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DTSTART;VALUE=DATE:20210317
DTEND;VALUE=DATE:20210422
DTSTAMP:20260510T014352
CREATED:20210318T134829Z
LAST-MODIFIED:20210318T134829Z
UID:25081-1615939200-1619049599@coe.northeastern.edu
SUMMARY:Study Recruitment: Ancient Techniques and Mental Health Today
DESCRIPTION:Northeastern Department of Philosophy & Religion  \nHave you been experiencing stress and anxiety? \nYou may be eligible to participate in our study! \nHelp us investigate the impact of mindfulness on various life outcomes! All components of this study will take place virtually; participants will be asked to attend two 30-minute Zoom sessions in addition to up to 5 weeks of short\, daily smartphone tasks. \nYou must be 18 years or older\, a Boston-based Northeastern undergraduate student\, and a native English speaker to be eligible to participate. \nParticipants will receive $80 in compensation. \nContact us at pwolstudy@gmail.com if you’re interested and to see if you are eligible! \nThis study has been reviewed and approved by the Northeastern University Institutional Review Board (#21-02-21).
URL:https://coe.northeastern.edu/event/study-recruitment-ancient-techniques-and-mental-health-today/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210405T180000
DTEND;TZID=America/New_York:20210405T193000
DTSTAMP:20260510T014352
CREATED:20210329T152354Z
LAST-MODIFIED:20210329T152354Z
UID:25237-1617645600-1617651000@coe.northeastern.edu
SUMMARY:Scattergories w/ GWiSE
DESCRIPTION:Join GWiSE for our monthly community time on Monday\, April 5th at 6 pm EST to play some zoom scattergories and maybe win some prizes! This event will have two winners: $25 for the person with the most points $25 for overall funniest answers (we will vote!). Register on SAIL 🙂
URL:https://coe.northeastern.edu/event/scattergories-w-gwise/
ORGANIZER;CN="GWiSE%3A Graduate Women in Science and Engineering":MAILTO:gwise.neu@gmail.com
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210406T100000
DTEND;TZID=America/New_York:20210406T110000
DTSTAMP:20260510T014352
CREATED:20210325T135135Z
LAST-MODIFIED:20210325T135135Z
UID:25215-1617703200-1617706800@coe.northeastern.edu
SUMMARY:Global Co-op Self-Developing Information 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-information-session/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210406T100000
DTEND;TZID=America/New_York:20210406T110000
DTSTAMP:20260510T014352
CREATED:20210401T183518Z
LAST-MODIFIED:20210401T183518Z
UID:25294-1617703200-1617706800@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Subhramoy Mohanti
DESCRIPTION:PhD Proposal Review: Distributed Data and Energy Beamforming with Unmanned Vehicles for Wireless IoT : A Systems Perspective \nSubhramoy Mohanti \nLocation: Teams Meeting \nAbstract: The pervasive deployment of the wireless Internet of Things (IoT) has given rise to heterogeneous sensors and small form-factor computing devices in homes\, offices\, public spaces\, manufacturing floors\, among others. Such large number of connected devices require (i) simple ways of charging\, so that they remain operationally available\, and (ii) effective ways of sharing wireless spectrum\, so that they continue to transmit and receive data amidst competing and interfering signals. This thesis focuses on the link and physical layer of the protocol stack to enable distributed beamforming as a key enabler for these two objectives. Specifically\, we experimentally demonstrate how beamforming capability can address both wireless power transfer (WPT) needs and resilient communication in interference-challenged environments.\nThis thesis proposes a method for accessing and sharing the wireless channel for both regular data communication and WPT. This is the first work that accomplishes these dissimilar tasks within the constraints of the standard compliant IEEE 802.11 protocol\, resulting in a practical and so called ‘WiFi-friendly Energy Delivery’ (WiFED). First\, WiFED exploits the IEEE 802.11 supported protocol features to request energy and for energy transmitters to participate in energy transfer via beamforming. Second\, it devises a controller-driven bipartite matching algorithm\, assigning appropriate number of energy transmitters to sensors for efficient energy delivery. Thirdly\, it detects outlier sensors\, which have limited power reception from static energy transmitters and utilizes mobile energy transmitters to satisfy their charging cycles.