BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Electrical & Computer Engineering - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Electrical & Computer Engineering
X-ORIGINAL-URL:https://ece.northeastern.edu
X-WR-CALDESC:Events for Department of Electrical & Computer Engineering
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20200308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20201101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211006T110000
DTEND;TZID=America/New_York:20211006T120000
DTSTAMP:20260519T225402
CREATED:20211004T224056Z
LAST-MODIFIED:20211004T224056Z
UID:5222-1633518000-1633521600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Bin Sun
DESCRIPTION:PhD Proposal Review: Lightweight Neural Networks via Factorization \nBin Sun \nLocation: Zoom Link \nAbstract: Deep learning has become popular in recent years primarily due to powerful computing devices such as GPUs. However\, many applications such as face alignment\, image classification\, and gesture recognition need to be deployed to multimedia devices\, smartphones\, or embedded systems with limited resources. Thus\, there is an urgent need for high-performance but memory-efficient deep learning models. For this\, we design several lightweight deep learning models for different tasks with factorization strategies. \nSpecifically\, we constructed a lightweight face alignment model by proposing a factorization-based deep convolution module named Depthwise Separable Block (DSB) and a light but practical module based on the spatial configuration of the faces. Experiments on four popular datasets verify that Block Mobilenet has better overall performance with less than 1MB storage size. Besides the face analysis application\, we also explored a general\, lightweight deep learning module for image classification with low-rank pointwise residual (LRPR) convolution\, called LRPRNet. Essentially\, LRPR aims at using a low-rank approximation to factorize the pointwise convolution while keeping depthwise convolutions as the residual module to rectify the LRPR module. Moreover\, our LRPR is quite general and can be directly applied to many existing network architectures.\nDue to the success of the factorization strategy on image-based data\, we extended factorization on time sequence data for Sign Language Recognition (SLR). We achieved the first rank in the challenge of SLR with the help of our proposed novel Separable Spatial-Temporal Convolution Network (SSTCN)\, which divides a 3D convolution on joint features into several stages \, which help the SSTCN achieve higher accuracy with fewer parameters.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-bin-sun/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211006T170000
DTEND;TZID=America/New_York:20211006T200000
DTSTAMP:20260519T225402
CREATED:20210929T180818Z
LAST-MODIFIED:20210929T181335Z
UID:5191-1633539600-1633550400@ece.northeastern.edu
SUMMARY:Showcase of Opportunities for Undergraduate Research and Creative Endeavor (SOURCE)
DESCRIPTION:Calling all Huskies! Learn more about what cutting-edge research and creative endeavor look like at Northeastern. This is your chance to talk one-on-one with faculty from across the colleges about their research – and how you can get involved in projects during your time at Northeastern. Not everyone will have open positions right now but you’ll get a chance to see the range of work that’s happening and begin to make connections that you can build for the rest of your time at Northeastern. \nBefore you attend\, here’s some advice to consider. \n\nTake a look and see who’ll be there. We have an online database of attendees. Go through it and learn more about the project areas and visit the researcher/creative practitioner web pages. This will help prepare you for a conversation and help you ask any questions you might have.\nGet ready for a conversation from your end. Think about goals you have \, as well as your current interests and skills and those that you want to develop. How does what you have read about the various projects align with what you know about yourself and your interests? Take a minute to practice a simple declarative sentence\, “My name is ____________. I study ____________. I think that this part of your work ____________ is interesting because of this intellectual reason/moral imperative/grand impact ____________.  I’d love to learn more about it because of this previous experience ____________ and these skills ____________ and my long-term interest in ____________.  How I can get involved?” You might not know how to fill in all of the blanks right now (that’s why you’re in school) but see how close you can get.  When you know who you are\, what you value\, and how you can contribute — and get some practice saying it out loud– being confident becomes easier.\nRemember\, not every faculty member will have open positions now — but a good impression lasts a long time. You can’t go wrong with being polite and courteous.   Address faculty members as Professor until told otherwise (better to err on the side of formality).\nKeep in mind your time! If you want to be involved in research or creative practice\, a good thing to keep in mind is that faculty members will commit a lot of time to training and mentoring their undergraduates. They’re investing time\, energy\, and expertise in their mentees and want to know that you will make time for the projects\, show up consistently\, and ideally be with them for longer than a semester if possible. The learning curve of most projects is steep and it takes some time to get to the meatiest parts of the work. Be honest with yourself about the commitment you can make\, be frank with your faculty mentors\, and stick to your word. Communication and honesty in relationships\, including the mentoring relationship\, is key.\n\nSOURCE is a collaboration between Bouvé College of Health Sciences; College of Arts\, Media and Design; College of Engineering; College of Science; College of Social Sciences and Humanities; D’Amore-McKim School of Business; and Khoury College of Computer Science. It is coordinated by Undergraduate Research and Fellowships on behalf of the Office of the Chancellor.
