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DTSTART;TZID=America/New_York:20230411T120000
DTEND;TZID=America/New_York:20230411T130000
DTSTAMP:20260426T031440
CREATED:20230405T175216Z
LAST-MODIFIED:20230405T175216Z
UID:6248-1681214400-1681218000@ece.northeastern.edu
SUMMARY:Tirthak Patel's Dissertation Defense
DESCRIPTION:“Robust System Software for Quantum Computing” \nCommittee Members: \nProf. Devesh Tiwari (Advisor) \nProf. David Kaeli \nProf. Ningfang Mi \nProf. Gene Cooperman \nProf. Kenneth Brown \nAbstract: \nDespite rapid progress in quantum computing in the last decade\, the limited usability of quantum computers remains a major roadblock toward its wider adoption. Current noisy intermediate-scale quantum (NISQ) computers produce highly erroneous program outputs for quantum-advantage-proven algorithms — that is\, algorithms that are infeasible or orders of magnitude slower on classical supercomputing and high-performance computing (HPC) clusters. Unfortunately\, currently\, quantum computing programmers lack robust system software tools and methods to make meaningful use of erroneous program executions on quantum computers. \nThis lack of capability is the core motivation behind the fundamental question this dissertation poses: “can we build system software tools for programmers to make the quantum program execution and output meaningful on NISQ machines?” This dissertation answers this question in the affirmative— experimentally demonstrating on real-system quantum computers that it is possible to extract near-accurate program output from noisy executions on today’s erroneous quantum computers\, ironically using classical HPC resources and knowledge. This dissertation demonstrates how to achieve this goal without requiring user intervention\, domain knowledge about quantum algorithms\, or additional quantum hardware support. \nUnfortunately\, as this dissertation uncovers\, progressing toward making quantum computers usable is a double-edged sword. In the near future\, only a few entities in the world may have access to powerful quantum computers\, and these quantum computers will be used to solve previously-unsolved large-scale optimization problems\, possibly without an explicit trust model between the service provider and the customer. Therefore\, this dissertation envisions that the solutions to such large-scale optimization problems will be considered sensitive and will need to be protected. This dissertation takes the first few steps toward preparing us for that future by developing a novel method that intelligently obfuscates near-accurate program output and quantum circuit structure to preserve a customer’s privacy under a specified computation model and resource availability. \nThe approaches introduced in this dissertation open up new research avenues for hybrid quantum-classical computing and lower the barrier to entry for quantum computing research for the experimental computer systems and HPC community by open-sourcing multiple novel datasets and software frameworks implemented for real-system quantum computers. \nCandidate Bio: \nTirthak Patel is an incoming Assistant Professor in the Department of Computer Science at Rice University; currently\, a PhD candidate at Northeastern University\, advised by Professor Devesh Tiwari. Tirthak conducts systems-level research at the intersection of quantum computing and high-performance computing (HPC). His research contributions have appeared at rigorously peer-reviewed publication venues including ASPLOS\, Supercomputing (SC)\, HPDC\, HPCA\, and USENIX FAST\, and have been recognized with multiple award distinctions. He has received the ACM-IEEE CS George Michael Memorial HPC Fellowship\, the NSERC Alexander Graham Bell Canada Graduate Scholarship\, and the Northeastern University Outstanding Graduate Student in Research award\, for his research contributions toward making noisy quantum computing systems useful and helping HPC programmers solve computationally challenging problems.
URL:https://ece.northeastern.edu/event/tirthak-patels-dissertation-defense/
LOCATION:442 Dana\, 360 Huntington Ave\, 442 DA\, Boston\, MA\, 02115\, United States
GEO:42.3387508;-71.0923044
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230404T123000
DTEND;TZID=America/New_York:20230404T133000
DTSTAMP:20260426T031440
CREATED:20230328T174955Z
LAST-MODIFIED:20230328T174955Z
UID:6229-1680611400-1680615000@ece.northeastern.edu
SUMMARY:Cheng Gongye's MS Thesis Defense
DESCRIPTION:“Using Floating-Point Timing Side-Channels to Reverse Engineer Deep Neural Networks” \nCommittee Members:\nProf. Yunsi Fei (Advisor)\nProf. Aidong Ding\nProf. Xiaolin Xu \nAbstract: \nTrained Deep Neural Network (DNN) models have become valuable intellectual property. A new attack surface has emerged for DNNs: model reverse engineering. Several recent attempts have utilized various common side channels. However\, recovering DNN parameters\, weights and biases\, remains a challenge. In this paper\, we present a novel attack that utilizes a floating-point timing side channel to reverse-engineer parameters of multi-layer perceptron (MLP) models in software implementation\, entirely and precisely. To the best of our knowledge\, this is the first work that leverages a floating-point timing side channel for effective DNN model recovery.
URL:https://ece.northeastern.edu/event/cheng-gongyes-ms-thesis-defense/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230403T100000
DTEND;TZID=America/New_York:20230403T110000
DTSTAMP:20260426T031440
CREATED:20230320T205517Z
LAST-MODIFIED:20230320T205517Z
UID:6217-1680516000-1680519600@ece.northeastern.edu
SUMMARY:Jared Miller Ph.D Defense/Proposal Announcement
DESCRIPTION:“Safety Analysis for Nonlinear and Time-Delay Systems using Occupation Measures” \nInternational Village 022 \nCommittee Members:\nProf. Mario Sznaier (Advisor)\nProf. Octavia Camps\nProf. Bahram Shafai\nProf. Eduardo Sontag\nProf. Didier Henrion (LAAS-CNRS) \nAbstract:\nThis research extends an occupation measure framework to analyze the behavior and safety of dynamical systems. A motivating application of trajectory analysis is in peak estimation\, which finds the extreme values of a state function along trajectories. Examples of peak estimation include finding the maximum height of a wave\, voltage on a power line\, speed of a vehicle\, and infected population in an epidemic. Peak estimation can be applied towards safety quantification\, such as by measuring the safety of a trajectory by its distance of closest approach to an unsafe set. \nA finite-dimensional but nonconvex peak estimation problem can be converted into an infinite-dimensional linear program (LP) in measures\, which is in turn bounded by a convergent sequence of semidefinite programs. The LP is posed in terms of an initial\, a terminal\, and an occupational measure\, where the occupation measure contains all possible information about the dynamical systems’ trajectories. This research applies measure-based methods towards safety quantification (e.g. distance estimation\, control effort needed to crash)\, hybrid systems\, bounded-uncertain systems (including for data-driven analysis)\, stochastic systems\, and time-delay systems. The modularity of this measure-based framework allows for multiple problem variations to be applied simultaneously (e.g. distance estimation under time-delays)\, and for optimization models to be synthesized using MATLAB. Solving these optimization problems results in certifiable guarantees on system performance and behavior.