\nFrom a communication-only perspective that relies on distributed beamforming\, this thesis presents AirBeam\, a software-based approach that runs on Unmanned Aerial Vehicles (UAVs) to deliver on-demand data to sensors deployed in infrastructure constrained environments. We first show why this problem is difficult given the continuous hovering-related channel fluctuations\, synchronizing the distributed transmit streams without a wired clock reference\, the need to ensure timely feedback from the ground receiver due to the channel coherence time\, and the size\, weight\, power\, and cost (SWaP-C) constraints for UAVs. This work is extended further to consider realistic traffic patterns and packet arrival thresholds\, involving dynamic grouping of transmitters to beamform towards target receivers at any given time. Again\, we evaluate outcome both experimentally and in a virtual environment in Colosseum\, the world’s largest RF emulator.\nSince beamforming requires the action of multiple devices not directly connected to each other by wire\, we introduce a security framework called AirID\, which identifies authorized beamforming UAVs by learning their so called ‘RF fingerprints’. This step requires applying deep learning techniques on their received signals\, with the goal of identifying discriminative features introduced by the transmitter due to process variations. Our approach involves intentionally inserting ‘signatures’ in the signals from each known UAV\, which are detected through a deep convolutional neural network (CNN) at the physical layer\, without affecting the ongoing UAV data communication process.\nIn the proposed work\, we will explore optimized placement of UAVs\, while also considering battery limits\, to enhance beamforming performance. We will validate these outcomes in a testbed of 4-5 UAVs.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-subhramoy-mohanti/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210407T120000
DTEND;TZID=America/New_York:20210407T130000
DTSTAMP:20260510T014352
CREATED:20210405T134754Z
LAST-MODIFIED:20210405T134754Z
UID:25305-1617796800-1617800400@coe.northeastern.edu
SUMMARY:ChE Seminar Series: Engineered Autonomous Control of Metabolic Pathways
DESCRIPTION:ChE Seminar Series Presents: \nKristala L. J. Prather\, Ph.D.\nArthur D. Little Professor\, Department Executive Officer\, Department of Chemical Engineering\, MIT \nEngineered Autonomous Control of Metabolic Pathways \nAbstract\nMicrobial systems offer the opportunity to produce a wide variety of chemical compounds in a sustainable fashion. Economical production\, however\, requires processes that operate with high titer\, productivity\, and yield. One challenge towards maximizing yields is the need to use substrate for biomass\, resulting in a competing pathway that cannot merely be eliminated. Productivities may also be significantly influenced by the timing of expression of genes in the production pathway. Dynamic metabolic engineering has emerged as a means to address these and other impediments in strain performance. Ideally\, the triggers for dynamic control would be autonomous\, that is\, independent of any external intervention by the operator. We have developed such autonomous devices based on pathway-independent quorum-sensing circuits and have demonstrated their utility across several distinct metabolic pathways and with varying levels of complexity. In this talk\, I will describe our approach for development of these Metabolite Valves and results to date from their implementation. \nBiography\nKristala L.J. Prather is the Arthur D. Little Professor in and Executive Officer of the Department of Chemical\nEngineering at MIT. She received an S.B. degree from MIT in 1994 and Ph.D. from the University of California\, Berkeley (1999)\, and worked 4 years in BioProcess Research and Development at the Merck Research Labs prior to joining MIT. Her research interests are centered on the design and assembly of recombinant microorganisms for the production of small molecules\, with additional efforts in novel bioprocess design approaches. A particular focus is the elucidation of design principles for the production of unnatural organic compounds with engineered control of metabolic flux within the framework of the burgeoning field of synthetic biology. Prather is the recipient of an Office of Naval Research Young Investigator Award (2005)\, a Technology Review “TR35” Young Innovator Award (2007)\, a National Science Foundation CAREER Award (2010)\, the Biochemical Engineering Journal Young Investigator Award (2011)\, and the Charles Thom Award of the Society for Industrial Microbiology and Biotechnology (2017). Additional honors include selection as the Van Ness Lecturer at Rensselaer Polytechnic Institute (2012)\, as a Fellow of the Radcliffe Institute for Advanced Study (2014-2015)\, the American Association for the Advancement of Science (AAAS; 2018)\, and the American Institute for Medical and Biological Engineering (AIMBE; 2020). \nPlease email Alyssa Ramsey at a.ramsey@northeastern.edu for the link to the seminar.
