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X-WR-CALDESC:Events for Northeastern University College of Engineering
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210410T080000
DTEND;TZID=America/New_York:20210410T190000
DTSTAMP:20260510T041916
CREATED:20210318T134623Z
LAST-MODIFIED:20210412T213010Z
UID:25074-1618041600-1618081200@coe.northeastern.edu
SUMMARY:Virtual Graduate School Open House
DESCRIPTION:Join us at the Virtual Graduate Open House that will take place on April 10\, 11 and 12. Learn more about your program of interest from faculty or learn more about services at Northeastern University that will enhance your graduate school experience.
URL:https://coe.northeastern.edu/event/virtual-graduate-open-house/2021-04-10/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210412T090000
DTEND;TZID=America/New_York:20210412T100000
DTSTAMP:20260510T041916
CREATED:20210311T202401Z
LAST-MODIFIED:20210311T202401Z
UID:24946-1618218000-1618221600@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Armin Moharrer
DESCRIPTION:PhD Dissertation Defense: Leveraging Structural Properties for Large-Scale Optimization \nArmin Moharrer \nLocation: Zoom Link \nAbstract: Large scale optimization problems abound in data mining\, machine learning\, and system design. We address the challenges posed by such large scale optimization problems by providing efficient optimization algorithms. The scope of studied problems is quite broad; it includes applications such as experimental design\, computing graph distances (dissimilarity scores)\, training auto-encoders\, multi-target regression\, and the design of cache networks. We leverage the structural properties present in these problems\, e.g.\, sparsity or separability. In particular\, we introduce some structural properties under which the Frank-Wolfe algorithm (FW) can be distributed over a cluster of computers. We show that the distributed FW running over 350 workers (CPUs) solves an instance of experimental design problem with 20M variables in 79 minutes\, while the serial implementation takes 48 hours. Furthermore\, we study a variant of FW for the design of cache networks. The problem is NP-hard\, but we achieve a $1-1/e$ approximation ratio\, by optimizing a non-convex relaxation via FW. We also propose a distributed Alternating Direction Method of Multipliers (ADMM) algorithm for computing graph distances. We observe speedups of 153 times when running over a cluster with 448 CPUs\, in comparison with running over 1 CPU\, for graphs with 2.4K nodes. Moreover\, we study applications of ADMM in solving robust variants of risk minimization problems; in these variants we replace the typically chosen mean squared error loss with a general lp norm. We combine model based optimization with ADMM to minimize the resulting non-smooth and non-convex objectives. We show that a stochastic variant of ADMM converges with the rate O(log T/T) and is highly efficient for optimizing the corresponding model functions.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-armin-moharrer/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210412T120000
DTEND;TZID=America/New_York:20210412T130000
DTSTAMP:20260510T041916
CREATED:20210309T213510Z
LAST-MODIFIED:20210309T213510Z
UID:24925-1618228800-1618232400@coe.northeastern.edu
SUMMARY:The Path to Climate Justice
DESCRIPTION:Join leading Northeastern faculty for a discussion on the important links between the climate crisis and social justice. Energy justice and climate researchers and activists Jennie Stephens\, Frances Roberts-Gregory\, and Brian Helmuth will lead a conversation about climate justice action and how to effectively connect knowledge to action during these disruptive times. \nRegistration
URL:https://coe.northeastern.edu/event/the-path-to-climate-justice/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210412T123000
DTEND;TZID=America/New_York:20210412T130000
DTSTAMP:20260510T041916
CREATED:20210331T135041Z
LAST-MODIFIED:20210331T135041Z
UID:25282-1618230600-1618232400@coe.northeastern.edu
SUMMARY:How To: Gain Creative Confidence
DESCRIPTION:Mohamed Kante\, E’12 Visionary & Chief Nerd iNERDE Inc. \n\n\n\n\n\n\nHave you ever wondered how to generate great ideas on demand? Are you looking to unleash your creative potential? This tutorial will explore some of the myths about creativity and innovation\, and guide you toward a higher level of thinking for your customers and users. By the end\, you’ll be equipped with tools and techniques for practicing outside-the-box problem-solving in our era of accelerating change. \nHosted by Northeastern Alumni Relations. Whether you identify as a seasoned entrepreneur or an entrepreneur in the making\, learn from thought leaders and idea generators on our Instagram page\, 30-minutes at a time.
URL:https://coe.northeastern.edu/event/how-to-gain-creative-confidence/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210412T150000
DTEND;TZID=America/New_York:20210412T160000
DTSTAMP:20260510T041916
CREATED:20210401T183643Z
LAST-MODIFIED:20210401T183643Z
UID:25296-1618239600-1618243200@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Neville Sun
DESCRIPTION:PhD Dissertation Defense: RF Magnetoelectric Devices for Communication\, Sensing\, and Power Electronics \nNeville Sun \nLocation: Zoom Link \nAbstract: A strong magnetoelectric (ME) coupling of layered magnetic/ferroelectric heterostructures can effectively convert energy between electric and magnetic fields. By utilizing strain mediated ME coupling\, it is possible to use an electric field to control magnetic film properties\, such as magnetization\, permeability\, and spin wave. Additionally\, an applied magnetic field can be used to control electric polarization. In this talk\, ME voltage tunable inductors and ME acoustically actuated mechanical antennas/sensors are demonstrated and analyzed with different heterostructure compositions and design considerations for improving device performance.\nThe first part examines a new class of voltage tunable magnetoelectric inductors with textured multiferroic cores consisting of a Metglas/piezoelectric laminate/Metglas composite for MHz adaptive power systems. These inductors demonstrate a large\, instantaneous\, and non-discrete tunable range with a wide operational frequency range from DC to 10 MHz. A tunable inductance range of up to 346% was achieved with an applied electric field of 24 kV/cm. However\, low voltage tunability is miniscule\, typically less than 6% at 30 V applied voltage. By optimizing the anisotropy of magnetoelastic stress\, a 50 um thick PMN-PT slab is shown to improve low voltage tuning by 6 times. These ME tunable inductors with low driving voltage provide adaptability for changing circuit conditions and are ideal for compact/lightweight power systems for electronic warfare and communication systems.\nThe second device of interest is a new MEMS ME antenna/sensor design based on the solidly mounted resonator (SMR) structure. The SMR replaces the freestanding membrane structure of a film-bulk acoustic resonator (FBAR) with a Bragg acoustic reflector for concentrated energy confinement while improving structural integrity and power handling. The antenna radiates using converse ME coupling physics while receiving and sensing EM waves by using direct ME coupling. A unique spin sprayed NiZn ferrite/AlN structure and performance characterization for arrayed resonators are presented. The acoustic resonance in the heterostructure films operates at UHF range for seamless on-chip integration with WiFi\, Bluetooth\, and GPS devices. The robust features of the sub-mm size SMR ME antenna are demonstrated in a miniature aerial drone communication system and provide a possible alternative for biomedical implantables for neurological studies.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-neville-sun/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210412T150000
