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X-WR-CALDESC:Events for Northeastern University College of Engineering
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20210510
DTEND;VALUE=DATE:20210515
DTSTAMP:20260405T065107
CREATED:20210421T202724Z
LAST-MODIFIED:20210421T203805Z
UID:25558-1620604800-1621036799@coe.northeastern.edu
SUMMARY:Huskies Wellness Week
DESCRIPTION:Welcome to Huskies Wellness Week\, a personal retreat that will leave you feeling refreshed and empowered from the comfort of your own home. Build some ‘you time’ into your days through a lineup of exclusive programming\, hosted by the Northeastern Boston Community. Share your progress with our Instagram gameboard—designed for us to stay on track together—for the chance to win a prize. \nHow do I play? \nOn Sunday\, May 9 we’re posting a gameboard on our Instagram account for the chance to win a $100 gift card to the bookstore. Complete 7 squares for one entry into our challenge\, and 10 squares for two entries. Send us a screenshot of the gameboard with your completed tiles checked off or with pictures of your activity overlayed on top by the following Sunday\, May 16 at 12pm EST to be entered into the raffle. \nThe winner will be selected on Monday\, May 17 by 12pm EST and contacted via email by Ilana Gensler\, MA’19\, Assistant Director\, Affinity and Domestic Engagement. \nYour completed board can be sent directly to @northeastern_alumni through direct message on Instagram. Be sure to share your progress throughout the week on Instagram by tagging @northeastern_alumni. \nWhat if I have a private account?\nUpon registering you will be asked to provide your Instagram handle. You will receive a follow-request ahead of Huskies Wellness Week. \nSessions\nViewing in Eastern Time \n\n\n\nActivate your Full Potential\n5/10/21\n8:30 AM-9:30 AM ET \nLeena Prabhoo\, MEd’90\nManaging Partner\nPath to Prajñā\nSolutions LLP \nEnhance your personal and professional wellness by learning how to activate your full potential. This session will explore what it means to be living your full potential and give you the tools to act so you can make it a reality. Learn about some of the benefits you can gain from this process and develop your personal game-plan for moving forward on this journey.\n\n\n\nMeditation to Cultivate Peace of Mind\n5/12/21\n3:00 PM-4:00 PM ET \nStacy Hernandez\, AS’98\, MS’01\nOwner/College\nCounselor\nThe Best U \nSettle in for a 40-minute meditation session to cultivate peace of mind in service of your mental health. When you dedicate time exploring within\, you learn to listen to your inner voice rather than the influences outside of you. This internal reconnection can help you activate the power you have inside of yourself to stay grounded and bring enhanced mindfulness to every element of your life as you move through each day.\n\n\n\nMOVE by The Handle Bar\n5/14/21\n8:00 AM-8:45 AM ETAnthony Charter\nIndoor Cycling Instructor\nThe Handle Bar Indoor Cycling Studio \nMOVE is a 45-minute\, total-body workout that combines high-intensity plyometric movement with slow-burning kettlebell strength work. It fuses The Handle Bar’s passion for music-driven exercise with thoughtful programming that complements and enhances the studio’s work on the bike. Class will be accessible for 48 hours after it goes Live at the time of the event.\n\n\n\nVinyasa Flow Yoga\n5/14/21\n12:00 PM-12:30 PM ET \nH Alex Harrison\, JD’11\nYoga Instructor\nBeacon Hill Yoga \nMake space both physically and mentally as we explore and connect with our bodies through the synthesis of yoga\, movement\, breath\, and mindfulness exercises. Expect to flow through traditional yoga postures as we explore the intricacies of skeletal alignment and the muscular engagement required to link pose to pose\, and end with a restful savasana.\n\n\n\n\nRegister Now
URL:https://coe.northeastern.edu/event/huskies-wellness-week/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20210513
DTEND;VALUE=DATE:20210515
DTSTAMP:20260405T065107
CREATED:20210513T192140Z
LAST-MODIFIED:20210513T192140Z
UID:25946-1620864000-1621036799@coe.northeastern.edu
SUMMARY:AMGEN Lecture Series: Biotechnology Edge
DESCRIPTION:This two-day lecture series is presented by AMGEN scientists\, in partnership with the School of Pharmacy and Bouvé College of Health Sciences Dean’s office. It will provide participants with an overview of the drug development process\, as well as the biotechnology industry and potential career paths. Lectures will be presented live online.  Recording is not allowed. See the full program below. \nThe lectures will be in real time on WebEx.  A link will be sent to everyone who has registered. Attendees of the two-day lecture series will be eligible to earn a Badge. \nRSVP \n\n\n\n\n\n\n\nThursday May 13th 2021\, 9.00 am – 4.30 pm\n\n\n9.00\nIntroduction\n\n\n9.10\nLife of a Drug\, Roger Hart\, PhD\n\n\n10.00\nDrug Discovery at Amgen: A Multi‐Modality Approach\, Roger Hart\, PhD\n\n\n10.50\nBreak and Informational Session\n\n\n11.10\nProcess Development from Clinic to Approval\, Jennifer Litowski\,Process Development Principal Scientist\n\n\n12.00\nLunch and Learn\n\n\n1.00\nTarget Identification & Validation\, John Ferbas\, PhD\,\n\n\n1.50\nIntroduction to Pharmaceutical Solid-State Chemistry and MaterialsScience\, Hyunsoo Park\, Process Development Principal  Scientist\n\n\n2.40\nBreak\n\n\n2.50\nFrom Bench to Bedside: Discovery of the First FDA‐ ApprovedAntibody Therapeutic for Migraine\, Cen Xu\, PhD\n\n\n3.40\nThe Role of Continuous Manufacturing to Advance Amgen’s SyntheticPortfolio\, Matt Beaver\, Principal Scientist\n\n\n\n\n\n\nFriday\, May 14th 2021\, 9.00 am – 4.30 pm\n\n\n9.00\nIntroduction\n\n\n9.10\nDrug Safety: An Industry Perspective Oluwadamilola Ogunyankin\,MD\,MPH\n\n\n10.00\nModeling of Processes\, Products and Devices for Drug Development &Manufacturing\, Pablo Rolandi\, Director Data Sciences\n\n\n10.50\nBreak and Informational Session\n\n\n11.10\nRaw Material Selection and Control for ManufacturingPharmaceuticals\, Susan Burke\, PhD\n\n\n12.00\nLunch and Learn\n\n\n1.00\nOncology\, Kristin Tarbell\, Principal Scientist\n\n\n1.50\nInnovations in Device Technologies for Delivering Biologics to Patients\,Shirish Ingawale\, PhD\n\n\n2.40\nBreak\n\n\n2.50\nDigital Transformation in Biopharmaceutical Operations\, Myra Coufal\,PhD\n\n\n3.40\nCareers in Biotech\, Jessica  Smith\,  Process  Development  AssociateScientist
URL:https://coe.northeastern.edu/event/amgen-lecture-series-biotechnology-edge/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210513T140000
DTEND;TZID=America/New_York:20210513T150000
DTSTAMP:20260405T065107
CREATED:20210503T135624Z
LAST-MODIFIED:20210510T135607Z
UID:25648-1620914400-1620918000@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Siyue Wang
DESCRIPTION:PhD Proposal Review: Towards Robust and Secure Deep Learning Models and Beyond \nSiyue Wang \nLocation: Zoom Link \nAbstract: Modern science and technology witness the breakthroughs made by deep learning during the past decades. Fueled by rapid improvements of computational resources\, learning algorithms\, and massive amount of data\, deep neural networks (DNNs) have played a dominant role in more and more real-world applications. Nonetheless\, there is a spring of bitterness mingling with this remarkable success – recent studies reveals that there are two main security threats of DNNs which limit its widespread usage: 1) the robustness of DNN models under adversarial attacks\, and 2) the protection and verification of intellectual properties of well-trained DNN models. \nIn this dissertation\, we fist focus on the security problems of how to build robust DNNs under adversarial attacks\, where deliberately crafted small perturbations added to the clean input can lead to wrong prediction results with high confidence. We approach the solution by incorporating stochasticity into DNN models. We propose multiple schemes to harden the DNN models when facing adversarial threats\, including Defensive Dropout (DD)\, Hierarchical Random Switching (HRS)\, and Adversarially Trained Model Switching (AdvMS). \nThe second part of this dissertation focuses on how to effectively protect the intellectual property for DNNs and reliably identify their ownership. We propose Characteristic Examples (C-examples) for effectively fingerprinting DNN models\, featuring high-robustness to the well-trained DNN and its derived versions (e.g. pruned models) as well as low-transferability to unassociated models. The generation process of our fingerprints does not intervene with the training phase and no additional data are required from the training/testing set.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-siyue-wang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210521T110000