URL:https://coe.northeastern.edu/event/che-seminar-series-engineered-autonomous-control-of-metabolic-pathways/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210407T140000
DTEND;TZID=America/New_York:20210407T150000
DTSTAMP:20260510T014352
CREATED:20210323T173831Z
LAST-MODIFIED:20210323T173831Z
UID:25189-1617804000-1617807600@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Vikrant Shah
DESCRIPTION:PhD Dissertation Defense: Visual Navigation Applications in Low Contrast Environments: Multi Sensor Iceberg Mapping \nVikrant Shah \nLocation: Zoom Link \nAbstract: Most approaches to visual navigation make multiple assumptions about the scenes being imaged. There are implicit assumptions about the scene being predominantly static and the availability of well illuminated\, texture rich\, objects in the scene. In some cases these assumptions severely limit or eliminate the full applicability of visual Simultaneous Localization and Mapping (SLAM) and Structure from Motion (SfM) methodologies. This dissertation attempts to address problems where the assumptions of static scenes and texture rich objects are not valid. Motivated by the application of mapping rotating and translating icebergs\, we propose a system level solution for addressing the problem of mapping large\, low contrast\, moving targets with slow but complicated dynamics. \nOur approach leverages the complementary nature of multiple sensing modalities and utilizes a rigidly coupled combination of a subsurface multibeam sonar (a line scan sensor) and an optical camera (an area scan sensor). This allows the system to exploit the optical camera information to perform iceberg relative navigation\, which can be directly used by the multibeam sonar to map the iceberg underwater. To compensate for the effect of low contrast we conducted an in-depth analysis of features detectors and descriptors on end-to-end SfM algorithms to demonstrate and understand how methodologies such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and Zernike Moment descriptors help improve the overall accuracy in these challenging applications. \nWe merge these approaches into an algorithmic framework that allows us to compute the scale of the navigation solution and iceberg centric navigation corrections. These corrections can then be used for accurate iceberg reconstructions. This enables a quantitative analysis of our iceberg mapping efforts including volume estimation and change detection. \nWe successfully demonstrate our approach on real field data from three of the icebergs surveyed multiple times during the 2018 and 2019 campaigns to the Sermilik fjord in Eastern Greenland. Availability of iceberg mounted Global Navigation Satellite System (GNSS) observations during these research expeditions also allowed for a comparison of this approach against ground truth\, providing additional confidence in the systems level mapping efforts. The accuracy of the reconstructions is demonstrated by estimating iceberg volumes\, calculating their ablation rates\, and performing change detection at a granular scale.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-vikrant-shah/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210408T110000
DTEND;TZID=America/New_York:20210408T120000
DTSTAMP:20260510T014352
CREATED:20210406T170543Z
LAST-MODIFIED:20210406T170543Z
UID:25337-1617879600-1617883200@coe.northeastern.edu
SUMMARY:ECE Seminar: Mahdi Imani
DESCRIPTION:ECE Seminar: Reinforcement Learning Perspective to Data-Driven and Model-Based Experimental Design \nMahdi Imani \nLocation: Zoom Link \nAbstract: Design and decision-making are pervasive in most practical systems including smart grids\, transportation\, manufacturing\, healthcare\, and smart homes. Accurate system modeling is difficult in most systems/processes due to the complicated system dynamics\, multi-physics and multiple time scales involved in phenomena\, hybrid dynamics across cyber and physical layers\, and various sources of parametric and environmental uncertainties. Design and decision-making in these systems are fraught with choices\, choices that are often expensive\, complex\, and high-dimensional\, with interactions and uncertainties that make them difficult for individuals to reason about. This talk will mainly focus on the speaker’s latest research on providing a new unified reinforcement learning perspective for model-based and data-driven experimental design to enable scalable\, efficient\, and reliable design and decision-making under various sources of uncertainty. \nBio: Mahdi Imani is an Assistant