DTEND;TZID=America/New_York:20210412T170000
DTSTAMP:20260510T041916
CREATED:20210412T145039Z
LAST-MODIFIED:20210412T145039Z
UID:25389-1618239600-1618246800@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Murphy Wonsick
DESCRIPTION:PhD Proposal Review: Improving Human Robot Interaction through Extended Reality Technologies \nMurphy Wonsick \nLocation: Teams Link \nAbstract: Recent advancements in robotics have allowed robots to become capable enough to be used in a wide variety of domains\, such as manufacturing\, search-and-rescue\, and space exploration. However\, human-robot interaction with these systems are still primarily achieved using 2D devices\, such and laptops\, tablets\, and/or game controllers despite operating in a 3D world. And although these interfaces can be very capable in operating a robot\, they are often complex and require expert operators as well as extensive training. Extended reality technologies provide an opportunity to create more intuitive human-robot interaction by allowing operators to visualize and interact with 3D data in a 3D environment\, allowing for a more natural interaction. Usage of extended reality technologies in human-robot interaction though are still very limited. In this proposal\, I aim to investigate how to provide better experiences for humans in human-robot interaction using extended reality technologies. Focus will be spent on using virtual reality headset to create supervisory control interfaces for remote robot operation and augmented reality head-mounted displays to help facilitate communication in human-robot shared workspaces. The goal of this work is to move towards more intuitive and easy-to-use interfaces for human-robot interaction.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-murphy-wonsick/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210414T120000
DTEND;TZID=America/New_York:20210414T130000
DTSTAMP:20260510T041916
CREATED:20210412T133846Z
LAST-MODIFIED:20210412T133846Z
UID:25382-1618401600-1618405200@coe.northeastern.edu
SUMMARY:ChE Seminar Series: Metal electrodes: the future of cost-effective storage of electrical energy
DESCRIPTION:ChE Seminar Series Presents: Dr. Lynden A. Archer \nLynden A. Archer\, Ph.D \nJoseph Silbert Dean of the College of Engineering and the James A Friend Family Distinguished Professor of Chemical and Biomolecular Engineering \nCornell University\, Ithaca NY \nMetal electrodes: the future of cost-effective storage of electrical energy \nAbstract\nThe levelized cost of electric power generated from renewable wind and solar resources have fallen\, continuously over the last decade. This trend is fueling optimism about humanity’s ability to achieve net-zero carbon emissions in the electric power generation and transportation sectors—without the large government subsides predicted as recently as a decade ago. It is known that the intermittency and seasonal variability of the electric power supply from wind and solar sources pose significant barriers to broad-based acceptance of clean electric power. Low-cost options for storing large quantities of renewable electric power would lower/eliminate these barriers and meet an unmet need in both the power generation and transportation sectors. Rechargeable electrochemical cells based on metallic anodes\, including lithium\, zinc\, and aluminum\, offer the potential for transformative advances in cost-effective storage of electrical energy. Such cells are under active development worldwide because they provide a path towards battery systems capable of meeting the performance and long-term storage requirements for truly dispatchable electric power generation from renewables. Recharge of any metal anode requires reversible electrodeposition/crystallization of metals; a process that is fundamentally unstable. This talk considers the stability limits for metal electrodeposition processes in liquid and semisolid structured electrolytes and\, on that basis\, proposes electrode and anode/electrolyte interphase design principles for enabling highly reversible storage solutions. The talk will also explore contemporary efforts to create minimal electrolytes and electrochemical interphases based on these principles and will discuss their effectiveness in enabling cost-effective energy storage systems with high levels of reversibility. \nBiography\nLynden Archer is the Joseph Silbert Dean of the College of Engineering and the James A Friend Family Distinguished Professor of Chemical and Biomolecular Engineering. His research focuses on the transport properties of polymers and polymer-nanoparticle hybrid materials\, and their applications for electrochemical energy storage. Archer received his Ph.D. in chemical engineering from Stanford University in 1993 and was a Postdoctoral Member of the Technical Staff at AT&T Bell Laboratories in 1994. He is a member of the National Academy of Engineering (NAE) and fellow of the American Physical Society (APS). His research contributions have been recognized with various awards\, including the AIChE Nanoscale Science and Engineering Forum award\, the National Science Foundation award for Special Creativity\, an NSF Distinguished Lectureship in Mathematical & Physical Sciences\, the American Institute of Chemical Engineer’s MAC Centeniell Engineer award\, and the Thompson-Reuters World’s Most Influential Scientific Minds in Materials Science for 2014 & 2015. At Cornell\, he has been recognized with the James & Mary Tien Excellence in Teaching Award and thrice with the Merrill Presidential award as the most influential member of the Cornell faculty selected by a Merrill Presidential Scholar.
URL:https://coe.northeastern.edu/event/che-seminar-series-metal-electrodes-the-future-of-cost-effective-storage-of-electrical-energy/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210414T170000
DTEND;TZID=America/New_York:20210414T180000
DTSTAMP:20260510T041916
CREATED:20210325T135223Z
LAST-MODIFIED:20210325T135223Z
UID:25217-1618419600-1618423200@coe.northeastern.edu
SUMMARY:Global Co-op Self Developing Info Session
DESCRIPTION:Join the College of Engineering Global Co-op team in learning about self-developing a global co-op opportunity for Summer II/ Fall 2021. This session will be interactive and the topics discussed will include: \n\nSearch techniques and global positions in your field\nWhat to consider when interested in a global co-op\nStep by step information for networking and self-developing\n\nRSVP via NUworks Events Calendar for Zoom link. \nPlease reach out to Sally Conant\, Global Co-op Coordinator\, s.conant@northeastern.edu or Kristina Kutsukos\, Global Co-op Coordinator\, k.kutsukos@northeastern.edu for additional information.