URL:https://ece.northeastern.edu/event/jared-miller-ph-d-defense-proposal-announcement/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230322T180000
DTEND;TZID=America/New_York:20230322T190000
DTSTAMP:20260426T031440
CREATED:20230131T012350Z
LAST-MODIFIED:20230131T012350Z
UID:6072-1679508000-1679511600@ece.northeastern.edu
SUMMARY:PlusOne Information Session
DESCRIPTION:LEARN ABOUT THE PLUSONE ACCELERATED MASTER’S DEGREE PROGRAM \nA master’s degree can provide you an additional level of expertise in an area aligned with your career goals. As a currently enrolled Bachelor of Science (BS) student in the College of Engineering at Northeastern\, you have the opportunity to earn a Master of Science degree (MS) in an accelerated time period with the PlusOne program. Once accepted into the program in an approved PlusOne pathway\, which is a BS and MS PlusOne combination\, you can earn an MS degree with\, in most cases\, just one extra year of study beyond your undergraduate degree program. \nIn this virtual information session\, College of Engineering undergraduate and graduate academic advisors will provide an overview of the PlusOne program to give you the knowledge and next steps to take advantage of the program if you choose. \nWHAT YOU WILL LEARN: \n\nWhat is PlusOne\nBenefits of the program\nEligibility\nCo-op considerations\nFinancial considerations\nSelecting your pathway\nAcademic advising resources\nTimeline to apply\nThe application process\nCourse registration\nTransitioning to graduate school\n\nZoom
URL:https://ece.northeastern.edu/event/plusone-information-session-4/
LOCATION:MA
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230315T140000
DTEND;TZID=America/New_York:20230315T153000
DTSTAMP:20260426T031440
CREATED:20230315T181507Z
LAST-MODIFIED:20230315T181507Z
UID:6210-1678888800-1678894200@ece.northeastern.edu
SUMMARY:Sadjad Asghari Esfeden's PhD Dissertation Defense
DESCRIPTION:“Spatiotemporal Localization of Object Handover for Human Robot Collaboration” \nCommittee Members: \nProf. Deniz Erdogmus (Advisor) \nProf. Taskin Padir \nProf. Eugene Tunik \nProf. Mathew Yarossi \nAbstract: \nHuman-robot interaction in a physical world like handover of objects requires perception systems to be efficient in localizing the object of interest. We propose an approach to estimate the location of the object with a low-cost RGB camera in a real-time inference for human-robot handover. While handover can take place in a short amount of time\, it is important for a robot to keep track of the object and fill in the gaps of missing detections in the perception module\, especially when the object is partially or completely occluded. A robot needs to proactively detect and track the object since the human decides where and when to transfer the object to the robot in a human to robot object handover.  In order to develop a perception system for robot to be capable of constantly localizing the object and predict its location and time of transfer\, we integrate an object detection algorithm with a tracking framework. The evaluation of this pipeline shows promising results for the goal of localization and tracking of the handover object and can help its location prediction in future.
URL:https://ece.northeastern.edu/event/sadjad-asghari-esfedens-phd-dissertation-defense/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230303T140000
DTEND;TZID=America/New_York:20230303T153000
DTSTAMP:20260426T031440
CREATED:20230227T195344Z
LAST-MODIFIED:20230227T195344Z
UID:6157-1677852000-1677857400@ece.northeastern.edu
SUMMARY:Kerem Enhos' PhD Proposal
DESCRIPTION:“Software-Defined Inter-medium Visible Light Communication and Underwater Acoustic Networks” \nCommittee Members:\nProf. Tommaso Melodia (Advisor)\nProf. Kaushik Chowdhury\nProf. Stefano Basagni\nDr. Emrecan Demirors \nAbstract:\n“Multi-Domain Operations” paradigm has been receiving significant attention both in military and civilian worlds. To realize this novel paradigm\, it is imperative to establish robust communication links to transfer data between devices operating in multiple domains. However\, as of today\, establishing high data rate\, robust\, secure\, and bi-directional communication links between aerial and underwater assets across the air-water interface is still an open problem. We address these challenges with software-defined visible light networking to establish bi-directional wireless links through the air-water interface. After generating a simulation model for inter-medium communication channel\, we also empirically derived an optimal parameter selection for carrierless amplitude and phase (CAP) modulation. Then\, we design and prototype a software-defined visible light  communication (VLC) modem and conducted extensive experimental evaluation. Apart from inter-medium communication\, software-defined networking can also be leveraged for underwater acoustic communication (UWAC)\, where we designed and assessed coexistence of multi-dimensional chirp spread spectrum (MCSS) with other UWAC schemes. We first evaluated the performance of the proposed communication scheme in a heterogeneous network setting  where it co-exists with a ZP-OFDM communication link\, then in a homogeneous network setting where all links are using MCSS scheme. Finally\, we used  this software-defined networking system to implement a single-input  multiple-output (SIMO) system for UWAC modems that are  deployed in a  distributed manner. Then\, we conduct a thorough experimental evaluation in  ocean environment for various subcarrier bandwidths and constellations  using three distributed receivers.
URL:https://ece.northeastern.edu/event/kerem-enhos-phd-proposal/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230303T100000
DTEND;TZID=America/New_York:20230303T110000
DTSTAMP:20260426T031440
CREATED:20230223T212329Z
LAST-MODIFIED:20230223T212329Z
UID:6152-1677837600-1677841200@ece.northeastern.edu
SUMMARY:Guanying Sun's PhD Dissertation Defense
DESCRIPTION:“Optimizing Reconstruction for Mm-Wave Body Scanner Imaging” \nCommittee: \nProf. Carey Rappaport (Advisor) \nProf. Edwin Marengo \nProf. Jose Martinez-Lorenzo \nAbstract: \nIn the past decades\, due to evolving threats\, passenger screening has become an important secure measure at airport and other secure locations. Numerous passenger screening techniques have been developed by researchers in both academia and industry to detect threats from explosives and weapons. Among these developments\, the multistatic mm-wave radar Advanced Imaging Technology (AIT) system was developed at Northeastern University. A problem with this system is the sidelobes from its physical limitations\, such as the finite aperture extent and the violation of the Nyquist sampling criterion by the sparse array. Therefore\, it is important to suppress the sidelobes so that to improve the quality of the reconstruction image. In this proposal\, we investigate two categories of methods\, one is based on post-processing\, and the other is based on system configuration optimization. In the former category four methods are developed\, while in the latter two methods are proposed. \nIn the first category\, the first method is the phase coherence method which is designed to weight the coherent sum based on the phase diversity of the reconstructed solutions for different transmitters. In this method\, two ways are considered to construct the Phase Coherence Factor (PCF). The first way is to use the information of wrapped phase\, and the second way is to use the information of unwrapped phase\, which is more intuitive than the first way. The second method is the coherence factor related method. Three coherence-factor based methods are analyzed and then incorporated into the imaging procedure of our nearfield millimeter-wave radar security scanning system. The third method is the SNR-dependent coherence factor method\, which takes SNR into consideration when forming the coherence factor. This method can generate better results than the pure coherence-factor based methods by choosing a proper set of parameters. The fourth method is the block-weighting algorithm where the neighbor weight amplifies bright areas and attenuates dark areas\, while the block keeps the influence local. The effectiveness of these methods has been verified with both simulation and measurement data. \nIn the second category\, the first method is optimizing receiver positions via PSF-based multi-objective optimization. Two metrics for measuring image quality of the PSF are proposed and defined as objective functions. The solution-selection metric is introduced to select the desired solution from the numerous Pareto-optimal solutions. Simulation shows that the optimized receiver design generates images with lower sidelobe level than the uniform receiver design. The second method is the dual-frequency radar design\, where a dual frequency\, wideband antenna array is designed by combining a high frequency subarray with a low frequency subarray. The image of the dual frequency array is obtained by multiplying the images of the two subarrays. We analyzed the amplitude of the PSF theoretically and proposed a criterion for the selection of dual frequency array design. The system imaging simulation shows that the grating lobes are significantly reduced for the dual frequency array with fewer radar modules/elements than the conventional array. This design will make the new generation system superior to the conventional scanning system.
