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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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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230703T150000
DTEND;TZID=America/New_York:20230703T160000
DTSTAMP:20260523T184616
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:20260523T184616
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/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230711T150000
DTEND;TZID=America/New_York:20230711T160000
DTSTAMP:20260523T184616
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:20260523T184616
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
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230717T150000
DTEND;TZID=America/New_York:20230717T170000
DTSTAMP:20260523T184616
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230719T130000
DTEND;TZID=America/New_York:20230719T140000
DTSTAMP:20260523T184616
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:20260523T184616
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/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230721T140000
DTEND;TZID=America/New_York:20230721T153000
DTSTAMP:20260523T184616
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230725T130000
DTEND;TZID=America/New_York:20230725T140000
DTSTAMP:20260523T184616
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:20260523T184616
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
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