URL:https://coe.northeastern.edu/event/global-co-op-self-developing-info-session/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210415T180000
DTEND;TZID=America/New_York:20210415T193000
DTSTAMP:20260510T041916
CREATED:20210322T140023Z
LAST-MODIFIED:20210322T140023Z
UID:25152-1618509600-1618515000@coe.northeastern.edu
SUMMARY:Machine Learning with MATLAB Webinar
DESCRIPTION:Date:  Thursday\, April 15th\nTime:  6:00pm – 7:30pm\, including Q&A \n Register Now \nEngineers and data scientists work with large amounts of data in various formats such as sensor\, image\, video\, telemetry\, databases\, and more. They use machine learning to find patterns in data and build models that predict future outcomes based on historical data.\nIn this session\, we explore the fundamentals of machine learning using MATLAB. We introduce machine learning techniques in MATLAB to quickly explore your data\, evaluate machine learning algorithms\, compare the results and apply the best technique to your problem. \nHighlights include: \n\nTraining\, evaluating\, and comparing a range of machine learning models\nUsing refinement and reduction techniques to create models that best capture the predictive power of your data\nRunning predictive models in parallel using multiple processors to expedite your results\nDeploying your models to production in a variety of formats
URL:https://coe.northeastern.edu/event/machine-learning-with-matlab-webinar/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210415T180000
DTEND;TZID=America/New_York:20210415T200000
DTSTAMP:20260510T041916
CREATED:20210331T171121Z
LAST-MODIFIED:20210331T171121Z
UID:25284-1618509600-1618516800@coe.northeastern.edu
SUMMARY:ADSE Trivia Night
DESCRIPTION:Join ADSE for a graduate student virtual trivia night on April 15th from 6:00-8:00 pm and help us support a local Black-owned business! Form a team of up to 4 members and answer a total of 30 questions on variable topics. \n1st place: $25 to each team member\n2nd place: $20 to each team member\n3rd place: $15 to each team member \nThe first 25 people to register that attend the event will also receive a $5 gift card from Delectable Desires\, a highly acclaimed Black-Owned pastry shop in Roxbury.
URL:https://coe.northeastern.edu/event/adse-trivia-night-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210416T093000
DTEND;TZID=America/New_York:20210416T103000
DTSTAMP:20260510T041916
CREATED:20210412T145721Z
LAST-MODIFIED:20210412T145721Z
UID:25398-1618565400-1618569000@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Shanchuan Liang
DESCRIPTION:MS Thesis Defense: Design and Characterization of Flexible Neural Interface Connector for Large-scale Neuronal Recording \nShanchuan Liang \nLocation: Zoom Link \nAbstract: With the increasing demand of the electrically active implantable devices for studying neuroscience\, microelectrode arrays (MEAs) have been widely developed to measure extracellular neuronal activity. Multiple channels MEAs with electrodes embedded are designed to allow coupling time-resolved data simultaneously. In this process\, a well-designed PCB is also essential which use as a bridge to connect MEAs and back-end data acquisition system. This work developed an up to 256-channel flexible neural interface connector for neural signal recording. This thesis aims to introduce the detailed design and implementation procedures of the neural interface connector which consists of MEA\, PCB and amplifier. Considering the contact physics of the connector\, a contact model was established by using COMSOL to address the contact zone and figure out the displacement and pressure on the layer MEAs embedded. The simulation results were used for characterization and optimizing. Robustness tests reveal that the connector is stable up to 500 cycles with high yield. The following in vivo recordings by installed the device on mouse brain validate its excellent performance of recordings of spontaneous single-unit activity of neurons in which spikes in neurons were captured after signal processing.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-shanchuan-liang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210416T120000
DTEND;TZID=America/New_York:20210416T130000
DTSTAMP:20260510T041916
CREATED:20210415T152227Z
LAST-MODIFIED:20210415T152227Z
UID:25460-1618574400-1618578000@coe.northeastern.edu
SUMMARY:Che Seminar Series: Creating Inclusive Spaces in the Curriculum to Improve the Classroom Climate
DESCRIPTION:ChE Seminar Series Presents: Dr. Matthew Lee \nMatthew Lee\, PhD \nTeaching Professor of Human Services \nNortheastern University \nCreating Inclusive Spaces in the Curriculum to Improve the Classroom Climate \nAbstract: In this Distinguished Lecture\, Professor Matthew Lee\, PhD\, from the Human Services Program at Northeastern\, will discuss his life\, career\, and lifelong commitment to equity and diversity for college students. Drawing on his years of experience engaged in intergroup dialogue\, research\, teaching study abroad\, and anti-racist training\, Dr. Lee will describe some lessons for attendees to consider in developing a more inclusive curriculum and climate. Question & answer period to take place during the session. \nBio: Dr. Matthew Lee received his PhD in Clinical and Community Psychology from the University of Illinois at Urbana-Champaign. He has taught courses in counseling theory and practice\, cross-cultural psychology\, ethnic identity and conflict (in Romania\, Germany\, Poland\, and Croatia)\, intro to psychology\, lifespan development\, developmental psychology\, race and empowerment\, Asian American identity\, psychology and literature\, and senior capstone. \nHis research examines campus climate and advocacy for diversity/inclusion in the classroom\, and Asian American mental health as it relates to experiences of microaggressions that may be associated with phenotype or socialization.
URL:https://coe.northeastern.edu/event/che-seminar-series-creating-inclusive-spaces-in-the-curriculum-to-improve-the-classroom-climate/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210416T130000
DTEND;TZID=America/New_York:20210416T140000
DTSTAMP:20260510T041916
CREATED:20210413T205310Z
LAST-MODIFIED:20210413T205310Z
UID:25432-1618578000-1618581600@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Kai Li
DESCRIPTION:PhD Dissertation Defense: Robust Visual Learning with Limited Labels \nKai Li \nLocation: Zoom Link \nAbstract: The recent flourish of deep learning in various tasks is largely credited to the rich and accessible labeled data. Nonetheless\, massive supervision remains a luxury for many real-world applications: It is costly and time-consuming to collect and annotate a large amount of training data. Sometimes it is even infeasible to get large training datasets because for certain tasks only a few or even no examples are available\, or annotating requires expert knowledge.\nIn this dissertation research\, I investigate techniques systematically addressing the problem of learning with limited labels from the following three aspects. The first aspect is learning to generalize from limited label supervision. I develop few-shot learning algorithms that perform data augmentation in the feature space and that generate task-specific networks based on the limited supervision provided. The second aspect is learning to reuse label supervision from a relevant but different task. I propose domain adaptation algorithms that adapt label supervision from a richly-labeled source domain to a scarcely-labeled target domain with consistency learning\, data augmentation and adversarial learning. The last aspect is learning representations without label supervision. I develop algorithms that learn semantic-rich representations that allow to reliably establish relations among high-dimensional data. This is achieved by explicitly modeling the intrinsic relationship among data points during the representation learning process. \n 
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-kai-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210416T133000
DTEND;TZID=America/New_York:20210416T170000
DTSTAMP:20260510T041916
CREATED:20210420T135431Z
LAST-MODIFIED:20210420T135431Z
UID:25479-1618579800-1618592400@coe.northeastern.edu
SUMMARY:Friday Fun Lab
DESCRIPTION:First-year engineering students showcase their success at Northeastern’s Friday Fun Lab! Play interactive electronic games that were designed and built by the Cornerstone of Engineering students. All games are designed with COVID safety protocol in mind\, so stop by and check out these amazing projects today!