URL:https://ece.northeastern.edu/event/guanying-suns-phd-dissertation-defense/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230302T090000
DTEND;TZID=America/New_York:20230302T100000
DTSTAMP:20260426T031440
CREATED:20230223T212222Z
LAST-MODIFIED:20230223T212222Z
UID:6150-1677747600-1677751200@ece.northeastern.edu
SUMMARY:Matthew Schinault's PhD Proposal Review
DESCRIPTION:“Development of A Large-Aperture 160-Element Coherent Hydrophone Array System for Instantaneous Wide Area Ocean Acoustic Sensing” \nAbstract: \nA large aperture coherent hydrophone towed array system comprising of 160 elements and an aperture length of 192 meters has been developed for real-time instantaneous wide-area ocean acoustic remote sensing and monitoring. The design and manufacture of these arrays requires a multidisciplinary approach to achieve acoustic performance capable for detection\, classification\, localization and tracking. Drawing from disciplines such as material science\, electrical engineering\, mechanical engineering\, hydrodynamics\, oceanography\, bioacoustics and signal processing. Due to the cost and complexity of towed array technology\, development of large aperture towed arrays has been limited at the university level. With military\, oil and gas exploration as the chief technology developers and users. The military and commercial focus is narrow and does not allow for scientific study\, resulting in significant gaps in the way we understand ocean acoustics around the globe. Here we model\, design\, fabricate and field test a broadband array for general ocean sensing that is configured to support a wide range of research to include study of marine mammals\, fish shoals\, geophysical processes\, surface or subsea man-made craft\, seismic surveying and the various challenges associated with detection\, classification and localization of underwater sound sources. \nHere\, we present the design process\, beginning with modeling and measurement of piezoelectric material properties. This allows us to perform finite element analysis\, estimating beampatterns and frequency response with a hydrophone electrical model. A pressure to voltage input model of the hydrophone is used to obtain the voltage levels produced to then configure amplification\, gain and filter stages providing a system level transfer function from analog to digital conversion. The array performance with a delay and sum beamformer is estimated for a broad range of frequencies\, with beamforming above half-lambda spacing. The components of the mechanical tow package are modeled to inform array construction estimating vibration and flow noise. A turbulent boundary layer model for flow noise estimation and environmental noise model determines the gains and cutoff frequencies necessary for performance. The comprehensive performance model is compared with a parameter estimation from test data to quantify array performance. \nTowed arrays are subject to environmental extremes\, with time at sea being costly. To increase the reliability\, the array is designed using field replaceable pressure tolerant components including hydrophones\, pre-amplifiers\, power modules\, telemetry and analog to digital conversion units. All components are verified by pressure chamber testing to ensure operation at depth. This large aperture array was able to be made without specialized facilities by utilizing modular interchangeable array interconnects allowing for conventional array populating and oil-filling methods with aperture lengths that are serviceable onboard research vessels. Array design\, fabrication and assembly was performed on-site at Northeastern University in Boston\, Massachusetts. Examples of passive acoustic data from array deployment during a sea trial in the U.S. Northeast coast are presented illustrating array capabilities. \nCommittee: \nProf. Purnima Ratilal Makris (Advisor)\nProf. Marvin Onabajo\nProf. Yongmin Liu\nDr. Alessandra Tesei
URL:https://ece.northeastern.edu/event/matthew-schinaults-phd-proposal-review/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230227T130000
DTEND;TZID=America/New_York:20230227T150000
DTSTAMP:20260426T031440
CREATED:20230131T200745Z
LAST-MODIFIED:20230131T200745Z
UID:6074-1677502800-1677510000@ece.northeastern.edu
SUMMARY:COE PhD Research Expo
DESCRIPTION:The College of Engineering is excited to announce the fifth annual COE PhD Research Expo\, and we invite all COE PhD students to submit a poster abstract. The expo is an excellent opportunity for your students to highlight their research and gain presentation experience before RISE. \nEvent:   COE PhD Research Expo\nDate:     Monday\, February 27\, 2023\nTime:    1:00pm – 3:00pm\nPlace:    McLeod Suites – Curry Student Center \nThe expo will take place following National Engineer’s Week. \nStudent Abstracts: \nPlease encourage your PhD students to submit poster abstracts by February 10\, 2023. The COE Communications Lab will offer interested students a poster preparation and presentation workshop early February. We will send details of the workshop to students soon. \nFaculty Judges: \nWe are looking for around ten faculty members to serve as judges. If you are available to judge between 1:30pm and 3:00pm on Monday\, February 27th please reach out to Taryn Urbanus (t.urbanus@northeastern.edu) by Friday\, February 17th. \n 
URL:https://ece.northeastern.edu/event/coe-phd-research-expo/
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:20230227T130000
DTEND;TZID=America/New_York:20230227T140000
DTSTAMP:20260426T031440
CREATED:20230223T212432Z
LAST-MODIFIED:20230223T212432Z
UID:6154-1677502800-1677506400@ece.northeastern.edu
SUMMARY:Yu Yin's PhD Proposal Review
DESCRIPTION:Committee: \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://ece.northeastern.edu/event/yu-yins-phd-proposal-review/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230222T173000
DTEND;TZID=America/New_York:20230222T183000
DTSTAMP:20260426T031440
CREATED:20230210T194123Z
LAST-MODIFIED:20230214T230832Z
UID:6103-1677087000-1677090600@ece.northeastern.edu
SUMMARY:Engineers Week: Fireside Chat – Break the Mold! Think Beyond Technology to Make an Impact in Unimaginable Ways
DESCRIPTION:Featuring Award-Winning Engineer and Commentator Dr. Shini Somara and Dean Gregory Abowd \nThink about engineering in a completely different way. Engineering is all around us and involves technology and beyond to solve the complex challenges of the world. Engineering is for everyone\, and everyone is for engineering! Dr. Somara has been featured on Crash Course\, BBC World\, Discovery Channel\, and more. Get ready for an engaging\, out-of-the-box session! \nWhen: Wed.\, February 22\, 5:30 p.m. – 6:30 p.m. (doors open at 5 p.m.) \nReception to follow with refreshments – opportunity to meet and network with Dr. Somara and Dean Abowd \nWhere: 17th Floor of East Village \nWho: For engineers and non-engineers (undergraduate\, graduate\, and high school students) \nRegister at: https://neweek.sites.northeastern.edu/
URL:https://ece.northeastern.edu/event/break-the-mold-think-beyond-technology-to-make-an-impact-in-unimaginable-ways/
LOCATION:East Village\, 17th floor\, 360 Huntington Ave\, East Village 17th floor\, Boston\, MA\, 02115\, United States
GEO:42.3394629;-71.0885286
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=East Village 17th floor 360 Huntington Ave East Village 17th floor Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave\, East Village 17th floor:geo:-71.0885286,42.3394629
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230221T173000
DTEND;TZID=America/New_York:20230221T193000
DTSTAMP:20260426T031440
CREATED:20230211T010858Z
LAST-MODIFIED:20230211T010858Z
UID:6120-1677000600-1677007800@ece.northeastern.edu
SUMMARY:Engineers Week: Cookies with the Dean
DESCRIPTION:Celebrating our COE students! Opportunity to meet and talk to Dean Gregory Abowd. Enjoy snacks (popcorn\, pretzels\, various desserts\, hot chocolate)\, free swag\, and photo booths! \nWhen: Tuesday\, February 21\, 5:30-7:30 p.m. \nWhere: Robinson Quad Bamboo & Industry Tents (near Mugar Life Sciences Building – 330 Huntington Ave) \nWho: COE students
URL:https://ece.northeastern.edu/event/engineers-week-cookies-with-the-dean/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230215T113000
DTEND;TZID=America/New_York:20230215T123000
DTSTAMP:20260426T031440
CREATED:20230210T210554Z
LAST-MODIFIED:20230210T210554Z
UID:6101-1676460600-1676464200@ece.northeastern.edu
SUMMARY:Yiyue Jiang's PhD Proposal Review
