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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210416T130000
DTEND;TZID=America/New_York:20210416T140000
DTSTAMP:20260426T205009
CREATED:20210414T005310Z
LAST-MODIFIED:20210414T005310Z
UID:4841-1618578000-1618581600@ece.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://ece.northeastern.edu/event/ece-phd-dissertation-defense-kai-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210416T093000
DTEND;TZID=America/New_York:20210416T103000
DTSTAMP:20260426T205009
CREATED:20210412T185721Z
LAST-MODIFIED:20210412T185721Z
UID:4838-1618565400-1618569000@ece.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://ece.northeastern.edu/event/ece-ms-thesis-defense-shanchuan-liang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210412T150000
DTEND;TZID=America/New_York:20210412T170000
DTSTAMP:20260426T205009
CREATED:20210412T185039Z
LAST-MODIFIED:20210412T185039Z
UID:4835-1618239600-1618246800@ece.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://ece.northeastern.edu/event/ece-phd-proposal-review-murphy-wonsick/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210412T150000
DTEND;TZID=America/New_York:20210412T160000
DTSTAMP:20260426T205009
CREATED:20210401T223643Z
LAST-MODIFIED:20210401T223643Z
UID:4822-1618239600-1618243200@ece.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://ece.northeastern.edu/event/ece-phd-dissertation-defense-neville-sun/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210412T090000
DTEND;TZID=America/New_York:20210412T100000
DTSTAMP:20260426T205009
CREATED:20210312T012401Z
LAST-MODIFIED:20210312T012401Z
UID:4780-1618218000-1618221600@ece.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://ece.northeastern.edu/event/ece-phd-dissertation-defense-armin-moharrer/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210408T110000
DTEND;TZID=America/New_York:20210408T120000
DTSTAMP:20260426T205009
CREATED:20210406T210543Z
LAST-MODIFIED:20210406T210543Z
UID:4829-1617879600-1617883200@ece.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://ece.northeastern.edu/event/ece-seminar-mahdi-imani/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210407T140000
DTEND;TZID=America/New_York:20210407T150000
DTSTAMP:20260426T205009
CREATED:20210323T213831Z
LAST-MODIFIED:20210323T213831Z
UID:4809-1617804000-1617807600@ece.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://ece.northeastern.edu/event/ece-phd-dissertation-defense-vikrant-shah/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210406T100000
DTEND;TZID=America/New_York:20210406T110000
DTSTAMP:20260426T205009
CREATED:20210401T223518Z
LAST-MODIFIED:20210401T223518Z
UID:4821-1617703200-1617706800@ece.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://ece.northeastern.edu/event/ece-phd-proposal-review-subhramoy-mohanti/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210326T130000
DTEND;TZID=America/New_York:20210326T140000
DTSTAMP:20260426T205009
CREATED:20210322T180141Z
LAST-MODIFIED:20210322T180141Z
UID:4805-1616763600-1616767200@ece.northeastern.edu
SUMMARY:ECE Seminar: Sara Dean
DESCRIPTION:ECE Seminar: Reliable Machine Learning in Feedback Systems \nSara Dean \nLocation: Zoom Link \nAbstract: Machine learning techniques have been successful for processing complex information\, and thus they have the potential to play an important role in data-driven decision-making and control. However\, ensuring the reliability of these methods in feedback systems remains a challenge\, since classic statistical and algorithmic guarantees do not always hold. In this talk\, I will provide rigorous guarantees of safety and discovery in dynamical settings relevant to robotics and recommendation systems. I take a perspective based on reachability\, to specify which parts of the state space the system avoids (safety) or can be driven to (discovery). For data-driven control\, we show finite-sample performance and safety guarantees which highlight relevant properties of the system to be controlled. For recommendation systems\, we introduce a novel metric of discovery and show that it can be efficiently computed. In closing\, I discuss how the reachability perspective can be used to design social-digital systems with a variety of important values in mind. \nBio: Sarah is a PhD candidate in the Department of Electrical Engineering and Computer Science at UC Berkeley\, advised by Ben Recht. She received her MS in EECS from Berkeley and BSE in Electrical Engineering and Math from the University of Pennsylvania. Sarah is interested in the interplay between optimization\, machine learning\, and dynamics in real-world systems. Her research focuses on developing principled data-driven methods for control and decision-making\, inspired by applications in robotics\, recommendation systems\, and developmental economics. She is a co-founder of a transdisciplinary student group\, Graduates for Engaged and Extended Scholarship in computing and Engineering\, and the recipient of a Berkeley Fellowship and a NSF Graduate Research Fellowship.