URL:https://coe.northeastern.edu/event/friday-fun-lab/
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:20210416T153000
DTEND;TZID=America/New_York:20210416T163000
DTSTAMP:20260510T041916
CREATED:20210412T145358Z
LAST-MODIFIED:20210412T145358Z
UID:25394-1618587000-1618590600@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Mehrshad Zandigohar
DESCRIPTION:MS Thesis Defense: Real-Time Grasp Type Estimation for a Robotic Prosthetic Hand \nMehrshad Zandigohar \nLocation: Zoom Link \nAbstract: For lower arm amputees\, prosthetic hands promise to restore most of physical interaction capabilities. This requires to accurately predict hand gestures capable of grabbing varying objects and execute them timely as intended by the user. Current approaches often rely on physiological signal inputs such as Electromyography (EMG) signal from residual limb muscles to infer the intended motion. However\, limited signal quality\, user diversity and high variability adversely affect the system robustness.\nInstead of solely relying on EMG signals\, our work enables augmenting EMG intent inference with physical state probability through machine learning and computer vision method.\nTo this end\, we: (i) study state-of-the-art deep neural network architectures to select a performant sources of knowledge transfer for the prosthetic hand; (ii) use a dataset containing object images and probability distribution of grasp types as a new form of labeling where instead of using absolute values of zero and one as the conventional classification labels\, our labels are a set of probabilities whose sum is 1. The proposed method generates probabilistic predictions which could be fused with EMG prediction of probabilities over grasps by using the visual information from the palm camera of a prosthetic hand.\nMoreover\, As robotic prosthetic hands are targeted for amputees with the goal of assisting them for their daily life activities\, it is crucial to have a portable and reliable system. Although embedded devices employed in such systems\, provide portability and comfort for the end user\, their limited computational resources comparing to a desktop or server computer impose longer latencies when executing such applications\, making them unreliable and generally impractical to use. Therefore\, it is critical to optimize the aforementioned applications especially DNNs to meet the specified deadline\, resulting in a real-time system. Therefore\, for real-time execution of grasp estimation we propose: (iii) the concept of layer removal as a means of constructing TRimmed Networks (TRNs) that are based on removing problem-specific features of a pretrained network used in transfer learning\, and (iv) NetCut\, a methodology based on an empirical or an analytical latency estimator\, which only proposes and retrains TRNs that can meet the application’s deadline\, hence reducing the exploration time significantly. We demonstrate that TRNs can expand the Pareto frontier that trades off latency and accuracy to provide networks that can meet arbitrary deadlines with potential accuracy improvement over off-the-shelf networks. Our experimental results show that such utilization of TRNs\, while transferring to a simpler dataset\, in combination with NetCut\, can lead to the proposal of networks that can achieve relative accuracy improvement of up to 10.43\% among existing off-the-shelf neural architectures while meeting a specific deadline\, and 27x speedup in exploration time.\nThe proposed methods in this work enable robust and realistic prediction of the grasp type as well as real-time execution of the detection pipeline\, resulting in the improved overall satisfaction of the targeted population.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-mehrshad-zandigohar/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210419T090000
DTEND;TZID=America/New_York:20210419T100000
DTSTAMP:20260510T041916
CREATED:20210412T145223Z
LAST-MODIFIED:20210412T145223Z
UID:25392-1618822800-1618826400@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Ahmet Oner
DESCRIPTION:PhD Dissertation Defense: Improving the Resilience of the Power Grid \nAhmet Oner \nLocation: Teams Meeting \nAbstract: The power grid constitutes one of the most critical infrastructures that have significant interdependencies with various others such as communication\, transportation\, emergency\, and health-care delivery systems. A disruption in the operation of the power grid may affect the operation of all others in an undesirable manner. Therefore\, improving the resiliency of power grids can also help increase the resiliency of other critical infrastructures. This dissertation presents methods to improve the resiliency of power grids against extreme events and/or system changes. \nFirst\, generation dispatch\, adaptable load shedding strategy\, and pro-active line switching are combined in order to maximize the resiliency of the overall power grid against extreme events. The moving event is monitored\, and the control actions are adjusted accordingly to improve the resilience under changing conditions affected by the natural disaster during its active period. Then\, that study is further extended and made it robust against voltage instability. The details of the methodology and its implementation are presented. \nTo reduce the probability of voltage problems and line flow limit violations\, and to improve power quality\, distributed generators (DG) are placed strategically ahead of the event using outage forecasts based on historical outage data. Therefore\, a possible set of outage scenarios is considered\, and a minimum number of required DG placements are determined to maintain system feasibility for all considered scenarios. \nLastly\, reactive power sources are placed to solve the voltage instability problems\, which are caused by the lack of reactive power in the system. The computational burden of optimal placement problem presents a practical limitation for applying it to very large scale systems considering multi-contingency cases. This part presents a practical and easily implementable solution that will address this limitation.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-ahmet-oner/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210419T150000
DTEND;TZID=America/New_York:20210419T160000
DTSTAMP:20260510T041916
CREATED:20210420T140007Z
LAST-MODIFIED:20210420T140007Z
UID:25489-1618844400-1618848000@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Kaier Liang
DESCRIPTION:MS Thesis Defense: Rough-Terrain Locomotion and Unilateral Contact Force Regulations With a Multi-Modal Legged Robot \nKaier Liang \nLocation: Zoom Link \nAbstract: The study for legged locomotion has made lots of achievements. However\, the stability of the state-of-the-art bipedal robots are still vulnerable to external perturbation\, cannot negotiate extreme rough terrains\, and cannot directly regulate unilateral contact force.\nThis thesis will introduce a thruster-assisted bipedal walking robot called Harpy. The objective is to integrate the merits of legged and aerial robots in a single platform. The robot’s dynamics is simulated with simplifying assumptions. Furthermore\, this research will show that the employment of thruster allows to stabilize the robot’s frontal dynamics and apply model predictive control (MPC) to jump over obstacles to achieve multi-modal locomotion. In addition\, we will capitalize the thruster actions to demonstrate an optimization-free approach by regulating contact forces using an Explicit Reference Governor (ERG). Then\, we will focus on ERG-based fine-tuning of the joint’s desired trajectories to satisfy unilateral contact force constraints.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-kaier-liang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210419T173000