DESCRIPTION:“FPGA-based Accelerator of Neural Networks for Digital Predistortion” \nCommittee: \nProf. Miriam Leeser (Advisor) \nProf. John Dooley \nProf. Stefano Basagni \nAbstract: \nPower Amplifiers (PAs) are an essential part of wireless communications. \nAs wireless standards evolve and become more demanding\,  the requirements for PAs change as well.  Specifically\, PAs need to balance linearity and energy efficiency while adhering to 5G wireless standards and beyond. PA behaviors differ based on several criteria\, including the type of PA\, power levels\, and the environment. To overcome the nonlinear behavior of a PA\, a flexible system to achieve digital predistortion (DPD) is required that can rapidly adapt to its environment. \nIn many situations\, traditional methods such as the memory polynomial model cannot adapt to all these factors. Neural networks have been used for some years in RF and microwave engineering. Early work demonstrated the suitability of neural networks to model complicated active device characteristics. Current neural network based DPD systems all do the training offline and are therefore not real-time systems. To reduce the cost to upgrade hardware and to provide more flexibility to different power amplifiers’ linearization needs\, a specific neural network based reconfigurable\, adaptive\, and real-time digital predistortion system is proposed. This system targets Zynq All Programmable System on Chip (SoC) devices which feature an ARM processor and FPGA together with RF frontend on the same chip. The system proposed in this research combines real-time DPD with on-chip training. Furthermore\, most research on FPGA based inference accelerators targets classification problems with probability output. There is no accelerator working on the signal processing problem focusing on sample-by-sample output. Our proposed system is optimized in both algorithm and implementation targeting sample-by-sample processing with high accuracy and real-time efficiency. \n 
URL:https://ece.northeastern.edu/event/yiyue-jiangs-phd-proposal-review/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230203T150000
DTEND;TZID=America/New_York:20230203T170000
DTSTAMP:20260426T031440
CREATED:20230201T200236Z
LAST-MODIFIED:20230201T200236Z
UID:6078-1675436400-1675443600@ece.northeastern.edu
SUMMARY:Kubra Alemdar's PhD Proposal Review
DESCRIPTION:“Overcoming and Engineering Wireless Signals for Communication and Computation” \nAbstract: \nThe phenomenal growth of connected devices\, especially rapid expansion of IoT networks and the increasing demand for wireless services are the main driving forces for the evolution of wireless technologies. However\, the realization of such technologies requires a radical transformation of existing infrastructures to satisfy the needs of changing wireless environments. The main limitation in delivering these systems stems from a huge diversity in their demands and constraints. To address this limitation\, this dissertation shows how wireless signals and their interaction with and within wireless propagation domain can be used as communication or computational tools that enable us to achieve certain novel tasks. Specifically\, we build i) cross-functionality architectures to engineer the wireless channel to a) enable the operation of emerging technologies\, and b) demonstrate a new paradigm for computing with wireless signals\, and ii) intelligently shape the wireless channel to create reliable communication links. \nThis dissertation presents an experimentally validated software-hardware system to deliver three key contributions: We present a physical layer solution for distributed networks that provides over-the-air (OTA) clock synchronization\, called as RFCLOCK\, to overcome the hurdle of implementing fine-grained synchronization for emerging technologies. We first develop the theory for such precision synchronization and second implement it in a custom-design\, which is compatible with commercial-off-the-shelf (COTS) software-defined radios (SDRs). We compare the performance of RFClock with popular wired and GPS-based hardware solutions\, both in terms of clock performance\, as well as impact on distributed beamforming. \nNext\, we propose an RIS-based (reconfigurable reflecting surface) spatio-temporal approach to enhance the link reliability for IoTs where sensors are small-factor designs with single-antenna in rich multipath environment. We demonstrate the design of RIS and how it can effectively perturb the environment\, generating multiple wireless propagation channels and achieving performance of multi-antenna receiver in a Single-Input Single-Output (SISO) link. We compare the performance of the system with multi-antenna receiver in terms of channel hardening and outage probability. \nFinally\, we propose AirFC\, a system harnessing the capability of OTA computation to run inference on a neural network (NN) consisting of a set of fully connected layers (FC) by leveraging multi-antenna systems. We experimentally demonstrate and validate that such computation is accurate enough when compared to its digital counterpart. \nAs part of proposed research ahead\, we will address the challenges of realizing RIS-assisted communication in non-stationary conditions where the wireless channel can abruptly change due to the dynamic environment. We will first demonstrate the conditions in which conventional channel estimation methods cannot be utilized. We will then propose a learning method to create directional beams through reflections from RIS towards target locations without estimating the channel. \nLocation: 632 ISEC \nCommittee: \nProf. Kaushik Chowdhury (Advisor) \nProf. Marvin Onabajo \nProf. Josep Jornet
URL:https://ece.northeastern.edu/event/kubra-alemdars-phd-proposal-review/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230202T103000
DTEND;TZID=America/New_York:20230202T123000
DTSTAMP:20260426T031440
CREATED:20230117T234535Z
LAST-MODIFIED:20230117T234535Z
UID:6055-1675333800-1675341000@ece.northeastern.edu
SUMMARY:Qing Jin's PhD Proposal Review
DESCRIPTION:“Decoupling Efficiency-Performance Optimization for Modern Neural Networks” \nCommittee:\n\nProf. Yanzhi Wang (Advisor)\nProf. David R. Kaeli\nProf. Sunil Mittal\nProf. Jennifer Dy \n\nAbstract:\n\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://ece.northeastern.edu/event/qing-jins-phd-proposal-review/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230202T103000
DTEND;TZID=America/New_York:20230202T113000
DTSTAMP:20260426T031440
CREATED:20230126T204948Z
LAST-MODIFIED:20230126T205022Z
UID:6070-1675333800-1675337400@ece.northeastern.edu
SUMMARY:Amani Al-shawabka's PhD Proposal Review
DESCRIPTION:“Channel-and-Adversary-Resilient Radio Fingerprinting through Data-Driven Approaches at Scale” \nCommittee: \nProf. Tommaso Melodia (Advisor)\nProf. Kaushik Chowdhury\nProf. Francesco Restuccia \nAbstract: \nRadio fingerprinting authenticates wireless devices by leveraging tiny hardware-level imperfections inevitably present in the radio circuitry. This way\, devices can be directly identified at the physical layer– thus avoiding energy-expensive upper-layer cryptography that resource-limited embedded devices may not be able to afford. Recent advances have proven that employing deep learning algorithms can achieve fingerprinting accuracy levels that were impossible to achieve by traditional low-dimensional algorithms. Still\, the wireless research community lacks an exhaustive understanding of the challenges associated with developing robust\, reliable\, and channel-resilient radio fingerprinting through deep-learning approaches for practical applications. Key challenges are the non-stationarity of the wireless channel\, and the dynamic effects introduced by the operational environment\, which significantly limit fingerprinting applications by obscuring the hardware impairments associated with the transmitted waveform.\nIn this thesis\, we (i) develop a full-fledged\, systematic investigation to quantify the impact of the wireless channel by providing a first-of-its-kind evaluation on deep-learning-based fingerprinting algorithms\, examining the worst-case scenario (employing devices with identical radio circuitry) and at scale; (ii) develop large-scale open datasets for radio fingerprinting collected in diverse\, rich\, channel conditions and environments\, and using different technologies\, including WiFi and LoRa; (iii) identify conditions where hardware impairments are still detectable; and (iv) design\, implement\, and benchmark new data-driven algorithms to counter the degradation introduced by the wireless channel. Notably\, we propose a generalized\, real-time channel- and adversary-resilient data-driven approach to authenticate wireless devices at scale in practical scenarios. To the best of our knowledge\, our work for the first time improves the fingerprinting accuracy of the worst-case scenario with up to 4x and 6.3x for WiFi and LoRa technologies\, respectively.
