the 2021 class reported being promoted within one year of graduation.  \nOur Director of Admissions will answer your application questions for Fall 2023.  \nYou will have the opportunity to hear from Alumni on how The Gordon Institute propelled their engineering careers. Program professors will also be present to answer curriculum questions. 
URL:https://coe.northeastern.edu/event/gordon-institute-virtual-information-session-15/
ORGANIZER;CN="Gordon Engineering Leadership program":MAILTO:gordonleadership@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230717T150000
DTEND;TZID=America/New_York:20230717T170000
DTSTAMP:20260405T044331
CREATED:20230714T191624Z
LAST-MODIFIED:20230717T134619Z
UID:37485-1689606000-1689613200@coe.northeastern.edu
SUMMARY:Poster Design and Editing: A CommLab Workshop Series
DESCRIPTION:COE Graduate students are invited to learn techniques\, develop\, edit and practice delivering your poster. Join the CommLab’s weekly meetings to improve your poster design and communication skills.  Drop in for some or all of the time.  This is a hybrid opportunity.  Join us on Zoom or in the Curry Student Center.  We will be in room 435 on Monday\, July 17 and in room 335 for the rest of July and August.
URL:https://coe.northeastern.edu/event/poster-design-and-editing-a-commlab-workshop-series/2023-07-17/
LOCATION:Curry Student Center\, 360 Huntington Ave.\, Boston\, MA\, 02115\, United States
GEO:42.3394629;-71.0885286
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Curry Student Center 360 Huntington Ave. Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave.:geo:-71.0885286,42.3394629
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230719T130000
DTEND;TZID=America/New_York:20230719T140000
DTSTAMP:20260405T044331
CREATED:20230717T140558Z
LAST-MODIFIED:20230717T140558Z
UID:37496-1689771600-1689775200@coe.northeastern.edu
SUMMARY:Small Talk for Networking: A CommLab Workshop Series
DESCRIPTION:Looking to improve your conversational skills and effortlessly connect with others?  This workshop series will equip you with the essential tools\, techniques\, and practice to connect with others\, whether you are attending networking events\, social gatherings\, or simply looking to strike up conversations with new acquaintances. Join us for these fun-filled sessions where we’ll explore icebreakers\, conversation starters\, and strategies to keep the dialogue flowing naturally. Don’t miss out on this opportunity to boost your confidence and unlock new connections. This is a virtual workshop series\, please register and join us Wednesdays on Zoom from 1-2pm ET.
URL:https://coe.northeastern.edu/event/small-talk-for-networking-a-commlab-workshop-series/2023-07-19/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230720T130000
DTEND;TZID=America/New_York:20230720T140000
DTSTAMP:20260405T044331
CREATED:20230711T140015Z
LAST-MODIFIED:20230711T140015Z
UID:37433-1689858000-1689861600@coe.northeastern.edu
SUMMARY:Qing Jin's PhD Dissertation Defense
DESCRIPTION:Title:Decoupling Efficiency-Performance Optimization for Modern Neural Networks \nDate: \n7/20/2023 \nCommittee Members: \nYanzhi Wang (Advisor); Prof. David Kaeli; Prof. Sunil Mittal; Prof. Jennifer Dy \nAbstract: \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-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230721T140000
DTEND;TZID=America/New_York:20230721T153000
DTSTAMP:20260405T044331
CREATED:20230718T135222Z
LAST-MODIFIED:20230718T135222Z
UID:37521-1689948000-1689953400@coe.northeastern.edu
SUMMARY:Daniel Uvaydov's PhD Dissertation Defense
DESCRIPTION:Title: Real-Time Spectrum Sensing for Inference and Control \nCommittee Members: \nProf. Tommaso Melodia (Advisor) \nProf. Kaushik Choudhury \nProf. Francesco Restuccia \nAbstract: \nThrough growing cellular innovations\, the usage and congestion of the wireless spectrum is increasing at incredible speeds. High demand and limited supply pose a resource issue known as the “spectrum crunch”. With the high diversity of users sharing a large portion of the spectrum to request and receive diverse services\, spectrum coordination becomes very difficult. Large scale device synchronization for spectrum coordination requires high overhead and more wireless transmissions further reducing spectrum resources. However\, by monitoring the spectrum\, otherwise known as spectrum sensing\, we can develop mechanisms where users can opportunistically take action based on the current state of the spectrum\, without need for direct coordination between devices. Spectrum sensing can enable the next generation of wireless applications ranging from opportunistic spectrum access to cognitive radio networks. The key unaddressed challenges of spectrum sensing are that (i) it requires very extensive and diverse datasets; (ii) it has to be performed with extremely low latency