DTEND;TZID=America/New_York:20210521T120000
DTSTAMP:20260405T065107
CREATED:20210503T135740Z
LAST-MODIFIED:20210503T135740Z
UID:25650-1621594800-1621598400@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Daniel Uvaydov
DESCRIPTION:MS Thesis Defense titled DeepSense: Fast Wideband Spectrum Sensing Through Real-Time In-the-Loop Deep Learning \nDaniel Uvaydov \nLocation: Microsoft Teams \nAbstract: Spectrum sharing will be a key technology to tackle spectrum scarcity in the sub-6 GHz bands. To fairly access the shared bandwidth\, wireless users will necessarily need to quickly sense large portions of spectrum and opportunistically access unutilized bands. The key unaddressed challenges of spectrum sensing are that (i) it has to be performed with extremely low latency over large bandwidths to detect tiny spectrum holes and to guarantee strict real-time digital signal processing (DSP) 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. To the best of our knowledge\, the literature lacks spectrum sensing techniques able to accomplish both requirements. In this paper\, we propose DeepSense\, a software/hardware framework for real-time wideband spectrum sensing that relies on real-time deep learning tightly integrated into the transceiver’s baseband processing logic to detect and exploit unutilized spectrum bands. DeepSense uses a convolutional neural network (CNN) implemented in the wireless platform’s hardware fabric to analyze a small portion of the unprocessed baseband waveform to automatically extract the maximum amount of information with the least amount of I/Q samples. We extensively validate the accuracy\, latency and generality performance of DeepSense with (i) a 400 GB dataset containing hundreds of thousands of WiFi transmissions collected “in the wild” with different Signal-to-Noise-Ratio (SNR) conditions and over different days; (ii) a dataset of transmissions collected using our own software-defined radio testbed; and (iii) a synthetic dataset of LTE transmissions under controlled SNR conditions. We also measure the real-time latency of the CNNs trained on the three datasets with an FPGA implementation\, and compare our approach with a fixed energy threshold mechanism. Results show that our learning-based approach can deliver a precision and recall of 98% and 97% respectively and a latency as low as 0.61ms.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-daniel-uvaydov/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210525T100000
DTEND;TZID=America/New_York:20210525T110000
DTSTAMP:20260405T065107
CREATED:20210517T134657Z
LAST-MODIFIED:20210517T134657Z
UID:25994-1621936800-1621940400@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Mohammad Hossein Hajkazemi
DESCRIPTION:PhD Dissertation Defense: High-performance Translation Layers for Cloud Immutable Storage \nMohammad Hossein Hajkazemi \nLocation: Zoom Link \nAbstract: Most storage interfaces support in-place updates: blocks can be rewritten\, files can be modified at byte granularity\, fields may be updated in database table rows. Yet internally these layers often rely on out-of-place (immutable) writes. In some cases\, this may be necessary to use media\, such as flash\, SMR (shingled magnetic recording) and IMR (interlaced magnetic recording) disk\, which do not allow overwrites. In others\, it is used to simplify the implementation of transactions and/or crash consistency\, in the form of journaling\, write-ahead logging\, shadow paging\, etc. \nIn a storage system\, translation layers perform out-of-place writes\, and they are implemented in different layers of storage stack from the file system to the storage device firmware depending on the application. In this dissertation I focus on translation layers for cloud immutable storage technologies to improve the cloud I/O performance. As a part of my thesis\, I focus on translation layers for state-of-the-art immutable storage media such as SMR and IMR used in cloud environments\, proposing several novel algorithms to improve their efficiency. I also introduce FSTL\, a framework to design and implement SMR translation layer. Finally\, I describe Collage\, a virtual disk I developed over S3 (could be implemented over a similar object storage) using a translation layer which performs large\, sequential\, out-of-place writes for high performance. It optionally uses fast local storage for write logging and as a write-back cache\, guaranteeing prefix consistency under all failure conditions and recovery of all acknowledged writes if the local cache is not lost. Collage supports snapshots and cloned volumes\, performs well over erasure-coded storage\, and allows consistent asynchronous volume replication over geographic distances. I show that Collage can achieve massive performance improvements (e.g.\, over 100x for microbenchmarks and 10x for macro-benchmarks) over CEPH RBD\, a popular open-source scale-out virtual disk implementation.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-mohammad-hossein-hajkazemi/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210526T130000
DTEND;TZID=America/New_York:20210526T140000
DTSTAMP:20260405T065107
CREATED:20210524T182653Z
LAST-MODIFIED:20210524T182653Z
UID:26078-1622034000-1622037600@coe.northeastern.edu
SUMMARY:PhD Dissertation Defense: Kunpeng Li
DESCRIPTION:PhD Dissertation Defense: Visual Learning with Limited Supervision \nKunpeng Li \nLocation: Zoom Link \nAbstract: Deep learning models have achieved remarkable success in many computer vision tasks. However\, they typically rely on large amounts of carefully labeled training data whose annotating process is usually expensive\, time-consuming and even infeasible when considering the task complexity and scarcity of expert knowledge.\nIn this dissertation talk\, I will discuss several explorations along the direction of visual learning with limited supervision. They are mainly about learning from data with weak forms of annotations and learning from multi-modal data pairs. Specifically\, I will first present a guided attention learning framework to conduct semantic segmentation by mainly using image-level labels\, as such weak form of annotation can be collected much more efficiently than pixel-level labels. Under mild assumptions\, our framework can also be used as a plug-in to existing convolutional neural networks to improve their generalization performance. This is achieved by guiding the network to focus on correct things when learning concepts from a limited set of training samples. Besides\, I will also introduce models that can effectively learn from multi-modal data pairs without relying on dense annotations of visual semantic concepts. Our models incorporate relational reasoning ability into the visual representation learning process so that it can be better aligned with the supervision from corresponding text descriptions.