DTEND;TZID=America/New_York:20210419T183000
DTSTAMP:20260510T041916
CREATED:20210303T144634Z
LAST-MODIFIED:20210303T144634Z
UID:24860-1618853400-1618857000@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Ilkay Yildiz
DESCRIPTION:PhD Dissertation Defense: Spectral Ranking Regression \nIlkay Yildiz \nLocation: Zoom Link \nAbstract: We consider learning from ranking labels generated as follows: given a query set of samples in a dataset\, a labeler ranks the samples w.r.t.~her preference. Such ranking labels scale exponentially with query set size; most importantly\, in practice\, they often exhibit lower variance compared to class labels. \nWe propose a new neural network architecture based on siamese networks to incorporate both class and comparison labels\, i.e.\, rankings of sample pairs\, in the same training pipeline using Bradley-Terry and Thurstone loss functions. Our architecture leads to a significant improvement in predicting both class and comparison labels\, increasing classification AUC by as much as 35% and comparison AUC by as much as 6% on several real-life datasets. We further show that\, by incorporating comparisons\, training from few samples becomes possible: a deep neural network of 5.9 million parameters trained on 80 images attains a 0.92 AUC when incorporating comparisons. \nFurthermore\, we tackle the problem of accelerating learning over the exponential number of rankings. We consider a ranking regression problem in which we learn Plackett-Luce scores as functions of sample features. We solve the maximum likelihood estimation problem by using the Alternating Directions Method of Multipliers (ADMM)\, effectively separating the learning of scores and model parameters. This separation allows us to express scores as the stationary distribution of a continuous-time Markov Chain. Using this equivalence\, we propose two spectral algorithms for ranking regression that learn shallow regression model parameters up to 579 times faster than the Newton’s method. \nFinally\, we bridge the gap between deep neural networks (DNNs) and efficient spectral algorithms that regress rankings under the Plackett-Luce model. We again solve the ranking regression problem using ADMM\, and thus\, express scores as the stationary distribution of a Markov chain. Moreover\, we replace the standard l_2-norm proximal penalty of ADMM with Kullback-Leibler (KL) divergence. This is a more suitable distance metric for Plackett-Luce scores\, which form a probability distribution\, and significantly improves prediction performance. Our resulting spectral algorithm is up to 175 times faster than siamese networks over four real-life datasets comprising ranking observations. At the same time\, it consistently attains equivalent or better prediction performance than siamese networks\, by up to 26% higher Top-1 Accuracy and 6% higher Kendall-Tau correlation.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-ilkay-yildiz/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T100000
DTEND;TZID=America/New_York:20210420T110000
DTSTAMP:20260510T041916
CREATED:20210420T140838Z
LAST-MODIFIED:20210420T140838Z
UID:25503-1618912800-1618916400@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Danton Zhao
DESCRIPTION:MS Thesis Defense: LiDAR with a Silicon Photomultiplier for Applications in Adverse Weather \nDanton Zhao \nLocation: Zoom Link \nAbstract: As Light Detection and Ranging (LiDAR) integration becomes more widespread in the field of remote sensing for autonomous navigation\, the impact of degraded visual environments will quickly need to be addressed. The particles responsible for the degradation not only reduce the reflected signal from targets of interest but can also trigger false returns given sufficient density. Of particular interest for solutions to this problem are Geiger-mode avalanche photodiodes\, as these detectors provide high photon sensitivity and high time accuracy with a caveat. In this thesis\, I will be discussing the work that I have done in modeling and addressing artifacts that were generated in the data as a result of using Geiger-mode detectors.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-danton-zhao/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T110000
DTEND;TZID=America/New_York:20210420T120000
DTSTAMP:20260510T041916
CREATED:20210412T144906Z
LAST-MODIFIED:20210412T144906Z
UID:25386-1618916400-1618920000@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Yize Li
DESCRIPTION:MS Thesis Defense: Supervised Classification on Deep Neural Network Attack Toolchains \nYize Li \nLocation: Zoom Link \nAbstract: Deep learning\, while an important machine learning technique\, is susceptible to adversarial example attacks. Adversarial examples generated by adding perturbations on clean images/video frames can lead to mis-predictions of deep neural networks. Moreover\, deep learning/machine learning can be used to deceive humans by generating adversarial falsified media e.g.\, deepfake attacks. The thesis work will study the above two attack scenarios\, i.e.\, machine-centric adversary and human-centric adversary\, with targets to fool ML decisions and human decisions\, respectively. We aim to build a generalizable and scalable supervised learning system for classifying attack attributes behind the machine-centric attacks as well as the human-centric attacks. We start from building an integrated Attack Toolchain Library (ATL) with a broad coverage of both machine-centric and human-centric adversaries\, as well as through an integrated user interface for great flexibility and extensibility to serve our downstream tasks. Based on the developed ATL\, we further design a meta-classifier pipeline architecture for predicting attack attributes. The proposed overall meta-classifier shows effectiveness in dealing with false alarms and data distribution shift\, and generalization to both machine-centric and human-centric attacks.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-yize-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T120000
DTEND;TZID=America/New_York:20210420T130000
DTSTAMP:20260510T041916
CREATED:20210414T173115Z
LAST-MODIFIED:20210414T173115Z
UID:25445-1618920000-1618923600@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Ashutosh Singh
DESCRIPTION:MS Thesis Defense: Variation is the Norm: Brain State Dynamics Evoked By Emotional Video Clips \nAshutosh Singh \nLocation: Zoom Link \nAbstract: Past affective neuroscience studies have attempted to identify a “biomarker” or consistent pattern of brain activity (as measured externally using\, for instance\, fMRI) to indicate the presence of a single pre-defined category of emotion (e.g.\, fear) that remains consistent throughout all instances of that category for an individual across contexts and even across individuals. In this thesis\, we investigated variation rather than consistency during emotional experiences. Using fMRI data acquired while individuals watched affect-invoking video clips that have been normed for their evoked emotion categories in prior population studies. Towards this end\, we developed a probabilistic model of the temporal dynamics associated with the hypothetical affect-related brain states\, fitted to the measured brain activity of the participants. We characterized brain states traversed while individuals watched these clips as distinct state occupancy periods between state transitions\, inferred by blood oxygen level-dependent (BOLD) signal patterns captured in fMRI measurements. We found substantial variability in the state occupancy probability distributions across individuals watching the same video\, hence supporting the hypothesis that when it comes to the brain correlates of emotional experience\, variation may indeed be the norm. Studying the mean activation pattern associated with each state\, as well as covariance (in the Gaussian conditional measurement model we assumed)\, we further improve our understanding of the variability between instances of these brain states. Additionally\, we analyzed the presence of potential clusters of brain state trajectories among participants who showed less divergence in their response to each of these videos and checked for their consistency throughout all the video clips.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-ashutosh-singh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T133000