over varying bandwidths and must guarantee strict real-time processing constraints; (iii) its underlying algorithms need to be extremely accurate\, and flexible enough to work with different wireless bands and protocols to find application in real-world settings. This dissertation focuses on addressing these challenges in multiple wireless applications by utilizing Deep Learning (DL) techniques as the main vehicle of spectrum sensing for both inference and control. Algorithmic spectrum sensing has generally been model-based which limits its performance in diverse settings and environments\, for this reason we explore data-driven spectrum sensing algorithms. Mainly\, this work takes a holistic approach to address spectrum sensing problems from multiple directions with the overarching goal of developing the core building blocks for the next generation of intelligent\, AI-driven\, efficient spectrum sharing systems. By leveraging mechanisms such as data augmentation\, channel attention\, voting\, and segmentation we are able to push beyond the capabilities of existing DL techniques and create generalizable spectrum sensing algorithms. Furthermore we deploy different spectrum sensing solutions in real testbeds for over the air evaluations and applicable proof-of-concepts. The contributions of this work includes (i) multiple datasets and implementations for DL enabled spectrum sensing with applications in radio frequency and underwater; (ii) a method for tackling the core issue of dataset generation in supervised learning algorithms for spectrum sensing via a novel data augmentation technique; (iii) a study into one of the first ever semi-unsupervised approaches for wideband multi-class spectrum sensing.
URL:https://coe.northeastern.edu/event/daniel-uvaydovs-phd-dissertation-defense/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=432 ISEC 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230725T130000
DTEND;TZID=America/New_York:20230725T140000
DTSTAMP:20260405T044331
CREATED:20230721T142252Z
LAST-MODIFIED:20230721T142322Z
UID:37567-1690290000-1690293600@coe.northeastern.edu
SUMMARY:Batool Salehihikouei Phd Proposal Review
DESCRIPTION:Title:\nLeveraging Deep Learning on Multimodal Sensor Data for Wireless Communication: From mmWave Beamforming to Digital Twins \nCommittee Members:\nProf. Kaushik Chowdhury (Advisor)\nProf. Hanumant Singh\nProf. Josep Jornet\nDr. Mark Eisen \nAbstract:\nWith the widespread Internet of Things (IoT) devices\, a wide variety of sensors are now present in different environments. For example\, self-driving vehicles and automated warehouses depend on sensor information for navigation and management of the robots\, respectively. In this dissertation\, we present a paradigm\, where these sensors are re-purposed to assist network management in wireless communication\, especially when classic approaches fall short to provide the required quality of service (QoS). This thesis presents data-driven and AI-based methods\, where the multimodal sensor information is used for beamforming at the mmWave band\, and envisions a systematic framework for joint optimization of the navigation and network management in factory floor environments. In particular\, the contributions in this dissertation are as follows. First\, we present deep learning fusion algorithms\, where the inputs from a multitude of sensor modalities such as GPS (Global Positioning System)\, camera\, and LiDAR (Light Detection and Ranging) are combined towards predicting the optimum beam at the mmWave band. We prove that fusing the multimodal sensor data improves the prediction accuracy compared to using single modalities. Second\, we study the trade-off between the accuracy and cost of different learning strategies for multimodal beamforming. In this regard\, we make a case for using federated learning for beamforming at the mmWave band and demonstrate that it is the most successful learning strategy\, with respect to the communication overhead. Finally\, we take measures to further optimize the computation and communication overhead\, by incorporating a pruning strategy tailored to the disturbed nature of the federated learning systems. In the proposed research work\, we suggest using digital twins to overcome the challenges of scarcity of data and close-world assumption in deep learning algorithms. A digital twin is a replica of a real world entity\, which is typically used for studying the impact of any configuration settings in a safe\, digital environment. In this dissertation\, we propose using digital twins for generating training data for multimodal beamforming\, in unseen scenarios. Moreover\, we study a robotic industrial setting\, where the path planning policy is continuously updated by monitoring the dynamics of the real world\, constructing the digital twin\, and updating the policy.