URL:https://coe.northeastern.edu/event/phd-dissertation-defense-kunpeng-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210527T090000
DTEND;TZID=America/New_York:20210527T180000
DTSTAMP:20260405T065107
CREATED:20210512T175130Z
LAST-MODIFIED:20210520T192306Z
UID:25900-1622106000-1622138400@coe.northeastern.edu
SUMMARY:NanoSI 2021 Annual Workshop on Nano Systems Innovation
DESCRIPTION:NanoSI 2021 Annual Workshop on Nano Systems Innovation \nThursday\, May 27th\, 2021 \nRegistration: Zoom Link \nLocation: Once registered\, a Zoom Link will be available. \nDescription: The goal of the Northeastern SMART Annual Workshop on Nano Systems Innovation (NanoSI 2021) is to bring together researchers\, government and industry to discuss new strategies to address the growing demand of sensing\, communication and artificial intelligence at the chip-scale while reducing the time for innovation and transition of the new foundational nano-system technologies that are going to be the at root of our nation’s economic strength\, national security and technological standing in the years to come. \nPreliminary Agenda:\n8:50 am – 9:00 am: Check-in\n9:00 am – 10:00 am: Opening remarks from University and SMART Center Leadership\, DARPA PMs\n10:00 am – 10:30 am: Plenary Talk – David Horsley\n10:30 am – 11:00 am: Intros from Industrial Partners\n11:00 am – 12:00 pm: Center Projects Presentations\n12:00 pm – 1:00 pm: Lunch Break\n1:00 pm – 3:00 pm: Center Projects Presentations\n3:00 pm – 4:30 pm: Panel Discussion with DARPA\, Industry\, and Academia: Benjamin Griffin\, Ronald Polcawich\, Amit Lal\, Troy Olsson\, David Horsley\n4:30 pm – 4:45 pm: Closing Remarks\n4:50 pm – 5:50 pm: IAB Meeting (Members Only)\n6:00 pm: Meeting adjourn \n  \n 
URL:https://coe.northeastern.edu/event/nanosi-2021-annual-workshop-on-nano-systems-innovation/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210601T120000
DTEND;TZID=America/New_York:20210601T130000
DTSTAMP:20260405T065107
CREATED:20210520T191425Z
LAST-MODIFIED:20210520T191425Z
UID:26062-1622548800-1622552400@coe.northeastern.edu
SUMMARY:Plant Shift Initiative | Food Tech
DESCRIPTION:To have 100% plant based diets is the most ecological and sustainable way to combat climate change. The main issue we have—making it tasty and accessible. This panel will dive into the food revolution taking form. \nPanelists \nSebastiano Cossia Castiglioni\, PNT’23 – Moderator\nFounder & Chairman\, Vegan Capital \nChris Kerr – Speaker\nCo-founder & Chair\, Gathered Foods Corp (Good Catch)\nFounding Partner & Chief Investment Officer\, Unovis Asset Management \nChristie Lagally – Speaker\nFounder & Chief Executive Officer\, Rebellyous Foods \nJulie Farkas – Speaker\nCo-founder & Social Impact Director\, PLNT Burger \nREGISTER
URL:https://coe.northeastern.edu/event/plant-shift-initiative-food-tech/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210603T110000
DTEND;TZID=America/New_York:20210603T120000
DTSTAMP:20260405T065107
CREATED:20210526T161737Z
LAST-MODIFIED:20210526T161737Z
UID:26088-1622718000-1622721600@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Mehdi Nasrollahpourmotlaghzanjani
DESCRIPTION:PhD Proposal Review: RFICs for Biomedical Magnetic and Magnetoelectric Microsystems \nMehdi Nasrollahpourmotlaghzanjani \nLocation: Zoom Links \nAbstract: Design and analysis of the advanced biomedical circuit and systems in wide variety of applications has emerged a significant interest. Not only in different engineering disciplines\, but also in a variety of applications such as neuroscience\, COVID-19\, etc. In this study\, we are proposing an implantable device\, handheld device for detecting different diseases and the RFIC design for the ME antenna and sensor evaluations.\nFirst\, we show and miniaturized implantable device for deep brain implantation that provides wireless power transfer efficiency (PTE) of 1 to 2 orders of magnitude higher than the reported micro-coils for brain stimulation. The proposed device will simultaneously measure the as magnetic field activity when neurons are firing. In the second part we will go over the RFIC design for the bio-implant devices\, evaluation of the ME antennas for communication purposes and the circuit interface to measure the ME and GMI sensors. For final part\, we will discuss the handheld device design for early diagnosis of different diseases such as\, lung cancer\, Alzheimer\, Covid-19\, etc through exhaled breath on the molecularly imprinted polymer (MIP) gas sensors.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-mehdi-nasrollahpourmotlaghzanjani/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210603T150000
DTEND;TZID=America/New_York:20210603T160000
DTSTAMP:20260405T065107
CREATED:20210602T173220Z
LAST-MODIFIED:20210602T173220Z
UID:26116-1622732400-1622736000@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Yumin Liu
DESCRIPTION:PhD Dissertation Defense: Learning from Spatio-Temporal Data with Applications in Climate Science \nYumin Liu \nLocation: Zoom Link \nAbstract: Climate change is one of the major challenges to human beings and many other species in our time. In the recent decade\, the number of disasters related to climate change such as wildfires\, storms\, floods and droughts are increasing\, and the casualty and economic losses caused by them are larger compared to those of decades ago. This calls for better and efficient ways to predict climate change in order to better prepare and reduce losses. Predicting climate change involves using historical observational data and model simulated data\, both of which usually involve multiple locations and timestamps and are spatio-temporal. With the rapid development and progress of machine learning\, these methods have achieved several impactful contributions in many domains; we would like to translate its impact to climate science.\nIn this thesis we address several problems in climate science. This challenging complex domain enable us to develop\, innovate\, adapt\, and advance machine learning in the following ways. 1) We develop a multi-task learning method to estimate relationships between tasks and learn the basis tasks in different locations especially for nearby locations which may share similar climate patterns. This method assumes that the weights of an observed task is a linear combination of several latent basis tasks and that the task relationships can be learnt by imposing a regularized precision matrix. 2) We propose a nonparameteric mixture of sparse linear regression models to cluster and identify important climate models for prediction. This model incorporates Dirichlet Process (DP) to automatically determine the number of clusters\, imposes Markov Random Field (MRF) constraints to guarantee spatio-temporal smoothness\, and selects a subset of global climate models (GCMs) that are useful for prediction within each spatio-temporal cluster with a spike-and-slab prior. We derive an effective Gibbs sampling method for this model. 3) We adapt image super resolution methods to climate downscaling — increasing spatial resolution for climate variables for local impact analysis. The proposed method is called YNet which is a novel deep convolutional neural network (CNN) with skip connections and fusion capabilities to perform downscaling for climate variables on multiple GCMs directly rather than on reanalysis data. 4) We use saliency map method to discover dependencies among climate variables. We propose the concept of cyclical saliency map (Cyclic-SM) which are meaningful in climate context and more robust to noise as compared to ordinary saliency maps. We show that Cyclic-SMs can reveal relevant spatial regions for prediction. We demonstrate the effectiveness of this method in climate downscaling\, ENSO index prediction and river flow prediction.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-yumin-liu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210608T100000
DTEND;TZID=America/New_York:20210608T110000
DTSTAMP:20260405T065107
CREATED:20210602T173048Z
LAST-MODIFIED:20210602T173048Z
UID:26114-1623146400-1623150000@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Lorenzo Bertizzolo
DESCRIPTION:PhD Dissertation Defense: Software-Defined Wireless Networking for 5G and Beyond: From Indoor Cells to Non-Terrestrial UAV Networks \nLorenzo Bertizzolo \nLocation: Microsoft Teams \nAbstract: While Software-defined Networking (SDN) is a consolidated and widely adopted concept in fixed infrastructure\, its adoption to the wireless domain has been limited by some fundamental challenges. Unlike traditional fixed infrastructure\, which relies entirely on fiber communications\, wireless networks suffer from unpredictable access and backhaul operations. Therefore\, they ask for new architectural solutions to implement SDN. A prerequisite to implementing SDN for wireless is to adopt programmable radio hardware and software-based implementation of the wireless protocol stack. Then\, it is necessary to adopt control APIs that expose the wireless protocols’ internal operations to external control. Finally\, to bridge the gap between SDN theory and implementation\, this thesis proposes a series of architectural solutions that provide the communication infrastructure and the architectural control innovations necessary to implement SDN control and optimization on real wireless systems. Specifically\, this thesis proposes two different architectural solutions. For fixed Radio Access Networks (RANs) that benefit from a low-latency and reliable backhaul infrastructure\, we propose to implement a centralized control approach similar to SDN for fixed infrastructure. Here a central controller collects network state information of a distributed RAN\, solves a network control problem\, and distributes the solutions to the individual wireless nodes. On the other hand\, for infrastrcuture-less RANs\, which implement both the access and the backhaul in wireless\, we propose a new architectural solution that moves the control logic to the edge of the networks\, to the very wireless nodes.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-lorenzo-bertizzolo/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210609T120000
DTEND;TZID=America/New_York:20210609T133000
DTSTAMP:20260405T065107
CREATED:20210513T194458Z
LAST-MODIFIED:20210513T194458Z
UID:25937-1623240000-1623245400@coe.northeastern.edu
SUMMARY:CILS June Seminar: Bruker BipSpec 3T MRI