DTEND;TZID=America/New_York:20210420T143000
DTSTAMP:20260510T041916
CREATED:20210420T135838Z
LAST-MODIFIED:20210420T135838Z
UID:25487-1618925400-1618929000@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Peng Chang
DESCRIPTION:PhD Dissertation Defense: Model-Based Manipulation of Linear Flexible Objects \nPeng Chang \nLocation: Teams Meetings \nAbstract: Manipulation of deformable objects plays an important role in various scenarios such as manufacturing\, service\, healthcare\, and security. Linear flexible objects such as cables\, wires\, and ropes are common in these scenarios. However\, the high dimensionality of the linear flexible objects brings challenges to the modeling and planning in manipulation tasks\, and automatic manipulation of these objects is computationally expensive due to their infinite degrees of freedom in the free spaces. In this dissertation\, we investigate model-based manipulation of linear flexible objects such as cables. We contribute to different models including geometrical and physical models to represent the linear flexible objects. With these models\, we then develop manipulation plans and strategies to achieve the automation of the linear flexible object manipulation tasks in both simulation and real-world. Besides\, we also investigate human-robot collaboration to complete a sample assembly task involving linear flexible object manipulation.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-peng-chang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T140000
DTEND;TZID=America/New_York:20210420T150000
DTSTAMP:20260510T041916
CREATED:20210420T135556Z
LAST-MODIFIED:20210420T135556Z
UID:25482-1618927200-1618930800@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Griffin Knipe
DESCRIPTION:MS Thesis Defense: Unifying Performance and Security Evaluation for Microarchitecture Design Exploration \nGriffin Knipe \nLocation: Zoom Link \nAbstract: Computer architects develop microarchitectural features that boost instruction-level parallelism to improve CPU performance. While performance may be improved\, adding new features increases the CPU’s design complexity. This further compounds the effort required to complete design verification. Trustworthy design verification is paramount to microarchitecture design\, as silicon chips cannot easily be patched in the field.\nDespite the best efforts for security verification\, researchers have created transient execution side-channel attacks which can exploit microarchitecture performance features to leak data across ISA-prescribed security boundaries. This motivates the unification of performance evaluation and security verification techniques to ensure that new microarchitectural features are understood from multiple design perspectives.\nThis thesis presents Yori\, a RISC-V microarchitecture simulator that aims to enable computer architects to evaluate microarchitecture performance and security using a single framework. As Yori is a work-in-progress\, this thesis presents the work-to-date\, focusing on a detailed model of the reference microarchitecture and evaluation of the current model accuracy. We describe a viable methodology to interface between the Yori simulator and an existing security verification tool. We conclude the thesis\, laying out a plan to complete this marriage of performance and security.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-griffin-knipe/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T160000
DTEND;TZID=America/New_York:20210420T170000
DTSTAMP:20260510T041916
CREATED:20210414T173500Z
LAST-MODIFIED:20210414T173500Z
UID:25439-1618934400-1618938000@coe.northeastern.edu
SUMMARY:MS Thesis Defense: Hao Chen
DESCRIPTION:MS Thesis Defense: Reconstruction of Sulcal Geometry in Brain Stimulation Models using Spherical Harmonics \nHao Chen \nLocation: Zoom Link \nAbstract: Over the past few years\, there has been increasing interest in transcranial electrical stimulation (tCS) and thus it has been the subject of a growing number of simulation studies. Indeed\, some federal agencies in the US now require model-based simulations to be included as part of tCS grant proposal. In order to obtain more accurate simulation results and guide the relevant research\, it is of important to assess the impact of the accuracy of the anatomical 3D brain model that these studies depend on. However\, due to the partial volume problem\, many 3D reconstruction results based on MR images are inaccurate with respect to the details of the geometry of the sulci. Specifically\, when the sulci are on the scale of\, or even smaller than\, the voxel resolution of the MRI\, these models generally really in a binary approximation\, either making the sulcus wider in the model than in reality or eliminating it altogether. In this thesis\, we describe a method for modeling the 3D reconstruction of the brain that may facilitate controlled study of the effect of these approximations. The general approach is to model the brain surface using a spherical harmonic expansion\, then modify the expansion coefficients in an attempt to selectively and smoothly control sulcal width. In the first part of the thesis\, we describe and evaluate an approach in which we experimentally selected two groups of spherical harmonic coefficients within a specified range that could simultaneously affect a chosen sample of the gyri. For the coefficients in the first group\, the widths of all gyri in the sample were increased by enlarging the corresponding coefficients for each spherical harmonic. Conversely\, for each coefficient in the second group\, this adjustment caused the widths of the sampled gyri to decrease simultaneously. We evaluated the method by alternately increasing / decreasing the coefficients in the first group\, and decreasing / increasing those in the second\, by a chosen range of factors\, and observing the effects on the model cortical surface. Experimental results showed that the widths of most of the sulci and gyri were simultaneously adjusted according to the desired effect.\nIn the second part of the thesis\, we tried to build a volume mesh starting from the modified spherical harmonic surfaces. It turned out that this problem was particularly challenging because most of the surface models in our study had self-intersection points. We used a well-known software package for mesh processing\, iso2mesh\, to successfully remove the self-intersection points on all surfaces were removed finally\, but this process seemed to create small holes in the surfaces of the models. Despite these holes\, with a few exceptions\, the widths of most sulci (gyri) were still simultaneously increased (decreased) with the coefficient adjustments. This result provides a direction for further study towards controlled study of the influence of the partial volume problem on modeling of tCS.