URL:https://coe.northeastern.edu/event/batool-salehihikouei-phd-proposal-review/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230726T170000
DTEND;TZID=America/New_York:20230726T180000
DTSTAMP:20260405T044331
CREATED:20230724T134259Z
LAST-MODIFIED:20230724T134259Z
UID:37589-1690390800-1690394400@coe.northeastern.edu
SUMMARY:LinkedIn\, CV\, Resume: A CommLab Workshop Series
DESCRIPTION:Join our empowering LinkedIn\, CV\, Resume Workshop any Wednesday from 5 pm to 6 pm ET\, starting the 26th July. This collaborative space offers valuable tips and peer feedback to enhance your online profile and professional presence. Whether you’re a student or seasoned professional\, our community will help you optimize your LinkedIn profile\, CV\, and resume to stand out in today’s job market. Don’t miss this chance to learn\, grow\, and build a strong network together!  This is a hybrid workshop\, join virtually on Zoom or in person in room 206 Egan.  
URL:https://coe.northeastern.edu/event/linkedin-cv-resume-a-commlab-workshop-series/2023-07-26/
LOCATION:206 Egan\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3376753;-71.0888734
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=206 Egan 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0888734,42.3376753
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230801T100000
DTEND;TZID=America/New_York:20230801T113000
DTSTAMP:20260405T044331
CREATED:20230721T143101Z
LAST-MODIFIED:20230721T143101Z
UID:37563-1690884000-1690889400@coe.northeastern.edu
SUMMARY:Huan Wang's PhD Proposal Review
DESCRIPTION:Title: \nTowards Efficient Deep Learning in Computer Vision via Sparsity and Distillation \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. This thesis focuses on two overarching approaches\, network sparsity and knowledge distillation\, to enhance the efficiency of deep learning models in the context of computer vision. Network sparsity 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. In the proposal\, I will start with the background and major challenges of leveraging these techniques towards efficient deep learning. Then\, I shall present the potential solutions in various tasks (e.g.\, image classification\, image super-resolution\, neural rendering\, and text-to-image generation)\, with preliminary results to justify the efficacy of the proposed approaches. Finally\, a comprehensive outlook of the future work will conclude this proposal.
URL:https://coe.northeastern.edu/event/huan-wangs-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230802T173000
DTEND;TZID=America/New_York:20230802T183000
DTSTAMP:20260405T044331
CREATED:20230726T152112Z
LAST-MODIFIED:20230726T152112Z
UID:37616-1690997400-1691001000@coe.northeastern.edu
SUMMARY:Gordon Institute Virtual Information Session
DESCRIPTION:Learn how you can earn a Graduate Certificate in Engineering Leadership as a stand-alone certificate or in combination with one of twenty-three Master of Science degrees offered through Northeastern’s College of Engineering\, College of Science\, or Khoury College of Computer Sciences. \nThe National Academy of Engineering recognized The Gordon Institute of Engineering Leadership (GIEL) for its innovative curriculum that combines technical education\, leadership capabilities\, and the “Challenge Project”: an opportunity for students to receive master’s level credit while working in industry. \nBy aligning technical proficiency with leadership capabilities\, GIEL accelerates the development of high-potential engineers and prepares them to lead complex projects early in their careers. Upon completing the program\, more than 88% of the 2021 class reported increased leadership responsibility\, while more than 50% of the 2021 class reported being promoted within one year of graduation. \nOur Director of Admissions will answer your application questions for Fall 2023. \nYou will have the opportunity to hear from Alumni on how The Gordon Institute propelled their engineering careers. Program professors will also be present to answer curriculum questions.
URL:https://coe.northeastern.edu/event/gordon-institute-virtual-information-session-17/
ORGANIZER;CN="Gordon Engineering Leadership program":MAILTO:gordonleadership@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230803T100000
DTEND;TZID=America/New_York:20230803T110000
DTSTAMP:20260405T044331
CREATED:20230508T153859Z
LAST-MODIFIED:20230508T153859Z
UID:36933-1691056800-1691060400@coe.northeastern.edu
SUMMARY:Yu Yin's PhD Dissertation Defense
DESCRIPTION:“Synthetic Data Generator: Understanding Human Face & Body via Image Synthesis” \nCommittee Members:\nProf. Yun Fu (Advisor)\nProf. Sarah Ostadabbas\nProf. Ming Shao \nAbstract:\nThe community has long enjoyed the benefits of synthesizing data\, as it provides a reliable and controllable source for training machine learning models while reducing the need for data collection from the real world. Human face and body synthesis are especially appealing to research communities\, where model fairness and ethical deployment are critical concerns. However\, generating digit humans that are convincing\, realistic-looking\, identity-preserving\, and high-quality are still challenging in 2D and 3D image synthesis. \nThis dissertation investigates the potential for understanding human behavior by recreating it\, and can be broadly divided into three sections. (1) In Section one\, we explore the 2D image generation models and their interaction with face applications (i.e.\, landmark localization and face recognition tasks). Specifically\, super-resolution (SR) and landmark localization of tiny faces are highly correlated tasks. To this end\, we propose joint frameworks that enable face alignment and SR to benefit from one another\, hence enhancing the performance of both tasks. Moreover\, we demonstrate that face frontalization provides an effective and efficient way for face data augmentation and further improves face recognition performance in extreme pose scenarios. (2) In Section two\, we explore the 3D parametric generation models and how they support human body pose and shape estimation. Advancing technology to monitor our bodies and behavior while sleeping and resting is essential for healthcare. However\, keen challenges arise from our tendency to rest under blankets. To mitigate the negative effects of blanket occlusion\, we use an attention-based restoration module to explicitly reduce the uncertainty of occluded parts by generating uncovered modalities\, which further update the current estimation via a cyclic fashion. (3) In Section three\, we explore the 3D Nerf-based Generative models in generating high-quality images with consistent 3D geometry. We propose a universal method to surgically fine-tune these NeRF-GAN models in order to achieve high-fidelity animation of real subjects only by a single image.