URL:https://ece.northeastern.edu/event/showcase-of-opportunities-for-undergraduate-research-and-creative-endeavor/
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:20211014T120000
DTEND;TZID=America/New_York:20211014T130000
DTSTAMP:20260519T225402
CREATED:20211013T001304Z
LAST-MODIFIED:20211013T001304Z
UID:5239-1634212800-1634216400@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Tirthak Patel
DESCRIPTION:PhD Proposal Review: Toward System Software Stack for NISQ–era Quantum Computers \nTirthak Patel \nLocation: Zoom Link \nAbstract: Despite rapid progress in quantum computing in the last decade\, the limited usability of quantum computers remains a major roadblock toward the wider adoption of quantum computing. Prohibitively high error rates on existing Near-term Intermediate-Scale Quantum (NISQ) computers limit their usability even for quantum-advantage-proven algorithms (that is\, algorithms that are infeasible or orders of magnitude slower on classical computers). As a result\, the executions of these algorithms on existing quantum computers are highly erroneous and produce noisy program outputs. Currently\, quantum computing programmers lack system software tools and methods to estimate the correct output from these erroneous executions. \nThis dissertation demonstrates how to extract correct program output from noisy executions on today’s erroneous quantum computers. In particular\, this dissertation describes the design and implementation of a suite of cross-layer system software for extracting meaningful output from the erroneous executions using hardware-level quantum pulse control\, noise-aware quantum compilation\, and post-execution error mitigation. The real-system prototypes and experimental evaluation on IBM quantum computers demonstrate how specific quantum mechanics properties\, hardware-level pulse control\, and post-execution statistical processing can be put together to improve the usability of today’s quantum computers transparently. This dissertation achieves this without requiring user intervention\, domain knowledge about quantum algorithms\, or additional quantum hardware support. \nThis dissertation opens up new research avenues for hybrid quantum-classical computing and lowers the barrier to entry for quantum computing research via open-sourcing multiple novel datasets and system software frameworks (independently verified and results reproduced by other researchers in the community).
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-tirthak-patel/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211022T123000
DTEND;TZID=America/New_York:20211022T133000
DTSTAMP:20260519T225402
CREATED:20211020T191155Z
LAST-MODIFIED:20211020T191155Z
UID:5252-1634905800-1634909400@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Chengju Yu
DESCRIPTION:PhD Proposal Review: Development of Interface-Engineered Thin Films and Magnetodielectric Bulk Composites for MMIC Applications \nChengju Yu \nLocation: Zoom Link \nAbstract: Magnetodielectric materials are ubiquitous in electronic\, energy\, automotive\, communication\, and medical systems over radio frequency bands from high frequency to quasi-optical frequencies. With recent developments in modern power and communication technologies\, improvements in magnetic materials and related components have attracted a great deal of attention from academic and industrial research groups.\nIn this proposal review\, we demonstrate multiple paths to the development of next generation magnetodielectric thin films and bulk composites that offer disruptive advances to performance and size reduction\, including:\n(i) Consistent and reliable processing protocols are established using interface-engineered barium magnetoplumbite films deposited on Si-polar SiC substrates with AlN capping layers and MgO nucleation layers for microwave and millimeter-wave monolithic integrated circuits (MMICs);\n(ii) Both thin and thick yttrium iron garnet films are achieved using PLD and LPE with outstanding crystalline and magnetic properties to meet the needs of magnonics and spintronics technologies; (iii) Inductor cores are developed for power generation\, conversion\, conditioning functions for use in power electronic systems and high-power pulse generators operating at 100s kHz and 100s MHz frequencies\, respectively. Power loss and thermal management models of non-linear magnetic inductors are established and implemented with viable paths demonstrated using interface-engineered composites as a means of achieving high magnetization\, high permeability\, low core losses.\nThe common theme of all three projects is the engineering of the chemistry\, structure\, magnetic and electric properties of the interface between the principal layers\, films\, and grains that constitute the product in order to optimize performance.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-chengju-yu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211027T140000