URL:https://ece.northeastern.edu/event/amani-al-shawabkas-phd-proposal-review-2/
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:20230130T090000
DTEND;TZID=America/New_York:20230130T100000
DTSTAMP:20260426T031440
CREATED:20230125T213940Z
LAST-MODIFIED:20230125T213940Z
UID:6065-1675069200-1675072800@ece.northeastern.edu
SUMMARY:Sai Geetha Seri's PhD Proposal Review
DESCRIPTION:“Advancing Passive Ocean Acoustic Waveguide Remote Sensing for Detection of Fish Sounds\, Seismo-Acoustic Airgun Signals\, and Marine Mammal Vocalizations including Instrumentation Enhancements” \nCommittee: \nProf Purnima Ratilal Makris (Advisor)\nProf Josep M Jornet\nDr Nils Olav Handegard \nAbstract: \nUnderwater passive acoustic monitoring is important for understanding the marine environment\, since many ocean entities produce sound that can travel long ranges especially at low frequencies. For instance\, sound plays a vital role in the communication\, navigation\, and behavior of many marine biological organisms. Human activities in the ocean\, such as shipping\, offshore piling\, and energy prospecting\, generate a wide range and levels of sound. Natural environmental processes\, such as the passage of a hurricane and offshore seismicity are sources of underwater sound. In this thesis\, the instantaneous wide-area Passive Ocean Acoustic Waveguide Remote Sensing (POAWRS) technology implemented with a coherent hydrophone array is developed further and enhanced in a number of ways. First\, the automatic detection and analysis of man-made seismo-acoustic airgun signals employed in offshore geophysical and energy exploration surveys is investigated. Next\, the POAWRS technique is applied successfully for the first time toward the analysis and identification of sounds from some oceanic fish species in the wild using an eight-element prototype hydrophone array. Probability of Detection (PoD) regions are quantified separately for both the seismo-acoustic signals and fish sounds to provide an understanding of the horizontal spatial propagation extent of the acoustic signals from these sources. Finally\, we demonstrate significant enhancements in monitoring marine mammal sounds to include real-time capability and over a wider frequency range via a new in-house developed and fabricated 160-element coherent hydrophone array system. Here\, data from three distinct receiver array systems are analyzed\, presenting a technological evolution in the sensor systems utilized to implement and advance the POAWRS approach for ocean sensing. Development and integration of data acquisition approaches for both acoustic and non-acoustic sensors contained in the in-house developed array are discussed\, including design challenges and solutions.
URL:https://ece.northeastern.edu/event/sai-geetha-seris-phd-proposal-review/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230111T100000
DTEND;TZID=America/New_York:20230111T120000
DTSTAMP:20260426T031440
CREATED:20230104T212321Z
LAST-MODIFIED:20230104T212321Z
UID:6042-1673431200-1673438400@ece.northeastern.edu
SUMMARY:Yukui Luo's PhD Proposal Review
DESCRIPTION:“Securing FPGA as a Shared Cloud-Computing Resource: Threats and Mitigations” \nAbstract:\nWith the widespread adoption of cloud computing\, the demand for programmable hardware acceleration devices\, such as field-programmable gate array (FPGA)\, has increased. 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 instances at the FPGA resource and time levels\, significantly improving the utilization and energy efficiency of the FPGA devices. However\, due to the hardware reconfigurability of FPGA\, current virtualization methods for multi-tenant GPU and TPU instances are incompatible with multi-tenant FPGA virtualization.We define the threat model for multi-tenant FPGA and discuss the security issues related to Confidentiality\, Data Integrity\, and Availability. Based on an analysis of potential attacks\, we present our latest research results and propose two future research directions for mitigations: (1) a multi-tenant FPGA plug-to-play obfuscation module and (2) a hardware-software co-designed multi-tenant FPGA virtualization system\, which includes a hypervisor and a smart multi-tenant FPGA platform.\n\n\nCommittee:\n\nProf. Xiaolin Xu (Advisor) \nProf. Yunsi Fei\nProf. Xue Lin
URL:https://ece.northeastern.edu/event/yukui-luos-phd-proposal-review/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221215T090000
DTEND;TZID=America/New_York:20221215T110000
DTSTAMP:20260426T031440
CREATED:20221213T011124Z
LAST-MODIFIED:20221213T011124Z
UID:6023-1671094800-1671102000@ece.northeastern.edu
SUMMARY:Daniel Uvaydov's PhD Proposal Review
DESCRIPTION:“Real-Time Spectrum Sensing for Inference and Control”\n\nAbstract:\nSpectrum 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 has to be performed with extremely low latency over varying bandwidths and must guarantee strict real-time processing constraints; (ii) 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. We address these challenges in multiple wireless applications by utilizing Deep Learning techniques as the main vehicle of spectrum sensing for both inference and control. By leveraging mechanisms such as data augmentation\, channel attention\, voting\, and segmentation we are able to push beyond the capabilities of existing Deep Learning 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.\n\n\nCommittee:\n\nProf. Tommaso Melodia (Advisor) \nProf. Francesco Restuccia\nProf. Kaushik Chowdhury
URL:https://ece.northeastern.edu/event/daniel-uvaydovs-phd-proposal-review/
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:20221209T120000
DTEND;TZID=America/New_York:20221209T133000
DTSTAMP:20260426T031440
CREATED:20221201T022737Z
LAST-MODIFIED:20221201T022840Z
UID:6006-1670587200-1670592600@ece.northeastern.edu
SUMMARY:Alexey Tazin's PhD Dissertation Defense
DESCRIPTION:“Composition of UML Class Diagrams Using Category Theory and External Constraints” \nAbstract:\nIn large software development projects there is always a need for refactoring and optimization of the design. Usually software designs are represented using UML diagrams (e.g class diagrams). A software engineering team may create multiple versions of class diagrams satisfying some external constraints. In some cases\, subdiagrams of the developed diagrams can be selected and combined into one diagram. It is difficult to perform this task manually since manual process is very time consuming\, is prone to human errors\, and is not manageable for large projects. In this dissertation we present an algorithmic support for automating the generation of composed diagrams\, where the composed diagram satisfies a given collection of external constraints and is optimal with respect to a given objective function. The composition of diagrams is based on the colimit operation from category theory. The developed approach was verified experimentally by generating random external constraints (expressed in SPARQL and OWL)\, generating random class diagrams using these external constraints\, generating composed diagrams that satisfy these external constraints\, and computing class diagram metrics for each composed diagram. \nCommittee: \nProf. Mieczyslaw Kokar (Advisor) \nProf. David Kaeli \nDr. Jeff Smith
URL:https://ece.northeastern.edu/event/alexey-tazins-phd-dissertation-defense/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221209T110000
DTEND;TZID=America/New_York:20221209T130000
DTSTAMP:20260426T031440
CREATED:20221201T023204Z
LAST-MODIFIED:20221201T023204Z
UID:6010-1670583600-1670590800@ece.northeastern.edu
SUMMARY:Bin Sun's PhD Dissertation Defense
DESCRIPTION:“Factorization guided Lightweight Neural Networks for Visual Analysis” \nCommittee: \nProf. Yun Fu (Advisor) \nProf. Ming Shao \nProf. Lili Su \nAbstract: \nDeep 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.\nBesides 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. \nWe also tried to factorize the features for single image super resolution (SISR). Factorization on features will reduce the feature size in order to reduce the computation costs. However\, the reduction of the spatial size is counter-intuitive for the super resolution task. With our exploration\, we demonstrated a network named Hybrid Pixel-Unshuffled Network (HPUN)\, which factorized the features to achieve the lightweight purpose while keeping high performance. Specifically\, we utilized pixel-unshuffle operation to factorize the input features. After the factorization\, we improved the performance by the grouped convolution\, max-pooling\, and self-residual. The experiments on popular benchmarks showed that the factorization strategy could achieve SOTA performance on SISR.