URL:https://ece.northeastern.edu/event/ece-seminar-sara-dean/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210326T120000
DTEND;TZID=America/New_York:20210326T130000
DTSTAMP:20260426T205009
CREATED:20210323T180009Z
LAST-MODIFIED:20210323T180009Z
UID:4808-1616760000-1616763600@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Mo Han
DESCRIPTION:PhD Dissertation Defense: Human Grasp Intent Inference and Multimodal Control in Prosthetic Hands \nMo Han \nLocation: Zoom Link \nAbstract: Upper limb and hand functionality is critical to many activities of daily living and the amputation of one can lead to significant functionality loss for individuals. From this perspective\, advanced prosthetic hands of the future are anticipated to benefit from improved shared control between a robotic hand and its human user\, but more importantly from the improved capability to infer human intent from multimodal sensor data to provide the robotic hand perception abilities regarding the operational context. Such multimodal data may be collected from various environment sensors such as camera providing visual information\, as well as easily-accessed human physiologic sensors including electromyographic (EMG) sensors. A fusion methodology for environmental state and human intent estimation can combine these sources of evidence in order to help prosthetic hand motion planning and control. \nAs part of a multi-disciplinary project\, i.e. HANDS project\, which aims at designing a robotic hand as an upper limb prosthetic device\, we developed two independent prosthetic control systems (HANDS V1 and HANDS V2) integrating multimodal sources of EMG and visual evidences into the control loop. Multiple grasps required for activities of daily living can be performed by both robotic systems which were developed in a lighter and cheaper semi-autonomous manner. The HANDS V1 system was first developed to provide an easy and convenient prosthesis with a portable EMG armband and a built-in palm camera\, and hereafter the HANDS V2 was constructed as an upgraded solution of HANDS V1 to achieve more difficult tasks with more identified grasp types\, more EMG channels and more complicated visual information involved. Both systems depend on multimodal signals from EMG and vision\, where the EMG could reflect the physiologic features related to user intents\, while the robustness and adaptability to different users could be retained by the visual information relying more on surrounding environments. We collected two datasets for the initialization of each system\, and the developments of the EMG-control\, visual-control\, and joint-control algorithms were conducted for both systems. We exploited efficient computer vision and physiological signal processing methodologies to decrease the system complexity as well as improve the user comfort\, in order to provide smarter and cheaper prosthetic hands to the audience. Online experiments were executed and evaluated on both HANDS V1 and HANDS V2 systems\, implemented by the Robot Operating System (ROS) system.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-mo-han/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210322T100000
DTEND;TZID=America/New_York:20210322T110000
DTSTAMP:20260426T205009
CREATED:20210226T235630Z
LAST-MODIFIED:20210315T180259Z
UID:4767-1616407200-1616410800@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Ibrahim Farah
DESCRIPTION:MS Thesis Defense: Multipath Beam Steering for OFDM Systems \nIbrahim Farah \nLocation: Zoom \nAbstract: OFDM systems prove effective in combatting the frequency-selective fading\, dispersive response of underwater acoustic channels. Coherent symbol detection requires estimation of this channel response at each receiving element\, typically done through tap-based models (Least Squares) or path-based models (Path Identification). This talk presents a spatial processing design which uses a multi-channel receiver to isolate individual multi-path returns\, both easing the channel estimation requirements and increasing the SNR for symbol detection. The beam steering algorithms are considered in both a coherent and differential OFDM system context and include narrowband and broadband beamforming to the principal\, stable path\, as well as narrowband and broadband null-steering. These spatial processing algorithms are then extended to an iterative implementation\, which approaches the theoretical performance for a beamformer with full multipath channel knowledge. The performance of these algorithms is compared to their single-channel equivalents using both the LS and PI algorithms for coherent detection.