DTEND;TZID=America/New_York:20211027T150000
DTSTAMP:20260519T225402
CREATED:20211021T174450Z
LAST-MODIFIED:20211021T174450Z
UID:5254-1635343200-1635346800@ece.northeastern.edu
SUMMARY:LEADERs Event: AI Challenges in the Deployment of Advanced Driver-Assistance Systems
DESCRIPTION:This presentation will help attendees learn about integrating AI in auto industry. While AI has been able to achieve remarkable success over the last 10 years\, can it really be trusted?  What does trustworthy AI look like in the context of connected vehicles and advanced driver-assistance systems (ADAS)?  To create an ADAS for all\, it’s paramount that the AI systems in the vehicle be trusted and work for everyone.  This is especially important given the nature of ADAS as a safety-critical\, cyber-physical\, and people-centric system of systems.  In this talk\, the presenter will outline a number of challenges in building AI systems which could potentially be deployed in future ADAS. \nSpeaker Dr. Jacob Bond leads trustworthy AI research at General Motors R&D.  In addition to his work on ensuring AI systems in the vehicle can be trusted\, his research looks at how to keep AI systems private and how to ensure cloud and vehicle systems can establish secure communications.  After receiving a Ph.D. in computational mathematics and cryptography from Purdue University\, he joined General Motors’ Product Cybersecurity organization\, focusing on applications of public-key cryptography.  Jacob then began investigating the security of AI systems\, moving to GM R&D and expanding his work to encompass the trustworthiness of AI systems.
URL:https://ece.northeastern.edu/event/leaders-event-ai-challenges-in-the-deployment-of-advanced-driver-assistance-systems/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211028T100000
DTEND;TZID=America/New_York:20211028T110000
DTSTAMP:20260519T225402
CREATED:20211025T211553Z
LAST-MODIFIED:20211025T211553Z
UID:5259-1635415200-1635418800@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Hongjia Li
DESCRIPTION:PhD Dissertation Defense: Automation Design and DNN Acceleration Frameworks: from software implementation to hardware physical design \nHongjia Li \nLocation: Northeastern Zoom Link \nAbstract: With the breakthrough of Deep Neural Networks (DNNs) in the past decade\, neural network-based computer vision has made huge progress\, achieving exceptional performance. Tasks such as object detection\, activity detection\, and medical diagnosis are deployed in a wide range of applications\, including autonomous driving\, robot vision and training\, human-computer interaction\, and augmented reality. To increase the demand of application accuracy\, DNN models are tuned to large scales by adding more parameters and layers. Meanwhile\, mobile devices are rapidly becoming the central computer and carrier for deep learning tasks. However\, real-time execution has been limited due to the computation/storage resource constraints on mobile devices.\nThe first part of this dissertation\, I will present our unified real-time mobile acceleration of DNNs framework\, seamlessly integrating hardware-friendly\, structured model compression with mobile-targeted compiler optimization. The goal of our framework is to provide an unprecedented\, real-time performance of such large-scale neural network inference using simply off-the-shelf mobile devices. Our proposed fine-grained block-based pruning scheme can be universally applicable to all types of DNN layers\, such as CONV layers with different kernel sizes and fully connected layers. Different weight pruning schemes\, such as unstructured pruning\, filter/column pruning\, and our block-based pruning\, are analyzed and compared given the specific deep learning problems. To validate our framework\, various applications are implemented and demonstrated\, object detection\, medical diagnosis. All applications can achieve real-time inference on mobile devices\, outperforming the current mobile acceleration framework by up to 6.7X in speed.\nFor the second part of this dissertation\, I will dive into an efficient automate framework for Adiabatic Quantum-Flux-Parametron (AQFP) technology\, meeting the unique features and constraints. Superconductive electronics (SCE) based on the Josephson junction (JJ) single flux quantum (SFQ) logic cells have evolved into a within-reach “beyond-CMOS” technology. Placement is the primary step in physical design of the electronic systems as it directly affects the maximum frequency and routability of circuits. Algorithms for global placement\, the core step in the placement process\, typically minimize the total wirelength of a design as the main objective as it indirectly affects the routability and timing of circuits. Although minimizing the total wirelength improves the timing of the circuit in general\, it does not directly target optimizing the delay of timing critical paths. Timing and routability driving placement methods are therefore needed. The currently mature design automation tools for CMOS cannot be directly applied to the design of superconducting electronics. In this dissertation\, I will present our proposed timing-aware AQFP-specific placement and routing framework\, given a path balanced AQFP netlist with clock phases. The proposed framework will reduce the solution complexity by making effective use of the row-wise placement/routing opportunity as each AQFP cell is assigned to a specific row (clock phase). \n 