URL:https://coe.northeastern.edu/event/yu-yins-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230808T100000
DTEND;TZID=America/New_York:20230808T110000
DTSTAMP:20260405T044331
CREATED:20230721T142808Z
LAST-MODIFIED:20230721T142808Z
UID:37565-1691488800-1691492400@coe.northeastern.edu
SUMMARY:Yukui Luo's PhD Dissertation Defense
DESCRIPTION:Title:\nSecuring FPGA as a Shared Cloud-Computing Resource: Threats and Mitigations \nCommittee Members:\nProf. Xiaolin Xu (Advisor)\nProf. Yunsi Fei\nProf. Xue Lin \nAbstract:\nWith the widespread adoption of cloud computing\, the demand for programmable hardware acceleration devices\, such as field-programmable gate arrays (FPGA)\, has increased. These devices benefit the growth of efficient hardware accelerators\, making cloud computing possible for a wide range of research and commercial projects\, including genetic engineering\, intensive online secure trading\, the Artificial Intelligence (AI) interface\, etc. To further improve the performance of FPGA-enabled cloud computing\, one promising technology is to virtualize the hardware resources of an FPGA device\, which allows multiple users to share the same FPGA. This solution can provide on-demand FPGA instances\, significantly improving the hardware utilization and energy efficiency of the cloud FPGA. However\, due to the hardware reconfigurability of FPGA\, current virtualization technologies used for multi-tenant CPU and GPU instances are incompatible with multi-tenant FPGA. \nWe aim to enhance the security of multi-tenant FPGA by defining the threat model and evaluating security concerns from the perspectives of confidentiality\, data integrity\, and availability. As part of this goal\, we constructed multi-tenant FPGA prototypes and demonstrated potential attacks. These attacks serve as preliminary steps toward developing a secure multi-tenant FPGA virtualization system. This system involves hardware and software co-design\, which extends the multi-tenant isolation from software to hardware\, ultimately resulting in a secure FPGA shared cloud computing service.
URL:https://coe.northeastern.edu/event/yukui-luos-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230808T103000
DTEND;TZID=America/New_York:20230808T113000
DTSTAMP:20260405T044331
CREATED:20230802T192515Z
LAST-MODIFIED:20230802T192515Z
UID:37694-1691490600-1691494200@coe.northeastern.edu
SUMMARY:Dinesh Murugan MS Thesis Defense
DESCRIPTION:Title: Advances in Modelling\, Control\, and Perception for Soft Robotics and Autonomous Vehicle Systems \nLocation on Campus: Snell Room \nCommittee Members:\nAdvisor: Prof. Milad Siami\nProf. Bahram Shafai\nProf. Rozhin Hajian – University of Massachusetts\, Lowell \nAbstract:\nIn this research project\, we investigate the distributed consensus and vehicle platoons control problem. We first investigate the performance deterioration of commensurate fractional-order consensus networks under exogenous stochastic disturbances. We formulate fractional-order differential equations for the network dynamics using Caputo derivatives and the Laplace transform\, and employ the H_2 norm of the dynamical system as a performance measure. By developing a graph-theoretic methodology\, we relate the structural specifications of the underlying graphs to the performance measure and explicitly quantify fundamental limits on the best achievable levels of performance in fractional-order consensus networks. We also establish new connections between the sparsity of the network and the performance measure\, characterizing fundamental tradeoffs that reveal the interplay between the two. Finally\, we provide numerical illustrations to verify our theoretical results\, which could help in the design of robust fractional-order control systems in the presence of disturbances. \nAdditionally\, the study examines the real-time application of the theoretical advancements on Quanser’s Qcars\, a scaled model vehicle used for academic purposes. The findings are highly relevant to the design and implementation of large-scale consensus networks and autonomous vehicle platoons\, as they emphasize the importance of balancing network density and update cycle speed for optimal performance. \nTo extend the research’s findings to viscoelastic based networks\, the interaction between agents is modeled as a fractional-order system.