URL:https://ece.northeastern.edu/event/ms-thesis-defense-ibrahim-farah/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210318T123000
DTEND;TZID=America/New_York:20210318T133000
DTSTAMP:20260426T205009
CREATED:20210316T213855Z
LAST-MODIFIED:20210317T003641Z
UID:4786-1616070600-1616074200@ece.northeastern.edu
SUMMARY:IEEE Magnetics Society Distinguished Seminar: Dr. Mathias Kläui
DESCRIPTION:IEEE Magnetics Society Seminar for 2021 \nDistinguished Lecture: Dr. Mathias Kläui \nJointly hosted by: Richmond Section Jt. Chapter\, Washington/Northern Virginia Jt Chapter\, and the Boston Section Chapter \n\nPlease join us online on Thursday\, March 18th\, at 12:30 pm for the first installment of out IEEE Distinguished Lecture Seminar Series.\nJoin with Google Meet: http://meet.google.com/xft-dvqw-pac\nJoin by phone: (US) +1 413-359-0778; PIN: 184921174#\n(US) +1 413-359-0778 PIN: 184 921 174# \n\nMathias Kläui is professor of physics at Johannes Gutenberg-University Mainz and adjunct professor at the Norwegian University of Science and Technology. He received his PhD at the University of Cambridge\, after which he joined the IBM Research Labs in Zürich. He was a junior group leader at the University of Konstanz and then became associate professor in a joint appointment between the EPFL and the PSI in Switzerland before moving to Mainz. His research focuses on nanomagnetism and spin dynamics on the nanoscale in new materials. His research covers from blue sky fundamental science to applied projects with major industrial partners. He has published more than 300 articles and given more than 200 invited talks. He is a Senior member of the IEEE and\, a Fellow of the IOP and has been awarded a number of prizes and scholarships. He is one of the 2020/2021 IEEE Magnetics Society Distinguished Lecturers. Contact details and more information at www.klaeui-lab.de. \n\nAntiferromagnetic Insulatronics: Spintronics without Magnetic fields \nWhile known for a long time\, antiferromagnetically ordered\nsystems have previously been considered\, as expressed by Louis Néel in his Nobel Prize Lecture\, to be “interesting but useless”. However\, since antiferromagnets potentially promises faster operation\, enhanced stability with respect to interfering magnetic fields and higher integration due to the absence of dipolar coupling\, they could potentially become a game changer for new spintronic devices. The zero net moment makes manipulation using conventional magnetic fields challenging. However recently\, these materials have received renewed attention due to possible manipulation based on new approaches such as photons or spin-orbit torques. In this talk\, we will present an overview of the key features of antiferromagnets to potentially functionalize their unique properties. This includes writing\, reading and transporting information using antiferromagnetic. \nWe recently realized switching in the metallic antiferromagnet Mn2Au by intrinsic staggered spin-orbit torques and characterize the switching properties by direct imaging. While switching by staggered intrinsic spin-orbit torques in metallic AFMs requires special structural asymmetry\, interfacial non-staggered spin-orbit torques can switch multilayers of many insulating AFMs capped with heavy metal layers. We probe switching and spin transport in selected collinear insulating antiferromagnets\, such as NiO\, CoO and hematite. In NiO and CoO we find that there are multiple switching mechanisms that result in the reorientation of the Néel vector and additionally effects related to electromigration of the heavy metal layer can obscure the magnetic switching. For the spin transport\, spin currents are generated by heating as resulting from the spin Seebeck effect and by spin pumping measurements and we find in vertical transport short (few nm) spin diffusion lengths. For hematite\, however\, we find in a non-local geometry that spin transport of tens of micrometers is possible. We detect a first harmonic signal\, related to the spin conductance\, that exhibits a maximum at the spin-flop reorientation\, while the second harmonic signal\, related to the Spin Seebeck conductance\, is linear in the amplitude of the applied magnetic field. The first signal is dependent on the direction of the Néel vector and the second one depends on the induced magnetic moment due to the field. We identify the domain structure as the limiting factor for the spin transport. We recently also achieved transport in the easy plane phase\, which allows us to obtain long distance spin transport in hematite even at room temperature. From the power and distance dependence\, we unambiguously distinguish long-distance transport based on diffusion from predicted spin superfluidity that can potentially be used for logic. A number of excellent reviews are available for further information on recent developments in the field.