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-hongjia-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211029T100000
DTEND;TZID=America/New_York:20211029T110000
DTSTAMP:20260519T225402
CREATED:20211020T191027Z
LAST-MODIFIED:20211020T191027Z
UID:5250-1635501600-1635505200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Cesar Antonio Galvez Nunez
DESCRIPTION:PhD Proposal Review: Fault Location in Radial and Meshed Networks Containing Distributed Energy Resources (DERs) \nCesar Antonio Galvez Nunez \nLocation: Microsoft Teams Link \nAbstract: Rapidly increasing numbers of Distributed Energy Resources (DERs) connected to transmission and distribution networks via Inverter Based Power Sources (IBPSs) introduce new challenges in detecting and locating faults. Distribution networks are historically designed to operate as radial systems with unidirectional power flows\, which may no longer hold true due to the presence of large numbers of IBPSs. The commonly used impedance-based fault location methods are no longer reliable due to the limitations imposed by unknown fault resistance\, asymmetry of lines\, and presence of IBPSs\, which need to comply with the new grid codes for Fault Ride Through (FRT) requirements. In this proposal\, a new fault location method that can be used for radial and meshed networks containing DERs and addresses the limitations of conventional methods mentioned above will be introduced. The approach requires a limited number of digital fault recorders (DFR) to be placed in the network and uses the Discrete Wavelet Transform (DWT) to compute the first arrival times of fault-generated traveling waves. The proposal first presents a new two-terminal fault location technique used strictly for radial distribution networks\, and then extends this to the general case of combined transmission and distribution networks with radial or meshed configurations. The method is further extended to be applied to hybrid AC/DC complex transmission grids containing DERs and High Voltage Direct Current (HVDC) lines. Preliminary results will be presented illustrating these methods on typical power grids and fault scenarios.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-cesar-antonio-galvez-nunez/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211029T110000
DTEND;TZID=America/New_York:20211029T120000
DTSTAMP:20260519T225402
CREATED:20211028T183932Z
LAST-MODIFIED:20211028T184125Z
UID:5273-1635505200-1635508800@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Ramtin Khalili
DESCRIPTION:PhD Proposal Review: Efficient State and Parameter Estimation in Three-Phase Power Systems \nRamtin Khalili \nLocation: Microsoft Link \nAbstract: As the number of renewable energy sources\, bulk energy storage systems\, and non-conventional loads increase and connect to the power grid not only at transmission but also sub-transmission and distribution levels\, three-phase unsymmetrical network monitoring becomes necessary for reliable operation and control of the overall power grid. The use of modal decomposition of measurement equations has already been shown to simplify the formulation and resulting computational complexity of three-phase state estimation of systems where all the transmission lines are three-phase and fully transposed. When there are untransposed and/or mixed-phase lines\, modal decomposition can no longer fully decouple the three-phase measurement equations. This shortcoming is eliminated by a simple yet practical solution based on the commonly used numerical compensation techniques. Thus\, it enables the application of the powerful decoupling approach to any type of three-phase networks which may contain untransposed or mixed-phase lines and are fully observable by PMUs. This implicit restriction is then removed by using a transformation that enables the use of SCADA measurements which are more commonly available in power grids. Furthermore\, It has been shown that network parameter errors can bias the state estimation solution. Network parameter errors are common due to aging\, changes in the ambient temperature\, human data entry error\, etc. So\, an efficient approach is proposed to detect and correct the network parameter errors in three-phase untransposed transmission lines. Preliminary results to illustrate the performance of the proposed methods and associated algorithms will be presented using different test systems.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-ramtin-khalili/
END:VEVENT
END:VCALENDAR