DESCRIPTION:Join the Institute for Chemical Imaging of Living Systems for an informational seminar on the Bruker BioSpec 3T MRI machine. \nThe seminar will cover \n\nhow preclinical MR offers longitudinal observation/measurement of disease processes\nan overview of components and process for preclinical MR\nreview of methods and data obtained in preclinical MR\n\nFollowing the talk\, Kristine Ma\, PhD candidate from the Clark Lab\, will present on DNA-based pH-responsive MR contrast agents. \nRegister: https://forms.gle/PTHE2yNtftbkRBGV9
URL:https://coe.northeastern.edu/event/cils-june-seminar-bruker-bipspec-3t-mri/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210610T130000
DTEND;TZID=America/New_York:20210610T140000
DTSTAMP:20260405T065107
CREATED:20210510T145954Z
LAST-MODIFIED:20210510T145954Z
UID:25857-1623330000-1623333600@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Andac Demir
DESCRIPTION:PhD Proposal Review: Automated Bayesian Network Exploration for Nuisance-Robust Inference \nAndac Demir \nLocation: Zoom Link \nAbstract: A fundamental challenge in the analysis of physiological signals is learning useful features that are robust to nuisance factors e.g.\, inter-subject & inter-session variability\, and achieve the highest nuisance-invariant classification performance. Towards resolving this problem\, we introduce 2 frameworks: AutoBayes\, which is an AutoML approach to conduct neural architecture search for research prototyping\, and a GNN based framework: EEG-GNN.\nThe ultimate goal of the AutoBayes framework is to identify the conditional relationship between a physiological dataset\, associated task labels\, nuisance variations and potential latent variables in order to robustly infer the task labels invariant of nuisance factors. Nuisance factors in the case of physiological datasets could be variations in subjects or sessions\, but we only focus on subject variations in the experiments. AutoBayes enumerates all plausible Bayesian networks between data\, labels\, nuisance variations and potential latent variables\, detects and prunes the unnecessary edges according to Bayes-Ball Algorithm\, and then trains the resulting DNN architectures for different hyperparameter configurations in an adversarial/non-adversarial or a variational/non-variational setting to achieve the highest validation performance. Instead of hyperparameter tuning for model optimization\, AutoBayes concentrates on the architecture search of plausible Bayesian networks\, and achieves state-of-the-art performance across several physiological datasets. Furthermore\, we ensemble several Bayesian networks by stacking their posterior probability vectors in a higher level learning space\, train a shallow MLP as a meta learner\, and measure the task and nuisance classification performance on a hold-out dataset. We observe that exploration of different inference Bayesian networks has a significant benefit in improving the robustness of the machine learning pipeline\, and the parallel activity of vast assemblies of different Bayesian network models significantly reduces variation across subjects in the cross-validation setting.\nIn the second part of the proposal\, we benchmark the performance of EEG-GNN against the AutoBayes framework. CNN’s have been frequently used to extract subject-invariant features from EEG for classification tasks\, but this approach holds the underlying assumption that electrodes are equidistant analogous to pixels of an image and hence fails to explore/exploit the complex functional neural connectivity between different electrode sites. We overcome this limitation by tailoring the concepts of convolution and pooling applied to 2D grid-like inputs for the functional network of electrode sites. Furthermore\, we develop various GNN models that project electrodes onto the nodes of a graph\, where the node features are represented as EEG channel samples collected over a trial\, and nodes can be connected by weighted/unweighted edges according to a flexible policy formulated by a neuroscientist. The empirical evaluations show that our proposed GNN-based framework outperforms standard CNN classifiers across ErrP and RSVP datasets\, as well as allowing neuroscientific interpretability and explainability to deep learning methods tailored to EEG related classification problems. Another practical advantage of our GNN-based framework is that it can also be used in EEG channel selection\, which is critical for reducing computational cost\, and designing portable EEG headsets.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-andac-demir/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210610T153000
DTEND;TZID=America/New_York:20210610T163000
DTSTAMP:20260405T065107
CREATED:20210609T134644Z
LAST-MODIFIED:20210609T134644Z
UID:26239-1623339000-1623342600@coe.northeastern.edu
SUMMARY:PhD Proposal Review: Bernard Herrera
DESCRIPTION:PhD Proposal Review: Ferroelectric Micromachined Ultrasonic Transducers for Intra-body and In-memory Sensing Applications \nBernard Herrera \nLocation: Teams \nAbstract: Piezoelectric Micromachined Ultrasonic Transducers (PMUTs) are Micro Electro-Mechanical Systems (MEMS) devices that have become an established technology in applications such as range-finding\, fingerprint sensing and imaging due to their capability of ultrasonic transduction in a miniaturized footprint\, easily amenable to create large arrays. However\, their application space still remains quite open. PMUTs are well fitted to applications in liquid media\, such as implantable and underwater devices\, due to their inherent acoustic matching and wide bandwidth. Thus\, in the first part of this proposal\, we explore novel applications such as PMUT-based intra-body networking\, power transfer\, source localization\, wide-band matching and duplexing.\nAluminum Nitride (AlN) has been the material of choice for our PMUTs due to its biocompatibility and possibility of single-chip integration with supporting CMOS circuitry. Scandium doping of AlN thin films has recently been demonstrated to increase piezoelectric coupling coefficients while introducing ferroelectric properties in the material. However\, a simultaneous use of both capabilities has not been demonstrated in the state-of-the-art. The ability of having distinct ferroelectric states\, that alter the mechanical performance of the devices\, allows for in-memory sensing and actuation features and provides the building blocks for neuromorphic signal processing capabilities. The second part of the proposal explores the AlScN material integration into novel Ferroelectric Micromachined Ultrasonic Transducers (FMUTs) and their emerging application space.
URL:https://coe.northeastern.edu/event/phd-proposal-review-bernard-herrera/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210610T163000
DTEND;TZID=America/New_York:20210610T173000
DTSTAMP:20260405T065107
CREATED:20210607T202747Z
LAST-MODIFIED:20210607T202747Z
UID:26188-1623342600-1623346200@coe.northeastern.edu
SUMMARY:CommLab Research Dissemination Series: Literature Review Workshop
DESCRIPTION:Are you joining a new research area that you are not familiar with and don’t know how to get started? Or perhaps you are writing an introduction section to your dissertation or fellowship proposal and need help? \nJoin the Northeastern COE CommLab for the next workshop in the Research Dissemination Series. We will discuss the importance of writing a literature review and different tools you can use to communicate broad ideas effectively. \nRegister for this Zoom virtual workshop.
URL:https://coe.northeastern.edu/event/commlab-research-dissemination-series-literature-review-workshop/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210611T103000
DTEND;TZID=America/New_York:20210611T113000
DTSTAMP:20260405T065107
CREATED:20210609T134749Z
LAST-MODIFIED:20210609T134749Z
UID:26237-1623407400-1623411000@coe.northeastern.edu
SUMMARY:PhD Proposal Review: Rashida Nayeem
DESCRIPTION:PhD Proposal Review: Human Strategies in the Control of Complex Objects: A Task-Dynamic Approach with Clinical Applications \nRashida Nayeem \nLocation: Zoom Link \nPasscode: 247537 \nAbstract: Functional interaction with objects – tool use – is essential in daily living and is regarded as the foundation of our evolutionary advantage. Humans effortlessly interact with a variety of objects\, including those with complex internal dynamics. Even the simple action of picking up a cup of coffee to drink is a mechanically intricate process: the hand applies a force to the cup\, and indirectly to the liquid\, which exerts forces back on the hand. Reacting to and mitigating these dynamics in real time is difficult due to long sensorimotor delays and ubiquitous noise in the human sensorimotor system. Hence\, prediction is necessary to preempt undesired ‘sloshing’. But prediction of this nonlinear and potentially chaotic object is extremely difficult. Hence\, this research tests the hypothesis that humans learn to control the object to make dynamics simpler––or more predictable. Inspired by the task of transporting a ‘cup of coffee\,’ a series of experiments use a virtual and a real testbed that model the task as a cup-and-ball system. In all experiments\, human subjects move the cup with a rolling ball inside. Aim-1 investigates the effect of linearization on human control strategies in a 2D virtual task. Aim-2 examines if humans exploit initial conditions to facilitate predictable dynamics in the 2D virtual task. This is tested in subjects that are provided with either full sensory information\, or when deprived of visual or haptic information. Aim-3 examines how subjects explore and prepare the cup-and-ball in the same 2D task and a novel 3D virtual task introducing planar cup movements. The question is how subjects explore and transport objects that have different dynamic properties\, either unknown or indicated by the visual cues. The analysis adopts a task-dynamic approach that affords principled hypothesis-testing by parsing the complex dynamics into execution and result variables\, with minimal assumptions about the human controller. Aim-4 takes the insights from this basic research to a clinical context\, testing patients with stroke in this functional task. A real version of the cup-and-ball task was created to quantitatively assess severity and recovery of motor impairment in patients after stroke. Using the same analysis methods\, the objective is to sensitively assess impairment in the context of a functional skill.