URL:https://ece.northeastern.edu/event/bin-suns-phd-dissertation-defense/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221208T140000
DTEND;TZID=America/New_York:20221208T160000
DTSTAMP:20260426T031440
CREATED:20221202T201226Z
LAST-MODIFIED:20221202T201226Z
UID:6014-1670508000-1670515200@ece.northeastern.edu
SUMMARY:Chuangtang Wang's PhD Proposal Review
DESCRIPTION:“All-optical Control of Magnetization in Nanostructures” \nCommittee: \nProf. Yongmin Liu (Advisor) \nProf. Don Heiman \nProf. Nian X. Sun \nAbstract:\nThe switching of magnetization by a femtosecond laser within several picoseconds has recently gained substantial attention\, because it promises next-generation\, energy-efficient\, and high-rate data storage technology. One of the most intriguing demonstrations is the helicity-dependent switching (HD-AOS) of a ferromagnet\, in which the magnetization states can be deterministically written and erased using left- and right-circularly polarized light. However\, the challenge is to realize a single-pulse HD-AOS. Controlling the spin angular momentum transfer from light to magnetic materials in nanostructures is the key to advance this field.\nIn my thesis research work\, I will study the all-optical control of magnetization in different nanostructures\, aiming to better understand the underlying mechanisms of HD-AOD and accelerate the technology development. Firstly\, helicity-driven magnetization dynamics in heavy metal/ferromagnet Au(Pt)/Co bilayer by the optical spin transfer torque (OSTT) is experimentally explored. The wavelength-dependent measurement of OSTT reveals that the quantum efficiency of OSTT strongly depends on the interface electronic structure and pump energy. The Inverse Faraday effect (IFE)\, which is believed to be the driving mechanism of HD-AOS\, is subsequently investigated in an Au thin film. The dependence of IFE on photon energy implies that the orbital angular momentum contribution to IFE is dominated by the excitation of laser pulses. To the best of our knowledge\, it is the first demonstration of this phenomenon. Lastly\, I will discuss our recent results on plasmonics-enhanced all-optical control of magnetization. Light can be tightly confined in plasmonic structures\, which can potentially enable low-energy and high-density magnetic data storage.
URL:https://ece.northeastern.edu/event/chuangtang-wangs-phd-proposal-review/
LOCATION:138 ISEC\, 360 Huntington Ave\, 138 ISEC\, Boston\, MA\, 02115\, United States
GEO:42.3401758;-71.0892797
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=138 ISEC 360 Huntington Ave 138 ISEC Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave\, 138 ISEC:geo:-71.0892797,42.3401758
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221208T110000
DTEND;TZID=America/New_York:20221208T120000
DTSTAMP:20260426T031440
CREATED:20221201T023045Z
LAST-MODIFIED:20221201T023045Z
UID:6008-1670497200-1670500800@ece.northeastern.edu
SUMMARY:Danlin Jia's PhD Dissertation Defense
DESCRIPTION:“Towards Performance and Cost-efficiency for Data-intensive Applications in Distributed Data Processing Systems” \nAbstract: \nData-intensive science (DIS) has experienced a significant boom in the past decade. The emerging technologies of data-intensive services and infrastructures contribute to DIS’s development and raise challenges. An ecosystem has been constructed considering performance\, scalability\, sustainability\, and reliability to provide a high-quality service to DIS applications. The ecosystem consists of services exposed to users for application deployment and infrastructures to support data storage\, transfer\, and management from the system’s perspective. DIS applications share typical features\, such as memory and I/O intensity. Thus\, addressing the bottlenecks triggered by memory-intensive or I/O-intensive workloads in services and infrastructures is essential to improve the performance and cost-efficiency of the whole ecosystem. In this dissertation\, we investigate the characteristics of various DIS applications and design new resource allocation and scheduling schemes for the services and infrastructures in the DIS ecosystem. \nWe first investigate memory optimization in DIS ecosystems. In-memory data analytic frameworks are proposed to cache critical intermediate data in memory instead of in storage drives. Apache Spark is a commonly adopted in-memory data analytic framework with two memory managers\, Static and Unified. However\, the static memory manager lacks flexibility. In contrast\, the unified memory manager puts heavy pressure on the garbage collection of the Java Virtual Machine on which Spark resides. To address these issues\, we propose a new learning-based bidirectional usage-bounded memory allocation scheme to support dynamic memory allocation considering both memory demands and latency introduced by garbage collection. Distributed data-processing workloads in container-based virtualization take advantage of resource sharing\, fast delivery\, and excellent portability of containerization but also suffer from resource competition and performance interference. This inevitably induces performance degradation and significantly long latency\, even worse when over-provisioning. Motivated by this problem\, we design an efficient memory allocation scheme (RITA) for containerized parallel systems to improve data processing latency. RITA monitors applications’ memory usage and cache characteristics and dynamically re-allocates memory resources. \nWe also propose I/O optimizations for DIS applications and infrastructures. Distributed Deep Learning (DDL) accelerates DNN training by distributing training workloads across multiple computation accelerators\, e.g.\, GPUs. Although a surge of research has been devoted to optimizing DDL training\, the impact of data loading on GPU usage and training performance has been relatively under-explored. When multiple DDL applications are deployed\, the lack of a practical and efficient technique for data-loader allocation incurs GPU idleness and degrades the training throughput. In this dissertation\, we thus investigate the impact of data-loading on the global training throughput and design a resource allocator that uses the data-loading rate as a knob to reduce the GPU idleness. Finally\, designs and optimizations on disaggregated storage systems supported by cutting-edge storage and network techniques emerge dramatically. Disaggregated storage systems can scale resources independently and provide high-quality services for hyper-scale architectures. The traditional congestion control mechanism relieves congestion by limiting the data-sending rate of senders. However\, such a design scarifies the storage drive’s performance as data are generated but stalled on storage host nodes if network congestion happens. To solve this issue\, we design a storage-side rate control mechanism to mitigate network congestion while avoiding sacrificing I/O performance. \nCommittee: \nProf. Ningfang Mi (Advisor) \nProf. Xue Lin \nProf. David Kaeli
URL:https://ece.northeastern.edu/event/danlin-jias-phd-dissertation-defense/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221206T160000
DTEND;TZID=America/New_York:20221206T173000
DTSTAMP:20260426T031440
CREATED:20221206T020005Z
LAST-MODIFIED:20221206T020005Z
UID:6017-1670342400-1670347800@ece.northeastern.edu
SUMMARY:Md Navid Akbar's PhD Dissertation Defense
DESCRIPTION:“Inference from Brain Imaging: Incorporating Domain Knowledge and Latent Space Modeling” \nAbstract:\n\nBrain imaging can probe the anatomy (structural) of our brain\, or its function (functional). A particular imaging modality (unimodal) generally provides only a particular insight into human health. Transcranial magnetic stimulation (TMS)\, though still in its infancy as a brain imaging modality\, is such a functional\, unimodal technique. TMS helps model human motor-cortical mapping\, using corresponding muscle activity captured by surface electromyography (EMG)\, but it necessitates a reliable data-driven model. Earlier works have modeled the causal direction only (from cortical representation to muscles)\, or the inverse direction (from muscles to cortical representation)\, with simple statistical regression. We modeled this motor-cortical mapping bi-directionally in this dissertation\, using deep learning. We first modeled TMS-induced 3D electric field (E-field) in a brain to causal multi-muscle activation picked up by EMG\, in a regression task using a convolutional neural network (CNN) autoencoder. By fusing neuroscience domain knowledge (e.g.