URL:https://coe.northeastern.edu/event/dinesh-murugan-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230809T110000
DTEND;TZID=America/New_York:20230809T120000
DTSTAMP:20260405T044331
CREATED:20230802T192340Z
LAST-MODIFIED:20230802T192340Z
UID:37696-1691578800-1691582400@coe.northeastern.edu
SUMMARY:Nasim Soltani PhD Proposal
DESCRIPTION:Title: Deep Learning for the Physical Layer: From Signal Classification to Decoding \nLocation: ISEC 532 \nCommittee Members:\nProf. Kaushik Chowdhury (Advisor)\nProf. Stratis Ioannidis\nProf. Robert Nowak \nAbstract:\nThe growth in wireless spectrum usage has created new physical layer applications and intensified the importance of the existing ones. Physical layer applications ranging from device authentication to signal decoding and interpretation are traditionally handled by deterministic signal processing algorithms. Such algorithms\, while effective\, often require long sequences of data for decision making\, or need approximations of the environmental conditions\, such as noise models\, which may not be always correct in practical conditions. For these reasons\, traditional algorithms are not suitable for making quick decisions on the high rate wireless data with higher noise and interference that is a result of crowded spectrum. To this end\, deep learning-based methods have been explored extensively by the researchers to substitute for the traditional signal processing algorithms for the physical layer. This thesis explores novel methods in this area in the following parts: \nPart I – Signal classification: In this part\, we look at two distinct problems of waveform classification and Radio Frequency (RF) fingerprinting. In the first problem\, we study two use cases of modulation classification on edge devices\, followed by waveform classification and spectrum localization in the Citizen Broadband Radio Service (CBRS) band. In the second problem\, we look at RF fingerprinting that is classifying received signals in terms of subtle impairments that each transmitter leaves in its emitted waveform\, due to its hardware manufacturing imperfections. We propose methods to overcome the wireless channel effect for RF fingerprinting in both stationary transmitters on a large scale dataset (i.e.\, 5k WiFi devices)\, and identical hovering Unmanned Aerial Vehicles (UAVs) that transmit proprietary signals. \nPart II – Signal decoding: In this part\, we introduce our design of a modular machine learning (ML)-aided Orthogonal Frequency Division Multiplexing (OFDM) receiver that improves the bit error rate (BER) of the traditional receiver. We show how a neural network-based demapper block can be used for secure data transmission. Furthermore\, we show how an ML-aided receiver can provide the possibility of reducing communication overhead by obviating the need for the first field of preamble in WiFi signals. We show that reducing the preamble length contributes to higher throughput in WiFi networks\, without BER degradation. \nPart III – As the proposed work\, we will explore the use of active learning for smart sampling of training sets in wireless communications tasks. Active learning reduces the labeling overhead that is often performed using the compute-intensive traditional signal processing algorithms\, by intelligently selecting the most informative training samples to be labeled instead of labeling the whole set. We will also design an ML-life cycle control scheme to monitor and update the performance of an ML-aided 5G receiver\, when deployed in the field with varying environmental conditions.