URL:https://coe.northeastern.edu/event/phd-proposal-review-rashida-nayeem/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210614T090000
DTEND;TZID=America/New_York:20210614T100000
DTSTAMP:20260405T065107
CREATED:20210602T141602Z
LAST-MODIFIED:20210602T141602Z
UID:26120-1623661200-1623664800@coe.northeastern.edu
SUMMARY:Robotics at Northeastern: A personal view of Autonomy Underwater\, On Land and with Aerial Systems
DESCRIPTION:This session will cover interconnected nature of autonomous vehicles on land\, underwater\, and in the air. Using examples from research at Northeastern\, it explores the interconnections of systems design\, application-driven robotics\, and the fundamental algorithms associated with simultaneous localization and mapping (SLAM)\, machine learning (ML)\, and computer vision and imaging as it applies to a variety of scientific and commercial settings. \nThis webinar will be lead by ECE Faculty Member Dr. Hanumant Singh. Event logistics are below: \nDate: June 14\, 2021 \nTime: 9:00am EST \nRegistration \n 
URL:https://coe.northeastern.edu/event/robotics-at-northeastern-a-personal-view-of-autonomy-underwater-on-land-and-with-aerial-systems/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210617T100000
DTEND;TZID=America/New_York:20210617T110000
DTSTAMP:20260405T065107
CREATED:20210614T141012Z
LAST-MODIFIED:20210614T141012Z
UID:26286-1623924000-1623927600@coe.northeastern.edu
SUMMARY:PhD Dissertation Defense: Huaihao Chen
DESCRIPTION:PhD Dissertation Defense: Integrated RF Devices Based on Magnetoelectric Coupling \nHuaihao Chen \nLocation: Zoom Meeting \nAbstract: The magnetoelectric (ME) coupling effect is a coupling behavior between the magnetic properties and electric properties in a single-phase crystal or a composite structure. At present\, ME composite is more widely used in practical application than the single-phase crystal due to the larger coupling coefficient and higher working temperature. Based on the coupling direction\, there are two types of ME coupling: the direct coupling by using magnetic field to control the electric polarization; the converse coupling by using electric field to control the magnetization. With the strong coupling effect and the low power consumption\, ME coupling becomes more and more attractive in RF devices design\, including magnetic sensor\, ME memory\, energy harvester and so on.\nIn this dissertation\, the integrated inductor and ME antenna based on ME coupling are reported\, including the design\, simulation\, fabrication and test of the devices. The performances\, advantages and issues of these devices are discussed\, and some improvements are applied for a better performance.\nThe first part of this thesis is the integrated high-Q and RF tunable inductors. In this part\, the 1-D laminated iron core inductor model\, choosing of magnetic material for the inductor core and the tuning principle based on converse ME coupling is explained firstly. Then\, the fabrication process flow is described. The practical high-Q inductor shows a constant inductance of ~1.4 nH in a wide frequency range from DC to 3 GHz\, with a peak Q-factor of 32.7\, after magnetic annealing. By attaching a PMN-PT slab to the device\, a tunable inductor is realized. Inductance tunabilites are achieved under both magnetic field (69.2%) and electric field (191%)\, which is higher than most of the reported inductors.\nThe second part is the design of ME antenna for biomedical implants. FeGaB/AlN heterostructure is chosen as the ME resonator of the antenna. The operation frequency is the acoustic resonance\, decided by the width of the resonator. This ME antenna has a ~1000 times smaller volume than conventional antennas\, due to the smaller wavelength of acoustic wave than electromagnetic wave. After fabrication\, the measured S11 resonance peak matches with the simulation\, and the antenna gain of -37.1 dBi is calculated by a gain comparison method. The modified Butterworth-Van Dyke (mBVD) model is used to calculate the Q-factor (114) and electromechanical coupling coefficient (kt2\, 0.98%). The radiation pattern and polarization are measured\, proving that this ME antenna performs as a magnetic dipole. Finally\, the input impedance matching is optimized with array structure. The third part is the performance improvement of FeGaB thin film by minimizing the mechanical stress from the film deposition\, by controlling the deposition pressure and magnetic annealing. The film deposited under 2 mTorr pressure shows the lowest stress and best magnetic properties of coercive field\, saturation magnetization\, magnetic damping and magnetostriction. And the magnetic annealing shows an improvement on the FeGaB film. This research helps to improve the performance of devices based on FeGaB thin film.
URL:https://coe.northeastern.edu/event/phd-dissertation-defense-huaihao-chen/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210617T120000
DTEND;TZID=America/New_York:20210617T133000
DTSTAMP:20260405T065107
CREATED:20210524T182548Z
LAST-MODIFIED:20210524T182548Z
UID:26075-1623931200-1623936600@coe.northeastern.edu
SUMMARY:Pre-Launch Briefing: Preparing to Spin Out A Hard-Tech Startup
DESCRIPTION:You’ve created something interesting and exciting in the lab\, published a few papers and maybe filed some patents and are wondering what to do next.\nMike Fuerstman and Becky Wilson of Rhapsody Venture Partners will walk through questions you should be asking and answering about your technology and your objectives to figure out the best path forward – whether it’s a startup or something else entirely.\nThe team will provide insights based on Rhapsody’s approach with its portfolio companies in hard tech along with concrete examples and best practices.
URL:https://coe.northeastern.edu/event/pre-launch-briefing-preparing-to-spin-out-a-hard-tech-startup/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210618T090000
DTEND;TZID=America/New_York:20210618T100000
DTSTAMP:20260405T065107
CREATED:20210614T141136Z
LAST-MODIFIED:20210614T141136Z
UID:26284-1624006800-1624010400@coe.northeastern.edu
SUMMARY:PhD Dissertation Defense: Trinayan Baruah
DESCRIPTION:PhD Dissertation Defense: Improving the Virtual Memory Efficiency of GPUs \nTrinayan Baruah \nLocation: Zoom Link \nAbstract: GPUs have been adopted widely based their ability to exploit data-level parallelism found in modern-day applications\, ranging from high performance computing to machine learning. This widespread adoption has\, in part\, been accelerated by the development of more intuitive high-level programming languages\, efficient runtimes and drivers\, and easier mechanisms to manage data movement. Modern day GPUs and multi-GPU systems utilize virtual memory systems\, enabling programmers to access large address spaces that are beyond the physical memory limits a GPU. There mechanisms have built in mechanisms for memory translation\, sparing the programmer from having to reason about complex data-movement operations. Virtual memory support on a GPU includes both hardware and software support. At the hardware level\, translation lookaside buffers~(TLBs) are used to cache translations close to the compute units. At the software level\, the programming model supports a unified memory model which automates the movement of pages across multiple devices in a a system. Despite the improvements in programmability\, due to inefficiencies existing virtual memory management mechanisms\, including TLB management and page migration policies\, the performance obtained on today’s GPUs is sub-optimal.\nIn this dissertation\, we first identify the key challenges in virtual memory support for GPUs today. We then propose mechanisms to reduce the bottlenecks arising from virtual memory support at both a hardware level and at the runtime level. This allows GPUs to fully enjoy the benefits of virtual memory\, while ensuring high performance. We also develop simulation tools that enable researchers to explore new and novel virtual memory features in future single GPU and multi-GPU systems.\nTo enhance hardware support for virtual memory on a GPU\, we explore a mechanism that enables prefetching of page-table entries into the GPUs TLBs\, thereby reducing the number of TLB misses and improving performance. We also leverage the fact that many page-table entries can be shared across different GPU cores. We design a low-cost interconnect that enables sharing of page-table entries across the GPU cores. To improve the performance of unified memory on multi-GPU systems\, we propose a hardware/software mechanism that monitors accesses to each page\, and uses this information when making page-migration decisions. We also propose mechanisms to reduce the cost of TLB shootdowns on the GPU during page-migration in NUMA multi-GPU systems.