\, an empirical neural response profile)\, we reduced 14% squared error\, compared to the baseline model that did not contain this. We then designed our novel inverse imaging CNN model\, to reconstruct physiologically meaningful E-field distributions (in the image domain) from a given set of muscle activations (in the sensor domain). By adopting variational inference in the CNN model\, to learn the underlying latent space better\, we were able to reduce 13% in squared error over our purely CNN baseline. \nDiagnosis with brain imaging is often incomplete with a unimodal technique\, and having multiple sources (multimodal) may be advantageous. Successful multimodal fusion can provide more holistic information\, compared to its constituents. One relevant example is the classification of late post-traumatic seizure (LPTS). Previous works in this space have tackled LPTS classification with either unimodal functional imaging\, or non-machine learning (ML) structural modeling. In this dissertation\, we first undertook the ML classification of binary LPTS: with unimodal\, structural brain imaging\, namely diffusion magnetic resonance imaging (dMRI). By incorporating interpretable domain knowledge (post-traumatic lesion volume compensation)\, we improved 7% in the mean area under the curve (AUC) over the standard technique in literature. Finally\, we classified LPTS for a larger sample of subjects\, utilizing multimodal imaging\, including functional MRI (fMRI) and electroencephalography (EEG). Following unsupervised imputation for any missing modality within the subjects\, we introduced our novel multimodal fusion algorithm\, which attempts to leverage the underlying structure of the multivariate information. We found that our proposed algorithm improved by 7% in AUC performance\, over a naive Bayesian estimator that can handle missing data intrinsically.\nCollectively\, the work presented here demonstrated that incorporating domain knowledge in the modeling pipeline successfully improved inference. Similar improvements were also observed by learning and leveraging the possible underlying latent structure of the given information\, and adapting the models accordingly. \n\n\n\nCommittee:\n\nProf. Deniz Erdogmus (Advisor) \nProf. Mathew Yarossi (Co-advisor)\nProf. Dominique Duncan\nProf. Sarah Ostadabbas
URL:https://ece.northeastern.edu/event/md-navid-akbars-phd-dissertation-defense/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221205T100000
DTEND;TZID=America/New_York:20221205T110000
DTSTAMP:20260426T031440
CREATED:20221201T022546Z
LAST-MODIFIED:20221201T022546Z
UID:6004-1670234400-1670238000@ece.northeastern.edu
SUMMARY:Ramtin Khalili's PhD Dissertation Defense
DESCRIPTION:Abstract: \nState estimation is a critical application in energy management systems. Due to the increased penetration of inverter-based resources\, installed advanced infrastructure at all voltage levels\, and unconventional loads like electric vehicle charging stations\, a three-phase state estimator formulation is essential. The first issue is the convoluted formulation and modeling techniques that are required in three-phase systems studies. Moreover\, the size of network matrices expanded\, which makes the analysis computationally costly. This dissertation addresses this by proposing a new decoupled state estimation method. The idea is to exploit the linearity of measurement equations\, decompose the three-phase coupled equations into three independent modal measurement equations\, perform the state estimation independently for each mode\, and finally reconstruct the three-phase quantities. This method is applicable to both radial and meshed three-phase networks. Furthermore\, multi-phase structures can be handled by the new estimator\, which makes the approach practical when monitoring mixed-phase feeder sections is of interest. \nWhile utilities are investing in expanding the grid and installing more PMUs\, there might not be enough PMUs to make the network observable in all networks\, especially at lower voltage levels. So\, PMU-based linear state estimators are not always feasible. On the other hand\, SCADA measurements are available with adequate redundancy in most networks. However\, SCADA-based state estimation is nonlinear\, which brings various problems like divergence issues and significant CPU times. The computational complexity will be even worse if the three-phase state estimation is formulated based on SCADA measurements due to their nonlinear nature\, which makes modal decoupling impossible. So\, a new linear formulation has been proposed for both the positive-sequence and three-phase networks based on conventional measurements. This approach converts the nonlinear recursive problem into an iterative linear state estimation problem. \nThe inherent assumption in most of the state estimators is a perfect network model. However\, network parameter errors are susceptible to errors that can bias the state estimation solution. This can deceive the existing bad data tools as parameter errors appear as if multiple interacting measurement errors occur locally. So\, a two-stage method is proposed for parameter error identification and correction for large three-phase networks. A systematic PMU placement strategy is also proposed to ensure the detectability of parameter errors. The benefits of multi-area state estimation are demonstrated for the deregulated power grids for monitoring the local and boundary areas. It has also shown promising results in increasing the efficiency of state estimation using a distributed framework. Parameter and measurement errors can remain undetected as a result of weakened measurement redundancy on the boundaries. However\, boundary errors in the area boundaries will be detected due to measurement consolidation at the coordination level. \nCommittee:\nProf. Ali Abur (Advisor)\nProf. Bahram Shafai\nProf. Mahshid Amirabadi
URL:https://ece.northeastern.edu/event/ramtin-khalilis-phd-dissertation-defense/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221202T080000
DTEND;TZID=America/New_York:20221202T170000
DTSTAMP:20260426T031440
CREATED:20220824T182336Z
LAST-MODIFIED:20220824T182336Z
UID:5782-1669968000-1670000400@ece.northeastern.edu
SUMMARY:First Year Engineering Expo
DESCRIPTION:Please come to the Curry Student Center indoor quad and pit on Friday\, December 2nd to see Northeastern’s First-Year Engineering Students’ inventive projects\, games\, and exhibits. \nStudents will showcase original board games\, interactive projects geared to teach children sustainability concepts\, and prolific prototypes to help solve a wide range of problems. \nEach project applies the engineering concepts introduced this past semester\, which includes the Engineering Design Process\, Solidworks\, AutoCAD\, Programming with C++ and Matlab\, and controlling microelectronics with Arduino.
URL:https://ece.northeastern.edu/event/first-year-engineering-expo-3/
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:20221129T140000
DTEND;TZID=America/New_York:20221129T153000
DTSTAMP:20260426T031440
CREATED:20221122T012209Z
LAST-MODIFIED:20221122T012209Z
UID:5975-1669730400-1669735800@ece.northeastern.edu
SUMMARY:Prof. Hui Guan -  "Towards accurate and efficient edge computing via multi-task learning "
DESCRIPTION:“Towards accurate and efficient edge computing via multi-task learning ” \n\nAbstract: \n\n\nAI-powered applications increasingly adopt Deep Neural Networks (DNNs) for solving many prediction tasks\, leading to more than one DNNs running on resource-constrained devices. Supporting many models simultaneously on a device is challenging due to the linearly increased computation\, energy\, and storage costs. An effective approach to address the problem is multi-task learning (MTL) where a set of tasks are learned jointly to allow some parameter sharing among tasks. MTL creates multi-task models based on common DNN architectures and has shown significantly reduced inference costs and improved generalization performance in many machine learning applications. In this talk\, we will introduce our recent efforts on leveraging MTL to improve accuracy and efficiency for edge computing. The talk will introduce multi-task architecture design systems that can automatically identify resource-efficient multi-task models with low inference costs and high task accuracy. \n\n\nBio:\n \n\n\n\nHui Guan is an Assistant Professor in the College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst\, the flagship campus of the UMass system. She received her Ph.D. in Electrical Engineering from North Carolina State University in 2020. Her research lies in the intersection between machine learning and systems\, with an emphasis on improving the speed\, scalability\, and reliability of machine learning through innovations in algorithms and programming systems. Her current research focuses on both algorithm and system optimizations of deep multi-task learning and graph machine learning.