URL:https://coe.northeastern.edu/event/ms-thesis-defense-hao-chen/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210420T173000
DTEND;TZID=America/New_York:20210420T183000
DTSTAMP:20260510T041916
CREATED:20210329T173703Z
LAST-MODIFIED:20210329T173812Z
UID:25249-1618939800-1618943400@coe.northeastern.edu
SUMMARY:Making a Graduate School Plan
DESCRIPTION:In this intensive workshop\, we’ll walk through the steps of identifying a right fit graduate program and help you plan out the who\, what\, when\, where\, and how of applying to graduate schools. Bring a laptop if you have one\, a notebook\, and come prepared with some thoughts about what’s next! If you can’t make it\, contact URF@Northeastern.edu to access our Canvas workshop site (graduating seniors only or alumni only please). \nRegistration
URL:https://coe.northeastern.edu/event/making-a-graduate-school-plan/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210421T090000
DTEND;TZID=America/New_York:20210421T100000
DTSTAMP:20260510T041916
CREATED:20210420T135730Z
LAST-MODIFIED:20210420T135730Z
UID:25484-1618995600-1618999200@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Rubens Lacouture
DESCRIPTION:MS Thesis Defense: GPUBLQMR: GPU-Accelerated Sparse Block Quasi-Minimum Residual Linear Solver \nRubens Lacouture \nLocation: Zoom Link \nAbstract: Solutions of linear systems of equations is the central point of many scientific and engineering research problems across a variety of domains. In many cases\, the solution of linear systems can even take most of the simulation time which presents a huge computational bottleneck issue. This can hinder the scalability of various scientific software hindering for larger problems. For large-scale simulations\, this can result in having to find the solutions of millions of unknowns\, making this an ideal problem to exploit parallelism to improve performance.\nPreconditioned Krylov subspace methods have proven effective and robust in various applications. The block Quasi-Minimum Residual (BLQMR) method as developed by Boyse et al. has been shown to be efficient for solving systems of equations with multiple righthand sides. This method is based on the conventional Quasi-Minimum Residual (QMR) method which is generalized using the block Lanczos algorithm to solve multiple solutions simultaneously. In particular\, it is shown that this method accelerates the convergence behavior based on the set number of righthand sides\, grouped to be solved simultaneously. Block iterative solver methods are often characterized by a high degree of parallelism.\nIn this thesis\, we show how BLQMR can be successfully implemented on a distributed memory computer taking advantage of Graphics Processing Units (GPU) accelerators. We leveraged the processing power of GPUs to show how the proposed GPU-accelerated BLQMR approach can out-perform state-of-the-art linear solvers and results in an ideal behavior for solving challenging linear algebra problems through data from various numerical experiments. The library code developed in this work is written using the CUDA framework. The performance of the parallel algorithm is optimized using several CUDA optimization strategies and the speedup of the parallel GPU implementation over the existing sequential CPU implementations is reported.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-rubens-lacouture/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210421T100000
DTEND;TZID=America/New_York:20210421T110000
DTSTAMP:20260510T041916
CREATED:20210420T140528Z
LAST-MODIFIED:20210420T140528Z
UID:25499-1618999200-1619002800@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Matin Raayai Ardakani
DESCRIPTION:MS Thesis Defense: A Framework for Denoising Two and Three-dimensional Monte CarloPhoton Transport Simulations Using Convolutional Neural Networks \nMatin Raayai Ardakani \nLocation: Zoom Link \nAbstract: The Monte Carlo (MC) method is considered to be the gold standard for modeling light propagation inside turbid media\, proving superior to other Radiative Transfer Equation (RTE) solvers relying on variational principles. However\, like most MC-based algorithm\, a large number of independently launched photons is needed for converging to the correct result and combating its inherent stochastic noise\, yielding longer computation times\, even when accelerated on GraphicProcessing Units (GPUs).\nTo remove this noise from the output without increasing the number of photons used for simulation\, modified versions of commonly used filters for image and volumetric data based on non-local self similarity has been used in the past. Current state-of-the-art denoising approaches rely on Convolutional Neural Networks (CNN) to remove spatially variant noise\, but the high dynamic range of MC simulations has hindered their adaptation to remove MC noise.\nIn this thesis\, we address this problem by presenting a supervised framework for using CNNs to denoise MC simulations. First\, a dataset is created with each entry comprising of a unique configuration simulated with different numbers of photons. The simulation configurations are generated using a simple generative model that introduces objects with both smooth and sharp edges into the volume. By selecting the group of fluence maps simulated with the maximum number of photons in the dataset as labels\, we train a range of CNN-based models to learn the underlying mapping between noisy and clean images. The CNN input is converted to log scale and normalized to reduce the high dynamic range\, and converted back after inference. The trained CNNs are then shown to have better performance compared to using an Adaptive Non-local Means filter\, in terms of mean square error (MSE)\, structural similarity index (SSIM)\, and peak signal-to-noise ratio (PSNR) in the image domain.\nFinally\, we purpose our own architecture that combines DnCNN and UNet\, a strategy that can learn both local and global residual noise maps\, achieving state-of-the-art performance compared to existing CNN methods. Future avenues of research and challenges for denoising 3D simulations are also discussed.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-matin-raayai-ardakani/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210421T100000
DTEND;TZID=America/New_York:20210421T170000
DTSTAMP:20260510T041916
CREATED:20210406T170701Z
LAST-MODIFIED:20210406T170701Z
UID:25342-1618999200-1619024400@coe.northeastern.edu
SUMMARY:MS Thesis Defense: Yuezhou Liu
DESCRIPTION:MS Thesis Defense: Optimizations of Caching Networks: Fairness and Application to Mobile Networks \nYuezhou Liu \nLocation: Zoom Link \nAbstract: In-network caching is playing a more and more important role in today’s network architectures\, because of the explosive growth of data traffic due to the proliferation of mobile devices and demands for high-volume media content\, as well as the development of low-latency applications\, such as VR/AR and cloud gaming. The replication of popular contents in the caches that located closer to end users than central servers\, can significantly reduce backbone traffic\, benefit request latency\, and balance the load of central servers. In this thesis\, we study two problems in the field of network caching. In the first part\, we consider fair caching policies in caching networks with arbitrary topology. We introduce a utility maximization framework to find a caching decision that reduces aggregate expected request routing cost in the network while taking fairness issues into consideration. The utility maximization problem is NP-hard\, and we propose two efficient approximation algorithms to solve it. In the second part\, we study how caching may affect user association in mobile networks. We jointly optimize the user association decision and caching at both base stations (BSs) and gateways (GWs). The resulting problem is also NP-hard. We propose a polynomial-time algorithm based on concave approximation and pipage rounding that produces a solution within a constant factor of 1-1/e from the optimal. Simulation results show that the proposed algorithm outperforms schemes that combine cache-independent user association methods with traditional caching strategies (e.g.\, LRU) in terms of minimizing the aggregate expected routing cost and backhaul traffic while achieving a high data sum rate in the access network.