URL:https://coe.northeastern.edu/event/phd-dissertation-defense-trinayan-baruah/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210622T130000
DTEND;TZID=America/New_York:20210622T140000
DTSTAMP:20260405T065107
CREATED:20210617T134637Z
LAST-MODIFIED:20210617T134637Z
UID:26318-1624366800-1624370400@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Majid Sabbagh
DESCRIPTION:PhD Dissertation Defense: The Perils of Shared Computing: A Hardware Security Perspective \nMajid Sabbagh \nLocation: Microsoft Teams Link \nAbstract: Modern processors and hardware accelerators\, in the cloud or on the edge\, are capable of running multiple workloads from different users concurrently. Despite software techniques for security such as virtualization and containers\, a new attack surface is emerging that pertains to the hardware vulnerabilities of shared computing resources\, posing serious threats to shared computing. Fault attacks (FAs)\, Side-Channel Attacks (SCAs)\, and Transient-Execution Attacks (TEA) are three hardware-oriented attacks that target the system implementations. FAs aim to tamper the integrity of application execution through different fault injection methods\, to compromise the data or disrupt computation at run-time. SCAs exploit the information leakage of sensitive applications in physical parameters\, such as power consumption\, electromagnetic emanations\, and timing\, to breach the confidentiality of the application. TEAs exploit transient hardware operations such as speculative execution in Central Processing Units (CPUs) to tap on sensitive data temporarily and retrieve them from implicative microarchitectural states.\nIn this dissertation\, we investigate the three kinds of attacks that all exploit vulnerabilities due to shared computing. We first introduce a new non-invasive FA against Graphics Processing Units (GPUs)\, called overdrive fault attacks. We discover the security vulnerability of GPU’s voltage-frequency scaling (VFS) mechanism\, a common feature to balance power consumption and performance. An out-of-specification configuration of GPU voltage and frequency can be set by an adversary on the host CPU\, through the software interfaces to GPU’s power management units. This setting will cause timing violations for the computation and result in silent data corruptions (SDCs). We apply the overdrive fault attacks on two common victim applications. One is cryptographic applications accelerated by GPU. We launch a differential fault analysis (DFA) attack on an AES kernel running on an AMD RX 580 GPU and successfully recover the secret key. The other victim is convolutional neural network (CNN) inference. We thoroughly characterize fault injections and propagation in a CNN on a GPU and analyze the controllability of the attack. We successfully launch an end-to-end misclassification attack during CNN inferences with careful timing control.\nWe then evaluate a timing side-channel attack called Prime+Probe attack on CPUs and propose a Side-Channel Attack DEtection Tool (SCADET). SCADET is a methodology and a tool that operates on an x86 program’s binary. It records and analyzes the program’s memory accesses using dynamic binary instrumentation by running the program in a controlled environment to accurately identify the malicious access patterns demonstrated by the Prime+Probe attack.\nFinally\, we introduce an efficient hardware-level taint-tracking defense against the most prominent TEAs\, the speculative execution attacks. We take a secure-by-design approach and propose a mechanism called Secure Speculative Execution via RISC-V Open Hardware Design (SSE-RV)\, based on the latest Berkeley Out-of-Order Machine (SonicBOOM). We prototype our SSE-RV processor on an FPGA running a Linux operating system. Our results show that we can protect against Spectre-v1\, v2\, and v5. Our defense scheme is general and can be extended to protect against other transient execution attacks.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-majid-sabbagh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210622T140000
DTEND;TZID=America/New_York:20210622T150000
DTSTAMP:20260405T065107
CREATED:20210614T171638Z
LAST-MODIFIED:20210614T171638Z
UID:26295-1624370400-1624374000@coe.northeastern.edu
SUMMARY:PhD Dissertation Defense: Ala Tokhmpash
DESCRIPTION:PhD Dissertation Defense: Fractional Order Derivative in Circuits\, Systems\, and Signal Processing with Specific Application to Seizure Detection \nAla Tokhmpash \nLocation: Zoom Link \nAbstract: Epilepsy is a chronic brain disease that affects around 50 million people worldwide. This disease is characterized by recurrent seizures\, which are brief episodes of involuntary movement that may involve a part or the entire body and are sometimes accompanied by loss of consciousness. It is the third most common neurological disorder in the United States\, only after Alzheimer’s disease and stroke. Patients suffering from epilepsy\, a brain disorder\, can have more than one type of seizure. Seizure detection systems can be life-changing for patients with epileptic seizures. By accurately identifying the periods in which seizure occurrence has a higher chance of happening we can help epileptic patients live a more normal life. Prior works on automated seizure detection overwhelmingly either rely solely entirely on domain knowledge\, or instead use a black box deep learning model. This thesis aims to integrate machine learning techniques with available seizure detection methods to improve detection performance. In this process\, we take advantage of mathematical tools provided by fractional-order derivatives as well as fuzzy entropy concepts. Specifically\, 1) we show the effectiveness of fractional order methods (FOM) in representing signals with long-range dependencies 2) using case studies in control and power systems\, we further examine the performance of FOM in the presence of parameter uncertainty. 3) using two publicly available data sets of brain signals from multiple patients\, we develop a cohesive framework to leverage FOM for extracting features that can be then used by statistical learning methods. 4) following recent works in this field\, we generalize the notion of entropy to include the fractional-order case. Combined with the fuzzy sets describing the uncertainty in data\, we leverage fractional fuzzy entropy as a robust descriptor of the state of brain signals. Through these case studies\, we demonstrate a significant increase in performance accuracy compared to models that do not consider FOM.
URL:https://coe.northeastern.edu/event/phd-dissertation-defense-ala-tokhmpash/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210629T110000
DTEND;TZID=America/New_York:20210629T120000
DTSTAMP:20260405T065107
CREATED:20210623T171312Z
LAST-MODIFIED:20210623T171312Z
UID:26363-1624964400-1624968000@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Yaoshen Yuan
DESCRIPTION:PhD Dissertation Defense: Enhancing Monte Carlo Light Modeling Methods for the Development of Near-infrared Based Brain Techniques \nYaoshen Yuan \nLocation: Zoom Link \nAbstract: Studying light propagation in biological tissues is critical for developing biophotonics techniques and its applications. Monte Carlo (MC) method\, a stochastic solver for radiative transfer equation\, has been recognized as the gold standard for modeling light propagation in turbid media. However\, due to the stochastic nature of MC method\, millions even billions of photons are usually required to achieve accurate results using MC method\, leading to a long computational time even with the acceleration using graphical processing units (GPU).\nFurthermore\, due to the rapid advances in multi-scale optical imaging techniques such as optical coherence tomography (OCT) and multiphoton microscopy (MPM)\, there is an increasing need to model light propagation in extremely complex tissues such as vessel networks. The mesh-based Monte Carlo (MMC) is usually superior than the voxel-based MC method for such modeling since unlike grid-like voxels\, tetrahedral meshes can represent arbitrary shapes with curved boundaries. However\, the mesh density can be excessively high when the tissue structure is extremely complex\, resulting in high computational costs and memory demand.\nThe goal of this proposal is to focus on solving the challenges mentioned above. To tackle the first challenge\, we came up with a filtering approach with GPU acceleration to improve the signal-to-noise ratio (SNR) of the results while keeping the simulated photons low. The adaptive non-local means (ANLM) filter is selected to suppress the stochastic noise in MC results because 1) the filtering process on each voxel is mutually independent\, making it possible for parallel computing; 2) it has high performance in denoising and a strong capacity in edge-preserving.\nFor the second problem\, a novel method\, implicit mesh-based Monte Carlo (iMMC)\, was proposed to significantly reduce the mesh density. The iMMC utilizes the edge\, node and face of the tetrahedral mesh to model tissue structures with shapes of cylinder\, sphere and thin layer. The typical applications for edge\, node and face-based iMMC are vessel networks\, porous media and membranes\, respectively.\nLastly\, we applied MC simulations and aforementioned filter on segmented brain models derived from MRI neurodevelopmental atlas to estimate the light dosage for transcranial photobiomodulation (t-PBM)\, a technique for treating major depressive disorder using near infrared\, across lifespan. The iMMC simulation was also applied to evaluate the impact of human hair on the brain sensitivity for functional near-infrared spectroscopy (fNIRS). Furthermore\, a new approach that can improve the penetration depth in optical brain imaging as well as PBM is proposed. In this approach\, the possibility of placing light sources in head cavities is investigated using MC simulations. The preliminary results demonstrate a better performance in deep brain monitoring compared to the standard transcranial approach using 10-20 EEG positioning system.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-yaoshen-yuan-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210701T110000
DTEND;TZID=America/New_York:20210701T120000
DTSTAMP:20260405T065107
CREATED:20210623T171449Z
LAST-MODIFIED:20210623T171449Z
UID:26361-1625137200-1625140800@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Xianfeng Liang