URL:https://coe.northeastern.edu/event/nasim-soltani-phd-proposal/
LOCATION:532 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230809T150000
DTEND;TZID=America/New_York:20230809T163000
DTSTAMP:20260405T044331
CREATED:20230731T152624Z
LAST-MODIFIED:20230807T134836Z
UID:37662-1691593200-1691598600@coe.northeastern.edu
SUMMARY:Yuanyuan Li PhD Dissertation Defense
DESCRIPTION:Title: Sub-modularity in Cache Networks \nCommittee Members:\nProf. Stratis Ioannidis\nProf. Lili Su\nProf. Edmund Yeh \nAbstract:\nAs information-based demand surges\, distributed network services\, e.g.\, cache networks\, play an important role to mitigate network traffic. Cache networks are a natural abstraction for many applications\, including information-centric networks\, content delivery networks\, cloud computing\, and edge/wireless IoT. How to allocate resources (routing\, placing items in caches\, flow control\, etc.) in cache networks is a crucial problem\, as resources (storage space\, and bandwidths) are usually limited. Resource allocation in networks has been traditionally approached through classic convex optimization. However\, simple problems becomes combinotorial in cache networks\, which leads to NP-hardness. Enlightened by several works studying cache networks\, we identify a useful property\, submodularity\, which is the key to approximation algorithms solving those NP hard resource allocation problem in cache networks. \nLeveraging submodularity\, we study a cache network\, in which intermediate nodes equipped with caches can serve content requests\, from different angles. \nFirst\, we model this network as a universally stable queuing system\, in which packets carrying identical responses are consolidated before being forwarded downstream. We refer to resulting queues as $\info$ or counting queues\, as consolidated packets carry a counter indicating the packet’s multiplicity. Cache networks comprising such queues are hard to analyze; we propose two approximations: one via $\mminf$ queues\, and one based on $\info$ queues under the assumption of Poisson arrivals. We show that\, in both cases\, the problem of jointly determining (a) content placements and (b) service rates admits a poly-time\, $1-1/e$ approximation algorithm. We also show that our analysis\, with respect to both algorithms and associated guarantees\, extends to (a) counting queues over items\, rather than responses\, as well as to (b) queuing at nodes and edges\, as opposed to just edges. \nSecond\, we refer to the cost reduction enabled by caching as the caching gain\, and the product of the caching gain of a content request and its request rate as \emph{caching gain rate}. We aim to study \emph{fair} content allocation strategies through a utility-driven framework\, where each request achieves a utility of its caching gain rate\, and consider a family of $\alpha$-fair utility functions to capture different degrees of fairness. The resulting problem is an NP-hard problem with a non-decreasing submodular objective function. Submodularity allows us to devise a deterministic allocation strategy with an optimality guarantee factor arbitrarily close to $1-1/e$.  When $0 < \alpha \leq 1$\, we further propose a randomized strategy that attains an improved optimality guarantee\,  $(1-1/e)^{1-\alpha}$\, in expectation. \nThird\, we study a cache network\, and model the problem of jointly optimizing caching and routing decisions with link capacity constraints over an arbitrary network topology. This problem can be formulated as a continuous diminishing-returns(DR) submodular maximization problem under multiple continuous DR-supermodular constraints\, and is NP-hard. We propose a poly-time alternating primal-dual  heuristic algorithm\, in which primal steps produce solutions within $1-\frac{1}{e}$ approximation factor from the optimal. Through extensive experiments\, we demonstrate that our proposed algorithm significantly outperforms competitors. \nForth\, we study a cache network under arbitrary adversarial request arrivals. We propose a distributed online policy based on the online tabular greedy algorithm. Our distributed policy achieves sublinear $(1-\frac{1}{e})$-regret\, also in the case when update costs cannot be neglected. \nFinally\, we propose an {\em experimental design network} paradigm\, wherein learner nodes train possibly different Bayesian linear regression models via consuming data streams generated by data source nodes over a network. We formulate this problem as a social welfare optimization problem in which the global objective is defined as the sum of experimental design objectives of individual learners\, and the decision variables are the data transmission strategies subject to network constraints. We first show that\, assuming Poisson data streams\, the global objective is a continuous DR-submodular function. We then propose a Frank-Wolfe type algorithm that outputs a solution within a $1-1/e$ factor from the optimal. Our algorithm contains a novel gradient estimation component which is carefully designed based on Poisson tail bounds and sampling.