Professor in the Department of Electrical and Computer Engineering at the George Washington University. He received his Ph.D. degree in Electrical and Computer Engineering from Texas A&M University in 2019\, and his M.Sc. degree in Electrical Engineering and his B.Sc. degree in Mechanical Engineering\, both from the University of Tehran in 2014 and 2012. His research interests include Machine Learning\, Control Theory\, and Signal Processing\, with a wide range of applications from computational biology to cyber-physical systems. He has been elevated to IEEE Senior Member grade in 2021. He is also the recipient of multiple awards\, including NSF SCH Aspiring PI Awardee in 2020 and 2021\, IBM Research Almaden Distinguished Speaker in 2019\, the Association of Former Students Distinguished Graduate Student Award for Excellence in Research-Doctoral in 2019\, the Best Ph.D. Student Award in ECE department and a single finalist nominee of ECE department for the Outstanding Graduate Student Award in the college of engineering at Texas A&M University in 2018\, and the best paper finalist award from the 49th Asilomar Conference on Signals\, Systems\, and Computers\, 2015.
URL:https://coe.northeastern.edu/event/ece-seminar-mahdi-imani/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210409T120000
DTEND;TZID=America/New_York:20210409T130000
DTSTAMP:20260510T014352
CREATED:20210405T134852Z
LAST-MODIFIED:20210405T134852Z
UID:25303-1617969600-1617973200@coe.northeastern.edu
SUMMARY:ChE Seminar Series: Tools for Analyzing and Repairing Biological Systems
DESCRIPTION:ChE Seminar Series Presents: \nDr. Edward S. Boyden\, Ph. D.\nY. Eva Tan Professor in Neurotechnology at MIT\nHoward Hughes Medical Institute\, McGovern Institute\nProfessor\, Departments of Brain and Cognitive Sciences\, Media Arts and Sciences\, and Biological Engineering\, MIT \nTools for Analyzing and Repairing Biological Systems \nAbstract \nUnderstanding and repairing complex biological systems\, such as the brain\, requires technologies for systematically observing and controlling these systems.  We are discovering new molecular principles that enable such technologies.  For example\, we discovered that one can physically magnify biological specimens by synthesizing dense networks of swellable polymer throughout them\, and then chemically processing the specimens to isotropically swell them.  This method\, which we call expansion microscopy\, enables ordinary microscopes to do nanoimaging – important for mapping the brain across scales.  Expansion of biomolecules away from each other also decrowds them\, enabling previously invisible nanostructures to be labeled and seen.  As a second example\, we discovered that microbial opsins\, genetically expressed in neurons\, could enable their electrical activities to be precisely controlled in response to light.  These molecules\, now called optogenetic tools\, enable causal assessment of how neurons contribute to behaviors and pathological states\, and are yielding insights into new treatment strategies for brain diseases.  Finally\, we are developing\, using new strategies such as robotic directed evolution\, fluorescent reporters that enable the precision measurement of signals such as voltage and calcium.  By fusing such reporters to self-assembling peptides\, they can be stably clustered within cells at random points\, distant enough to be resolved by a microscope\, but close enough to spatially sample the relevant biology. Such clusters\, which we call signaling reporter islands (SiRIs)\, permit many fluorescent reporters to be used within a single cell\, to simultaneously reveal relationships between different signals.  We share all these tools freely\, and aim to integrate the use of these tools so as to enable comprehensive understandings of neural circuits. \nBiography: \nEd Boyden is Y. Eva Tan Professor in Neurotechnology at MIT\, an investigator of the Howard Hughes Medical Institute and the MIT McGovern Institute\, and professor of Brain and Cognitive Sciences\, Media Arts and Sciences\, and Biological Engineering at MIT. He leads the Synthetic Neurobiology Group\, which develops tools for analyzing and repairing complex biological systems such as the brain\, and applies them systematically to reveal ground truth principles of biological function as well as to repair these systems. He co-directs the MIT Center for Neurobiological Engineering\, which aims to develop new tools to accelerate neuroscience progress\, and is a faculty member of the MIT Center for Environmental Health Sciences\, Computational & Systems Biology Initiative\, and Koch Institute. \nAmongst other recognitions\, he has received the Wilhelm Exner Medal (2020)\, the Croonian Medal (2019)\, the Lennart Nilsson Award (2019)\, the Warren Alpert Foundation Prize (2019)\, the Rumford Prize (2019)\, the Canada Gairdner International Award (2018)\, the Breakthrough Prize in Life Sciences (2016)\, the BBVA Foundation Frontiers of Knowledge Award (2015)\, the Carnegie Prize in Mind and Brain Sciences (2015)\, the Jacob Heskel Gabbay Award (2013)\, the Grete Lundbeck Brain Prize (2013)\, the NIH Director’s Pioneer Award (2013)\, the NIH Director’s Transformative Research Award (three times\, 2012\, 2013\, and 2017)\, and the Perl/UNC Neuroscience Prize (2011). He was also named to the World Economic Forum Young Scientist list (2013) and the Technology Review World’s “Top 35 Innovators under Age 35” list (2006)\, and is an elected member of the National Academy of Sciences (2019)\, the American Academy of Arts and Sciences (2017)\, the National Academy of Inventors (2017)\, and the American Institute for Medical and Biological Engineering (2018). His group has hosted hundreds of visitors to learn how to use new biotechnologies\, and he also regularly teaches at summer courses and workshops in neuroscience\, and delivers lectures to the broader public (e.g.\, TED (2011)\, TED Summit (2016)\, World Economic Forum (2012\, 2013\, 2016)). \nEd received his Ph.D. in neurosciences from Stanford University as a Hertz Fellow\, working in the labs of Jennifer Raymond and Richard Tsien\, where he discovered that the molecular mechanisms used to store a memory are determined by the content to be learned. In parallel to his PhD\, as an independent side project\, he co-invented optogenetic control of neurons\, which is now used throughout neuroscience. Previously\, he studied chemistry at the Texas Academy of Math and Science at the University of North Texas\, starting college at age 14\, where he worked in Paul Braterman’s group on origins of life chemistry. He went on to earn three degrees in electrical engineering and computer science\, and physics\, from MIT\, graduating at age 19\, while working on quantum computing in Neil Gershenfeld’s group. Long-term\, he hopes that understanding how the brain generates the mind will help provide a deeper understanding of the human condition\, and perhaps help humanity achieve a more enlightened state. \nPlease email Alyssa Ramsey at a.ramsey@northeastern.edu for the link to the seminar.
URL:https://coe.northeastern.edu/event/che-seminar-series-tools-for-analyzing-and-repairing-biological-systems/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210410T080000
DTEND;TZID=America/New_York:20210410T190000
DTSTAMP:20260510T014352
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:20260510T014352
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:20260510T014352
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:20260510T014352
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:20260510T014352
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:20260510T014352
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:20260510T014352
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:20260510T014352
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:20260510T014352
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:20260510T014352
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:20260510T014352
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:20260510T014352
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:20260510T014352
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:20260510T014352
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
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Curry Student Center 360 Huntington Ave. Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave.:geo:-71.0885286,42.3394629
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210416T153000
DTEND;TZID=America/New_York:20210416T163000
DTSTAMP:20260510T014352
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:20260510T014352
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:20260510T014352
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:20260510T014352
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:20260510T014352
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:20260510T014352
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:20260510T014352
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:20260510T014352
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
END:VCALENDAR