DESCRIPTION:PhD Dissertation Defense: RF Magnetoelectric Microsystems \nXianfeng Liang \nLocation: Zoom Link \nAbstract: Multiferroic materials are the materials that inherently exhibit two or more ferroic properties\, such as ferroelectricity\, ferromagnetism and ferroelasticity\, etc. Magnetoelectric (ME) materials with coupled magnetization and electric polarization have attracted intense interests recently due to the realization of strong ME coupling and their key roles inME applications. Since the revival of thin-film ME heterostructures with giant ME coefficients\, a variety of multifunctional ME devices\, such as sensors\, inductors\, filters\, antennas etc. have been developed. Exciting progress has been made on novel ME materials and devices because of their high-performance ME coupling.\nIn this dissertation\, we will first show the properties of magnetostrictive (FeGaC and SmFe) and piezoelectric (ZnO)thin-film materials that are necessary for realizing strong ME coupling. A systematic investigation of the soft magnetism\, the change of modulus of elasticity with magnetization (delta-E effect)\, and microwave properties was carried out on FeGaC and SmFe thin films. We successfully developed the magnetostrictive FeGaC thin films with low coercive field of less than 1 Oe\, high saturation magnetization\, narrow ferromagnetic resonance (FMR) linewidth\, and an ultra-low Gilbert damping constant of 0.0027. A record high piezomagnetic coefficient of 9.71 ppm/Oe\, high saturation magnetostriction constant of 81.2 ppm\, and large delta-E effect of -120 GPa at 500 nm were achieved. ZnO films with high c-axis crystal orientation was also achieved by carefully optimizing the sputtering process parameters. These properties make them attractive materials for magnetoelectric and other voltage tunable RF/microwave device applications.\nAfter presenting the magnetostrictive and piezoelectric thin films and their static and dynamic properties\, we introduce the radio frequency (RF) ME microsystems. Mechanically driven antennas have been demonstrated to be the most effective method to miniaturize antennas compared to state-of-the-art compact antennas.The ME antennas based on a released magnetostrictive/piezoelectric heterostructure rely on electromechanical resonance instead of electromagnetic wave resonance\, which results in an antenna size as small as one-thousandth of an electromagnetic wavelength. Due to the strong ME coupling in thin-film ME heterostructures\, we proposed the ultra-compact MEMS ME antennas and improved their performance by using anchor designs\, array structure\, and SMR structure. These miniaturized robustME antennas can be implemented in numerous real-world applications such as internet of things\, wearable and bio-implantable devices\, smart phones\, wireless communication systems\, etc. The ME antennas\, with an overall dimension of 700 m×700 m (L×W)\, were designed to operate at a resonant frequency of 2 GHz and experimentally demonstrated a gain of -18.85 dBi. Furthermore\, we demonstrated highly sensitive integrated RF giant magnetoimpedance (GMI)sensors based on amplitude and phase sensitive mechanisms. The amplitude and phase magnetic noise levels were demonstrated to be 810pT /√Hz at 1000 Hz and 100pT /√Hz\, respectively.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-xianfeng-liang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210707T140000
DTEND;TZID=America/New_York:20210707T150000
DTSTAMP:20260405T065107
CREATED:20210706T135010Z
LAST-MODIFIED:20210706T135010Z
UID:26505-1625666400-1625670000@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Xiaolong Ma
DESCRIPTION:PhD Proposal Review: Towards Efficient Deep Neural Network Execution with Model Compression and Platform-specific Optimization \nXiaolong Ma \nLocation: Zoom \nAbstract: Deep learning or deep neural networks (DNNs) have become the fundamental element and core enabler of ubiquitous artificial intelligence. Recently\, with the emergence of a spectrum of high-end mobile devices\, many deep learning applications that formerly required desktop-level computation capability are being transferred to these devices. However\, executing DNN inference is still challenging considering the high computation and storage demands\, specifically\, if real-time performance with high accuracy is needed. Weight pruning of DNNs is proposed\, but existing schemes represent two extremes in the design space: non-structured pruning is fine-grained\, accurate\, but not hardware friendly; structured pruning is coarse-grained\, hardware-efficient\, but with higher accuracy loss. To solve the problem\, we propose a compression-compilation co-optimization framework\, which includes 1) a new dimension\, fine-grained pruning patterns inside the coarse-grained structures that achieves accuracy enhancement and preserve the structural regularity that can be leveraged for hardware acceleration\, 2) a pattern-aware pruning framework that achieves pattern library extraction\, pattern selection\, pattern and connectivity pruning and weight training simultaneously\, and 3) a set of thorough architecture-aware compiler/code generation-based optimizations\, i.e.\, filter kernel reordering\, compressed weight storage\, register load redundancy elimination\, and parameter auto-tuning for real-time execution of the mainstream DNN applications on the mobile platforms. Evaluation results demonstrate that our framework outperforms three state-of-the-art end-to-end DNN frameworks\, TensorFlow Lite\, TVM\, and Alibaba Mobile Neural Network with speedup up to 44.5x\, 11.4x\, and 7.1x\, respectively\, with no accuracy compromise. Real-time inference of representative large-scale DNNs (e.g.\, VGG-16\, ResNet-50) can be achieved using mobile devices.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-xiaolong-ma/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210707T170000
DTEND;TZID=America/New_York:20210707T180000
DTSTAMP:20260405T065107
CREATED:20210706T135131Z
LAST-MODIFIED:20210706T135131Z
UID:26484-1625677200-1625680800@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Kaidi Xu
DESCRIPTION:PhD Dissertation Defense: Can We Trust AI? Towards Practical Implementation and Theoretical Analysis in Trustworthy Machine Learning \nKaidi Xu \nLocation: Zoom Link \nAbstract: Deep learning has achieved extraordinary performance in many application domains recently. It has been well accepted that DNNs are vulnerable to adversarial attacks\, which raises concerns of DNNs in security-critical applications and may result in disastrous consequences. Adversarial attacks are usually implemented by generating adversarial examples\, i.e.\, adding sophisticated perturbations onto benign examples\, such that adversarial examples are classified by the DNN as target (wrong) labels instead of the correct labels of the benign examples. The adversarial machine learning aims to study this phenomenon and leverage it to build robust machine learning systems and explain DNNs.\nIn this talk\, I will present the mechanism of adversarial machine learning in both empirical and theoretical ways. Specifically\, a uniform adversarial attack generation framework\, structured attack (StrAttack) is introduced\, which explores group sparsity in adversarial perturbations by sliding a mask through images aiming for extracting key spatial structures. Second\, we discuss the feasibility of adversarial attacks in the physical world and introduce a convincing framework\, Expectation over Transformation (EoT). Utilize EoT with Thin Plate Spline (TPS) transformation\, we can generate Adversarial T-shirts\, a powerful physical adversarial patch for evading person detectors even if it could undergo non-rigid deformation due to a moving person’s pose changes. Third\, we stand on the defense side and design the first adversarial training method based on Graph Neural Network. Finally\, we introduce Linear relaxation-based perturbation analysis (LiRPA) for neural networks\, which computes provable linear bounds of output neurons given a certain amount of input perturbation. LiRPA studies the adversarial example in a theoretical way and can guarantee the test accuracy of a model by given perturbation constraints. The generality\, flexibility\, efficiency and ease-of-use of our proposed framework facilitate the adoption of LiRPA based provable methods for other machine learning problems beyond robustness verification
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-kaidi-xu/
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DTSTART;TZID=America/New_York:20210713T100000
DTEND;TZID=America/New_York:20210713T110000
DTSTAMP:20260405T065107
CREATED:20210706T134832Z
LAST-MODIFIED:20210706T134832Z
UID:26507-1626170400-1626174000@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Maher Kachmar
DESCRIPTION:PhD Dissertation Defense: Active Resource Partitioning and Planning for Storage Systems using Time Series Forecasting and Machine Learning Techniques \nMaher Kachmar \nLocation: Zoom \nAbstract: In today’s enterprise storage systems\, supported data services such as snapshot delete or drive rebuild can result in tremendous performance overhead if executed inline along with heavy foreground IO\, often leading to missing Service Level Objectives (SLOs). Moreover\, new classes of data services\, such as thin provisioning\, instant volume snapshots\, and data reduction features make capacity planning and drive wear-out prediction quiet challenging. Having enough free storage pool capacity available ensures that the storage system operates in favorable conditions during heavy foreground IO cycles. This enables the storage system to defer background work to a future idle cycle. Static partitioning of storage systems resources such as CPU cores or memory caches may lead to missing data reduction rate (DRR) guarantees. However\, typical storage system applications such as Virtual Desktop Infrastructure (VDI) or web services follow a repetitive workload pattern that can be learned and/or forecasted. Learning these workload pattern allows us to address several storage system resource partitioning and planning challenges that may not be overcome with traditional manual tuning and primitive feedback mechanism.\nFirst\, we propose a priority-based background scheduler that learns this pattern and allows storage systems to maintain peak performance and meet service level objectives (SLOs) while supporting a number of data services. When foreground IO demand intensifies\, system resources are dedicated to service foreground IO requests. Any background processing that can be deferred is recorded to be processed in future idle cycles\, as long as our forecaster predicts that the storage pool has remaining capacity. A smart background scheduler can adopt a resource partitioning model that allows both foreground and background IO to execute together\, as long as foreground IOs are not impacted\, harnessing any free cycles to clear background debt. Using traces from VDI and web services applications\, we show how our technique can out-perform a static policy that sets fixed limits on the deferred background debt and reduces SLO violations from 54.6% (when using a fixed background debt watermark)\, to only 6.2% when employing our dynamic smart background scheduler.