URL:https://coe.northeastern.edu/event/yuanyuan-li-phd-dissertation-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230811T140000
DTEND;TZID=America/New_York:20230811T150000
DTSTAMP:20260405T044331
CREATED:20230731T152837Z
LAST-MODIFIED:20230731T152837Z
UID:37660-1691762400-1691766000@coe.northeastern.edu
SUMMARY:Jinkun Zhang PhD Proposal Review
DESCRIPTION:Location: ISEC305 \nTitle: Low-latency forwarding\, caching and computation placement in data-centric networks \nCommittee Members:\nProf. Edmund Yeh (Advisor)\nProf. Stratis Ioannidis\nProf. Kaushik Chowdhury \nAbstract:\nWith the exponential growth of data- and computation-intensive network applications\, such as real-time augmented reality/virtual reality rendering and large-scale language model training\, traditional cloud computing frameworks exhibit inherent limitations. These limitations include significant round-trip delays caused by backhaul network capacity bottlenecks and exorbitant costs associated with centralized computing power\, e.g.\, training GPT-4 requires over 16\,000 A100 GPUs.\nTo address these challenges\, dispersed computing has emerged as a promising next-generation networking paradigm. By enabling geographically distributed nodes with heterogeneous computation capabilities to collaborate\, dispersed computing overcomes the bottlenecks of traditional cloud computing and facilitates in-network computation tasks\, including the training of large models. \nFurthermore\, in data-centric networking\, communication and computation are resolved around data names instead of host addresses.\nThe deployment of network caches\, by enabling data reuse\, offers substantial benefits for data-centric networks.\nFor instance\, consider a scenario where multiple machine learning applications seek to train different models simultaneously. These application could (partially) share data samples and intermediate results\, and carefully designed data-reusing mechanisms become necessary. Optimal caching of data or intermediate results can significantly reduce the overall training cost\, compared to each application independently gathering and transmitting data. \nTo efficiently manage computation and storage resources in heterogeneous data-centric networks\, several frameworks have been proposed with different design objectives\, such as optimizing throughput or incorporating multicast flows. However\, previous approaches have failed to minimize average user delay despite the latency sensitivity of numerous real-world applications. \nThis proposal aims to address this gap by introducing a low-latency framework that jointly optimizes packet forwarding\, storage deployment\, and computation placement. The proposed framework effectively supports data-intensive and latency-sensitive computation applications in data-centric networks with heterogeneous communication\, storage\, and computation capabilities. \nSpecifically\, to minimize user latency in congestible networks\, we model delays caused by link transmissions and CPU computations using\ntraffic-dependent nonlinear functions. We formulate the joint forwarding\, caching\, and computation problem as an NP-hard mixed-integer non-submodular optimization\, for which no constant-factor approximation algorithms are currently known. To make progress\, we approach the joint problem by dividing it into two subproblems: the joint forwarding/computation problem and the joint forwarding/caching problem. Despite the non-convexity of the former subproblem\, we provide a set of sufficient optimality conditions that lead to a distributed algorithm with polynomial-time convergence to the global optimum. For the latter subproblem\, we demonstrate its NP-hardness and non-submodularity\, even after continuous relaxation. We show that the objective function is a sum of a convex function and a geodesic convex function\, and propose a set of conditions that provide a finite bound from the optimum. To the best of our knowledge\, our method represents the first analytical progress in addressing the joint caching and forwarding problem with arbitrary topology and non-linear costs. Furthermore\, our theoretical bound leads to a constant-factor approximation under additional assumptions. \nAs future work\, we propose to develop a novel in-network large model training framework\, building upon the aforementioned method.\nDue to the substantial model size and extensive data samples required for training\, centralized model storing and training are nearly infeasible for small and intermediate service providers.\nConsequently\, we will adopt horizontal model partitioning and distribute different model layers across the network nodes through caching.\nData samples or batches are input into the network and undergo the forward-backward procedure for training. Our objective is to jointly optimize data forwarding and model/computation placement\, thereby minimizing the total cost of transmission\, computation\, and storage. \nFurthermore\, we introduce several network resource allocation optimization problems related to data-centric networking\, thereby expanding the scope of our proposal.
URL:https://coe.northeastern.edu/event/jinkun-zhang-phd-proposal-review/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230815T153000
DTEND;TZID=America/New_York:20230815T163000
DTSTAMP:20260405T044331
CREATED:20230816T150241Z
LAST-MODIFIED:20230816T150241Z
UID:37860-1692113400-1692117000@coe.northeastern.edu
SUMMARY:Sumegha Singhania MS Thesis Defense
DESCRIPTION:Title: Exploring Log of RGB Space as a Better Input for Computer Vision Tasks \nCommittee Members:\nProf. Bruce Maxwell (Advisor)\nProf. Hanumant Singh\nProf. David Rosen\nProf. Mahdi Imani \nAbstract:\nThere are specific\, physics-based rules that govern the interaction of light and matter. Though studied extensively in the greater computer vision community\, these rules are largely broken by common image processing techniques like JPEG compression and sRGB conversion. While the reliability and usability of color and intensity found in RAW images might better train networks to successfully complete vision-based tasks\, these smaller\, more heavily-processed formats have become the standard input for training sets. As a result\, many of the images used to train neural networks do not retain the inherent structure that would enable neural networks to learn more general rules that exist in the natural world. \nWe hypothesize that using linear RGB or log RGB images\, which preserve the physics of reflection\, can simplify the learning process for certain vision tasks\, enhance overall robustness and performance\, and provide invariance to visual variations that exist in real-world vision applications. Our research demonstrates that employing linear and log RGB images to train deep networks for the task of object detection improves their performance when using the same network architecture and the same set of training images. Additionally\, we also show that the networks trained on linear and log RGB show greater resilience to variations in intensity and color balance. Specifically\, the network trained on linear and log RGB inputs shows invariance to intensity and color balance variations that were not encountered during training\, while the network trained on the same images in sRGB JPEG format experiences significant performance degradation. To understand the reasons behind this disparity\, we analyze and visualize low-level features in log RGB\, linear RGB\, and JPEG data. Our findings reveal that the log space preserves certain relevant features across variations in intensity and color balance.