URL:https://ece.northeastern.edu/event/ieee-magnetics-society-distinguished-seminar-dr-mathias-klaui/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210226T123000
DTEND;TZID=America/New_York:20210226T133000
DTSTAMP:20260426T205009
CREATED:20210126T181107Z
LAST-MODIFIED:20210217T220812Z
UID:4709-1614342600-1614346200@ece.northeastern.edu
SUMMARY:Engineers Week: Exotic Interactions with Light and Sound in Metamaterials with Broken Symmetries
DESCRIPTION:The Electrical and Engineering Department presents speaker Andrea Alù for this year’s Engineers Week events. \n\nLocation: This event is virtual\, free\, and open to the NU community. \nJoin Zoom Meeting\nhttps://northeastern.zoom.us/j/94710036507?pwd=RWFqbmtPMit0RktsSXpCZE1Fd3VmZz09 \nPasscode: 0000 \nMeeting ID: 947 1003 6507\nOne tap mobile\n+13017158592\,\,94710036507# US (Washington DC)\n+13126266799\,\,94710036507# US (Chicago) \n\nExotic Interactions with Light and Sound in Metamaterials with Broken Symmetries \nAndrea Alù Founding Director and Einstein Professor at the Photonics Initiative\, CUNY Advanced Science Research Center \nAndrea Alù\nPhotonics Initiative\, Advanced Science Research Center\, City University of New York\nPhysics Program\, Graduate Center\, City University of New York\nDepartment of Electrical and Computer Engineering\, City College of New York\n85 St. Nicholas Terrace\, New York\, NY 10031\, U.S.A.\naalu@gc.cuny.edu\, http://alulab.org \nIn this talk\, I discuss our recent research findings in nano-optics\, electromagnetics and acoustics\, showing how suitably tailored meta-atoms and arrays of them enable new phenomena to manipulate light\, radio-waves and sound. I discuss venues to largely break Lorentz reciprocity and realize isolation without the need of a magnetic bias\, based on broken time-reversal symmetry induced by mechanical motion\, spatio-temporal modulation and/or nonlinearities. I also discuss how broken symmetries in space and space-time provide the opportunity to induce topological order in metamaterials. Another class of metamaterials based on broken symmetries are parity-time symmetric media\, which are asymmetric in space\, but symmetric upon parity and time inversion\, and can enhance the exotic response of metamaterials beyond the limitations of passive systems. In the talk\, I will discuss the impact of these concepts from basic science to technology\, from classical waves to quantum phenomena. \nAndrea Alù is the Founding Director and Einstein Professor at the Photonics Initiative\, CUNY Advanced Science Research Center. He received his Laurea (2001) and PhD (2007) from the University of Roma Tre\, Italy\, and\, after a postdoc at the University of Pennsylvania\, he joined the faculty of the University of Texas at Austin in 2009\, where he was the Temple Foundation Endowed Professor until Jan. 2018. Dr. Alù is a Fellow of NAI\, IEEE\, AAAS\, OSA\, SPIE and APS\, and has received several scientific awards\, including the IEEE Kiyo Tomiyasu Award\, the Vannevar Bush Faculty Fellowship from DoD\, the ICO Prize in Optics\, the NSF Alan T. Waterman award\, the OSA Adolph Lomb Medal\, and the URSI Issac Koga Gold Medal. \nDownload Flyer (pdf) \n \n 
URL:https://ece.northeastern.edu/event/engineers-week-presentation-andrea-alu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210225T110000
DTEND;TZID=America/New_York:20210225T120000
DTSTAMP:20260426T205009
CREATED:20210222T233157Z
LAST-MODIFIED:20210222T233157Z
UID:4760-1614250800-1614254400@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Jinghan Zhang