\nSecond\, we propose a smart capacity planning and recommendation tool that ensures the right number of drives are available in the storage pool in order to meet both capacity and performance constraints\, without over-provisioning storage. Equipped with forecasting models that characterize workload patterns\, we can predict future storage pool utilization and drive wear-outs. Similarly\, to meet SLOs\, the tool recommends expanding pool space in order to defer more background work through larger debt bins. Overall\, our capacity planning tool provides a day/hour countdown for the next Data Unavailability/Data Loss (DU/DL) event\, accurately predicting DU/DL events to cover a future 12-hour time window.\nMoreover\, supported services such as data deduplication are becoming a common feature adopted in the data center\, especially as new storage technologies mature. Static partitioning of storage system resources\, memory caches\, may lead to missing SLOs\, such as the Data Reduction Rate (DRR) or IO latency. Lastly\, we propose a Content-Aware Learning Cache (CALC) that uses online reinforcement learning models (Q-Learning\, SARSA and Actor-Critic) to actively partition the storage system cache between a deduplicated data digest cache\, content cache\, and address-based data cache to improve cache hit performance\, while maximizing data reduction rates. Using traces from popular storage applications\, we show how our machine learning approach is robust and can out-perform an iterative search method for various data-sets and cache sizes. Our content-aware learning cache improves hit rates by 7.1% when compared to iterative search methods\, and 18.2\% when compared to traditional LRU-based data cache implementation.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-maher-kachmar/
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DTSTART;TZID=America/New_York:20210719T113000
DTEND;TZID=America/New_York:20210719T123000
DTSTAMP:20260405T065107
CREATED:20210713T172719Z
LAST-MODIFIED:20210713T172719Z
UID:26599-1626694200-1626697800@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Berkan Kadioglu
DESCRIPTION:PhD Dissertation Defense: An Analysis of Algorithms with Discrete Choice Models \nBerkan Kadioglu \nLocation: Zoom Link \nAbstract: In the first half of our work\, we consider a rank regression setting\, in which a dataset of $N$ samples with features in $\mathbb{R}^d$ is ranked by an oracle via $M$ pairwise comparisons.\nSpecifically\, there exists a latent total ordering of the samples; when presented with a pair of samples\, a noisy oracle identifies the one ranked higher w.r.t. the underlying total ordering.\nA learner observes a dataset of such comparisons\, and wishes to regress sample ranks from their features.\nWe show that to learn the model parameters with $\epsilon > 0$ accuracy\, it suffices to conduct $M \in \Omega(dN\log^3 N/\epsilon^2)$ comparisons uniformly at random when $N$ is $\Omega(d/\epsilon^2)$.\nCompared to learning from class labels\, learning from comparison labels has two advantages: First\, comparison labels reveal both inter and intra-class information\, where class labels only contain the former.\nSecond\, comparison labels also exhibit lower variability across different labelers.\nThis has been observed experimentally in multiple domains\, including medicine \citep{campbell2016plus\,kalpathy2016plus\, stewart2005absolute} and recommendation systems \citep{schultz2004learning\,zheng2009mining\,brun2010towards\, koren2011ordrec}\, and is due to the fact that humans often find it easier to make relative\, rather than absolute\, judgements.\nMany works focusing on empirically learning comparison labels show excellent performance in practice \citep{tian2019severity\,yildiz2019classification}.\nOur work provides a theoretical foundation for analyzing and understanding this empirical performance.\nMoreover\, we extend the problem we initially study to a harder setting.\nWe do this by moving from pairwise comparisons to multi-way comparisons.\nFurthermore\, we study an online variant of the previous problem where the goal is to maintain high user engagement throughout the learning period.\nThis of course\, indirectly leads to the goal of learning parameters of the discrete choice model as accurately as possible\, fast.\nThis new problem is directly related to a setting in which a retailer recommends products to customers.\nA common problem in many recommendation tasks is to simultaneously learn the utilities of items to be recommended and maintain high user engagement.\nWe are generally constrained by a limit on the total number of items to be recommended at a time for an unknown time horizon.\nRecently\, bandit algorithms have been proposed for this setting where the multinomial logit model is assumed.\nBounds on error metrics are provided for upper confidence and Thompson sampling based algorithms.\nIn our paper\, we propose a variational inference based Thompson sampling algorithm and identify the required properties to achieve $\tilde O(D^{3/2}\sqrt T)$ worst-case regret.\nThrough extensive experiments we show that our method performs much better than the recently proposed \emph{TSMNL} algorithm in many error metrics.\nWe further accelerate our algorithm to be used in practical settings.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-berkan-kadioglu/
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DTSTART;TZID=America/New_York:20210720T080000
DTEND;TZID=America/New_York:20210722T130000
DTSTAMP:20260405T065107
CREATED:20210621T200006Z
LAST-MODIFIED:20210621T200006Z
UID:26347-1626768000-1626958800@coe.northeastern.edu
SUMMARY:COE CommLab/Khoury College Writing Retreat
DESCRIPTION:College of Engineering PhD students are invited to join us for a writing retreat July 20 – 22.  The aim of this retreat is to create sustained writing time for researchers to work in a calm\, supportive environment on a longer project.  Studies have shown that an academic writing retreat supports productivity and progress while also encouraging helpful guidance from peers. \nOur virtual retreat is organized around alternating periods of quiet work on individual projects with collective sessions on topics related to research writing. Each of the three days begins with a welcome message and group gathering. On the last day\, we’ll wrap up the retreat with a virtual lunch to share concluding thoughts. \nRegister here for this event by June 24.
URL:https://coe.northeastern.edu/event/coe-commlab-khoury-college-writing-retreat/
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DTSTART;TZID=America/New_York:20210723T100000
DTEND;TZID=America/New_York:20210723T110000
DTSTAMP:20260405T065107
CREATED:20210706T205406Z
LAST-MODIFIED:20210706T205406Z
UID:26516-1627034400-1627038000@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Mahmoud Ibrahim
DESCRIPTION:PhD Dissertation Defense: Low-Power Integrated Circuit Design for Wireless Devices in the Internet of Things \nMahmoud Ibrahim \nLocation:  Zoom \nAbstract: Numerous integrated sensing devices are under development for wireless medical diagnostic and monitoring applications. However\, the data rates of wireless devices connected to the Internet of Things are limited and strongly depend on the available power. This research addresses the need for circuit-level design methods to enable higher data rates with lower power consumption in order to facilitate the proliferation of wireless devices that can overcome the speed-power conundrum. The potential applications include continuous-time monitoring of physiological signals\, where increased data rates imply the ability to exchange more information during the same time\, more accurate data\, and/or data from a greater number of sites associated with each wireless node.\nAn energy-efficient binary frequency shift keying (BFSK) transmitter architecture for biomedical applications is introduced as the first part of this dissertation research. To achieve low power consumption with higher data rates\, the novel transmitter architecture leverages image rejection techniques to generate each of the two tones of the transmitted BFSK signal while keeping the phase-locked loop (PLL) oscillator frequency unchanged\, and thus maintaining low PLL power and overall transmitter power. A fabricated prototype chip in 130nm complementary metal-oxide-semiconductor (CMOS) technology achieves data rates up to 10 Mbps while consuming 180 µW with up to -20 dBm output power according to Medical Implant Communication System (MICS) band requirements. The measurement results confirm state-of-the-art energy-efficient performance with 18 pJ/bit.\nAs a natural continuation of the first part of this research\, a complementary receiver architecture is described in the second part of this dissertation to provide full transceiver capabilities. The new receiver design approach takes advantage of the transmitted signal characteristics by using both the frequency information and phase information to demodulate the received digital bits. This design method results in improved sensitivity with reduced power consumption through relaxed receiver block specification requirements. The custom-designed receiver circuits include a new low-noise amplifier (LNA) topology for energy-efficient antenna impedance matching\, and a single mixer circuit that realizes the signal down-conversion with differential in-phase and quadrature-phase baseband output signals to circumvent the complexity associated with two mixers and to save power. Measurement results of the fabricated receiver in 65nm CMOS technology show a sensitivity of -82 dBm with an input signal at 10 Mbps centered around 416 MHz. With a power consumption of 610 µW and an energy efficiency of 61 pJ/bit\, this receiver architecture displays state-of-the-art performance with respect to data rate\, power and sensitivity compared to other receivers in the same frequency range.\nIn addition to the new transmitter and receiver architectures\, a large-signal transconductance linearization technique is presented as part of this dissertation research to extend the dynamic range of analog baseband filters. Furthermore\, a low-power sinusoidal signal generation technique is introduced and analyzed\, which is a versatile and essential component of the transmitter design approach.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-mahmoud-ibrahim/
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