URL:https://ece.northeastern.edu/event/prof-hui-guan-towards-accurate-and-efficient-edge-computing-via-multi-task-learning/
LOCATION:442 Dana\, 360 Huntington Ave\, 442 DA\, Boston\, MA\, 02115\, United States
GEO:42.3387508;-71.0923044
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221129T100000
DTEND;TZID=America/New_York:20221129T130000
DTSTAMP:20260426T031440
CREATED:20221104T010151Z
LAST-MODIFIED:20221104T010151Z
UID:5952-1669716000-1669726800@ece.northeastern.edu
SUMMARY:Research Presentations On Bendable Electronics and Sustainable Technologies (BEST)
DESCRIPTION:Professor Ravinder Dahiya will be joining Northeastern’s ECE Department on January 2023. Please join us for an interactive mini-symposium featuring projects from the BEST Lab directed by Professor Dahiya. \n  \nThe presenters are: \nDr. Dhayalan Shakthivel\, Research Associate\, Inorganic Nanowires for Flexible and Large Area Electronics \nDr. Gaurav Khandelwal\, Post-doc\, Functional Materials based Triboelectric Nanogenerators for Selfpowered Sensors and Systems \nDr. Fengyuan Liu\, Post-doc\, “Hebbian-like” learning in electronic skin \nDr. Abhishek S. Dahiya\, Research Associate\, Towards energy autonomous electronic skin using sustainable technologies \nAyoub Zumeit\, PhD candidate\, Inorganic nanostructures-based high-performance flexible electronics \nAdamos Christou\, PhD candidate\, Novel Technologies for High-Performance Printed Electronics \nRadu Chirila\, PhD candidate\, Electronic Skin and Holographic Systems for Socially Intelligent Robots \nJoão Neto\, PhD candidate\, Hardware building for neuromorphic electronic skin \nLuca De Pamphilis\, PhD candidate\, Nanowire-based electronic layers for flexible neuromorphic devices \nMake sure to RSVP & specify inperson or virtual attendance. See you soon!
URL:https://ece.northeastern.edu/event/research-presentations-on-bendable-electronics-and-sustainable-technologies-best/
LOCATION:442 Dana\, 360 Huntington Ave\, 442 DA\, Boston\, MA\, 02115\, United States
GEO:42.3387508;-71.0923044
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221128T120000
DTEND;TZID=America/New_York:20221128T140000
DTSTAMP:20260426T031440
CREATED:20221121T212045Z
LAST-MODIFIED:20221121T212045Z
UID:5973-1669636800-1669644000@ece.northeastern.edu
SUMMARY:Xuanyi Zhao's PhD Proposal Review
DESCRIPTION:“AlN/AlScN based Micro Acoustic Metamaterials for Radio Frequency Applications of the Next Generations” \nAbstract: \nIn the last two decades‚ micro-acoustic resonators (μARs) have played a key role in integrated 1G-to-4G radios‚ providing the technological means to achieve compact radio frequency (RF) filters with low loss and moderate fractional bandwidths (BW<4%). More specifically‚ Aluminum Nitride (AlN) based filters have populated the front-end of most commercial mobile transceivers due to the good dielectric‚ piezoelectric and thermal properties exhibited by AlN thin-films and because their fabrication process is compatible with the one used for any Complementary Metal Oxide Semiconductor (CMOS) integrated circuits (ICs). Nevertheless‚ the rapid growth of 5G and the abrupt technological leap expected with the development of sixth-generation (6G) communication systems are expected to severely complicate the design of future radio front-ends by demanding Super-High-Frequency (SHF) filtering components with much larger fractional bandwidths than achievable today. In the meantime\, as more acoustic filters replying on bulk waves which requests the devices to be physically-suspended to operate\, thermal related nonlinearity has been a challenge which requests new designs to enhance the thermal linearity thus power handling for these acoustic components. Even more‚ the recent invention of on-chip nonreciprocal components‚ like the circulators and isolators recently built in slightly different CMOS technologies‚ has provided concrete means to double the spectral efficiency of current radios by enabling the adoption of full-duplex communication schemes. Nevertheless‚ for such schemes to be really usable in wireless systems‚ self-interference cancellation networks including wideband‚ low-loss and large group delay lines are needed. Yet‚ the current on-chip delay lines that are also manufacturable through CMOS processes‚ which rely on the piezoelectric excitation of Surface Acoustic Waves (SAWs) or Lamb Waves in piezoelectric thin films‚ have their bandwidth and insertion-loss severely limited by the relatively low electromechanical coupling coefficient exhibited by their input and output transducers. As a results‚ these components are hardly usable to form any desired self-interference cancelation networks. In order to overcome these challenges‚ only recently‚ new classes of microacoustic resonators and delay lines exploiting the high piezoelectric coefficient of Aluminum Scandium Nitride (AlScN) thin films and the exotic dispersive features of acoustic metamaterials (AMs) have been emerging. These devices rely on forests of locally resonant piezoelectric rods to generate unique modal distributions‚ as well as unconventional wave propagation features that cannot be found in conventional SAW and Lamb wave counterparts. In this presentation‚ the design‚ fabrication and performance of the first microacoustic metamaterials (μAMs) based resonators and delay lines will be showcased. Moreover\, AMs based reflectors are invented and demonstrated providing new improving the linearity and power handling of the AlScN μARs. In addition to reviewing the current status of our work\, we will propose several further explorations of using our AlN/AlScN based AMs in RF applications of the next generations. \nCommittee: \nProf. Cristian Cassella (advisor) \nProf. Matteo Rinaldi \nDr. Jeronimo Segovia-Fernandez
URL:https://ece.northeastern.edu/event/xuanyi-zhaos-phd-proposal-review/
LOCATION:MA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221122T110000
DTEND;TZID=America/New_York:20221122T120000
DTSTAMP:20260426T031440
CREATED:20221103T213322Z
LAST-MODIFIED:20221103T213322Z
UID:5942-1669114800-1669118400@ece.northeastern.edu
SUMMARY:Mahshid Asri's Proposal Review
DESCRIPTION:“Development of Anomaly Detection and Characterization Algorithms Using Wideband Radar Image Processing for Security Applications” \nAbstract:\nDetection and characterization of suspicious body-worn objects is necessary for safe and effective personnel screening. In airports\, developing a precise system that can distinguish threats and explosives from objects like money belt can reduce the pat-down significantly while maintaining effective security.\nThis work proposes two main algorithms which are developed for different millimeter-wave radar systems. The first project is a material characterization algorithm designed for a 30 GHz wideband multi bi-static radar system used for passenger screening in airports. The proposed algorithm can automatically distinguish lossless materials from lossy ones and calculate their thickness and permittivities. Starting from the radar reconstructed image showing a cross-section of the body\, we extract the nominal body contour using Fourier series\, separate body and object responses\, categorize the object as lossy or lossless based on the depression and protrusion of the body contour\, and finally predict possible values for the object’s permittivity and thickness. Our resulting classification is good\, implying fewer nuisance alarms at check points. The second project is a metal detection algorithm designed to monitor pedestrians walking along a sidewalk for large\, concealed metallic objects. Finite Difference Frequency Domain and SAR algorithms are used to simulate the images produced by this 6 GHz wideband radar system. \nCommittee: \nProf. Carey Rappaport (Advisor) \nProf. Charles DiMarzio \nProf. Edwin Marengo
URL:https://ece.northeastern.edu/event/mahshid-asris-proposal-review/
LOCATION:MA
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END:VCALENDAR