URL:https://coe.northeastern.edu/event/ms-thesis-defense-yuezhou-liu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210421T120000
DTEND;TZID=America/New_York:20210421T130000
DTSTAMP:20260510T041916
CREATED:20210420T140408Z
LAST-MODIFIED:20210420T140408Z
UID:25497-1619006400-1619010000@coe.northeastern.edu
SUMMARY:ChE Seminar Series: Biomaterials to unlock the regenerative capacity of tissues
DESCRIPTION:ChE Seminar Series Presets: Dr. Tatiana Segura \nTatiana Segura\, PhD \nProfessor of Biomedical Engineering\, Duke University \nBiomaterials to unlock the regenerative capacity of tissues \nAbstract: Injectable materials that can conform to the shape of a desired space are used in a variety of fields including medicine. The ability to fill a tissue defect with an injectable material can be used for example to deliver drugs\, augment tissue volume\, or promote repair of an injury. This talk will explore the development of injectable materials that are based on assembled particle building blocks\, for tissue repair. We find that using microparticle building blocks to build the scaffold generates a porous network by the space left behind between adjacent building blocks. Due to the injectability of this microporous material we have explored its wide applicability to tissue repair applications ranging from skin to brain wounds. In this talk\, I will describe how MAP scaffolds can modulate the wound healing immune response and lead to regenerative wound healing. \nBiography: Professor Tatiana Segura received her BS degree in Bioengineering from the University of California Berkeley and her doctorate in Chemical Engineering from Northwestern University. Her graduate work in designing and understanding non-viral gene delivery from hydrogel scaffolds was supervised by Prof. Lonnie Shea. She pursued post-doctoral training at the Swiss Federal Institute of Technology\, Lausanne under the guidance of Prof. Jeffrey Hubbell\, where her focus was self-assembled polymer systems for gene and drug delivery. Professor Segura’s Laboratory studies the use of materials for minimally invasive in situ tissue repair. On this topic\, she has published 113 peered reviewed publications to date. She has been recognized with the Outstanding Young Investigator Award from the American Society of Gene and Cell Therapy\, the American Heart Association National Scientist Development Grant\, and the CAREER award from National Science Foundation. She was Elected to the College of Fellows at the American Institute for Medical and Biological Engineers (AIMBE) in 2017. She spent the first 11 years of her career at UCLA department of Chemical and Biomolecular Engineering and has recently relocated to Duke University\, where she holds appointments in Biomedical Engineering\, Neurology and Dermatology.
URL:https://coe.northeastern.edu/event/che-seminar-series-biomaterials-to-unlock-the-regenerative-capacity-of-tissues/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210421T173000
DTEND;TZID=America/New_York:20210421T173000
DTSTAMP:20260510T041916
CREATED:20210421T153821Z
LAST-MODIFIED:20210421T153821Z
UID:25541-1619026200-1619026200@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Muhamed Yildiz
DESCRIPTION:PhD Dissertation Defense: Interpretable Machine Learning for Retinopathy of Prematurity \nMuhamed Yildiz \nLocation: Zoom Link \nAbstract: Retinopathy of Prematurity (ROP)\, a leading cause of childhood blindness\, is diagnosed by clinical ophthalmoscopic examinations or reading retinal images. Plus disease\, defined as abnormal tortuosity and dilation of the posterior retinal blood vessels\, is the most important feature to determine treatment-requiring ROP. State-of-the-art ROP detection systems employ convolutional neural networks (CNNs) %\cite{brown2018automated} and achieve up to $0.947$ and $0.982$ area under the ROC curve (AUC) in the discrimination of \textit{normal} and \textit{plus} levels of ROP. However\, due to their black-box nature\, clinicians are reluctant to trust diagnostic predictions of CNNs.\nFirst\, we aim to create an interpretable\, feature extraction-based pipeline\, namely\, I-ROP ASSIST\, that achieves CNN like performance when diagnosing plus disease from retinal images. Our method segments retinal vessels\, detects the vessel centerlines. Then\, our method extracts features relevant to ROP\, including tortuosity and dilation measures\, and uses these features for classification via logistic regression\, support vector machines and neural networks to assess a severity score for the input. For predicting \textit{normal} and \textit{plus} levels of ROP on a dataset containing 5512 posterior retinal images\, we achieve $0.88$ and $0.94$ AUC\, respectively. Our system combining automatic retinal vessel segmentation\, tracing\, feature extraction and classification is able to diagnose plus disease in ROP with CNN like performance.\nThen\, we introduce a novel method for extracting tortuosity features. Current feature extraction pipelines of retinal image analysis systems extract tortuosity features based on the derivatives of vessel centerlines or a segment of a vessel. Our method eliminates the need for finding vessel centerlines by introducing a method for calculating curvature at each pixel in the fundus image. When calculating curvature\, we use the geometric interpretation of eigenvectors of the Hessian of an interpolation function. By selecting an appropriate interpolation function\, our method can be applied in many domains\, including corner detection\, noise removal and image registration. We present the results of our method on artificial images that contains curved structures such as circle\, sine waves as well as real images from MNIST and our retinal fundus image dataset. Experimental results shows that our model accurately captures the high curvature parts of the blood vessels. \nFurthermore\, we aim to address the interpretability problem of CNN-based ROP detection system. Incorporating visual attention capabilities in CNNs enhances interpretability by highlighting regions in the images that CNNs utilize for prediction. Generic visual attention methods do not leverage structural domain information such as tortuosity and dilation of retinal blood vessels in ROP diagnosis. We propose the Structural Visual Guidance Attention Networks (SVGA-Net) method\, that leverages structural domain information to guide visual attention in CNNs. SVGA-Net achieves $0.979$ and $0.987$ AUC to predict \textit{normal} and \textit{plus} levels of ROP. Moreover\, SVGA-Net consistently results in higher AUC compared to visual attention CNNs without guidance\, baseline CNNs\, and CNNs with structured masks.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-muhamed-yildiz/
END:VEVENT
END:VCALENDAR