URL:https://coe.northeastern.edu/event/sumegha-singhania-ms-thesis-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230817T090000
DTEND;TZID=America/New_York:20230817T110000
DTSTAMP:20260405T044331
CREATED:20230802T192116Z
LAST-MODIFIED:20230802T192116Z
UID:37698-1692262800-1692270000@coe.northeastern.edu
SUMMARY:Jagatpreet Nir PhD Proposal
DESCRIPTION:Title: Low Contrast Visual Sensing and Inertial Navigation in GPS Denied Environments \nCommittee Members:\nProf. Hanumant Singh\nProf. Martin Ludvigsen\nProf. Pau Closas\nProf. Michael Everrett \nAbstract:\nVisual inertial navigation has shown remarkable performance in publicly available datasets\, assuming certain ideal conditions such as textured scenes\, uniform illumination\, and static environments. However\, real-world scenarios often violate these assumptions\, resulting in significant visual degradation. Consequently\, the classical visual navigation pipelines fail and produce erroneous results\, rendering these systems ineffective for demanding field robotic missions. \nThis research aims to enhance the robustness of visual-inertial systems in visually degraded situations\, taking a comprehensive approach from both systems and algorithm perspectives. The work encompasses two primary objectives. Firstly\, it focuses on refining the characterization of MEMS-based inertial sensors and their error propagation in position\, while proposing improved dead-reckoning algorithms. Secondly\, it explores the performance limits of visual navigation under moderate to extreme visual degradation and investigates novel algorithms that leverage deep learning methods to bolster the visual navigation engine. To validate the efficacy of these advancements\, new datasets comprising drone and underwater robot scenarios are utilized\, demonstrating the applicability of this work in field robotic applications. \nBy addressing the limitations of existing visual-inertial navigation systems and developing robust algorithms\, this research aims to significantly enhance the reliability and performance of such systems in visually degraded environments\, thus expanding their potential for real-world applications in demanding field robotic missions.
URL:https://coe.northeastern.edu/event/jagatpreet-nir-phd-proposal/
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DTSTART;TZID=America/New_York:20230817T103000
DTEND;TZID=America/New_York:20230817T110000
DTSTAMP:20260405T044331
CREATED:20230817T143057Z
LAST-MODIFIED:20230817T143057Z
UID:37892-1692268200-1692270000@coe.northeastern.edu
SUMMARY:Rohit Rajput MS Thesis Defense
DESCRIPTION:Title:Towards Autonomous Multi-Modal Mobility Morphobot (M4) Robot: Traversability Estimation and 3D Path Planning \nLocation: ISEC 632 & Zoom \nCommittee Members:\nProf. Rifat Siphai\nProf. Hanumant Singh\nProf. Alireza Ramezani (advisor) \nAbstract:\nThis thesis enhances the autonomy of the M4 (Multi-Modal Mobility Morphobot) robot\, designed for Mars and rescue missions. The research enables the robot to autonomously select its locomotion mode and path in complex terrains. Focusing on walking and flying modes\, a Gazebo simulation and custom perception-navigations pipelines are developed. Leveraging deep learning\, the robot determines optimal mode transitions based on a 2.5D map. Additionally\, an energy-efficient path planner is implemented and validated in simulations. The contributions demonstrate scalability for future mode integrations. The M4 robot showcases intelligent mode switching\, efficient navigation\, and reduced energy consumption\, bringing us closer to fully autonomous multi-modal robots for exploration and rescue missions. This work paves the way for future advancements in autonomous robotics\, with the ultimate vision of deploying the M4 robot for exploration and rescue tasks\, making a significant impact in the quest for intelligent and versatile robotic systems.
URL:https://coe.northeastern.edu/event/rohit-rajput-ms-thesis-defense/
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