DESCRIPTION:PhD Proposal Review: Design Space Exploration: Designing a Unified Platform for a Domain of Streaming Applications \nJinghan Zhang \nLocation: Zoom Link \nAbstract: Many demanding streaming applications share functional and structural similarities with\nother applications in their respective domain\, e.g. video analytics\, software-defined radio\, and radar. This opens the opportunity for specialization (e.g. heterogeneous computing) to achieve the needed efficiency and/or performance. However\, current Design Space Exploration (DSE) focuses on an individual application in isolation (e.g. one particular vision flow)\, but not a set of similar applications.Hence\, optimizations that occur due to considering multiple applications simultaneously are missed. New DSE methodologies and tools are needed with a broader scope of application sets instead of individual applications.\nThis dissertation introduces a novel Domain DSE approach focusing on streaming applications. Key contributions are: (1) a formalized method to extract the functional and structural similarities of domain applications\, (2) domain application generation to provide enough synthetic domains as study cases\, (3) a rapid platform performance estimation and comparison at two abstraction levels: Domain Score (DS) and Analytic Performance Estimation (APE) model\, (4) a methodology to evaluate a platform’s benefit for a set of applications\, (5) two novel algorithms\, Dynamic Score Selection (DSS) and GenetIc Domain Exploration (GIDE)\, to allocate a domain-specific platform to maximize the throughput across domain applications under certain constraints\, and (6) Multi-Granularity Domain DSE (MG-DmDSE) to extend DSE considering multi-granularity functionality similarity in the platform allocation and application binding. \n 
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-jinghan-zhang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210218T150000
DTEND;TZID=America/New_York:20210218T160000
DTSTAMP:20260426T205009
CREATED:20210125T194649Z
LAST-MODIFIED:20210125T194649Z
UID:4702-1613660400-1613664000@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Shadi Emam
DESCRIPTION:PhD Dissertation Defense: Devices and Methods for Diagnosis from Exhaled Breath \nShadi Emam \nLocation: 325 DA \nAbstract: A novel electrochemical gas sensor was developed with a variety of applications. The sensor is composed of four layers: Silicon substrate\, Chromium\, Graphene-Prussian blue\, and molecularly imprinting polymer (MIP). Molecular imprinting technology is a technique to polymerize around a template molecule. Subsequent removal of the template molecule leaves cavities in the polymer matrix with an affinity for a chosen template molecule. The sensor was applied toward the early diagnosis of Alzheimer’s disease first. Three sensors were fabricated to detect three biomarkers of Alzheimer’s disease in the exhaled breath. The sensors were tested against three cohorts of rats: young healthy control\, old on high fat/high sugar diet and\, old on high fat/high sugar with Alzheimer’s human gene APOE4. The sensor only detected the 3 biomarkers from the exhaled breath of the rats from the last cohort. The MRI results of the rats’ brain also confirmed the sensors readings. The sensors were then applied toward the diagnosis of lung cancer at an early stage and detection of controlled substances in the air/exhaled breath/body fluid. Recently\, sensors were fabricated and tested to detest SARS-CoV-2. With some modification to the basic and structure of the sensors\, 3 generations of SARS-CoV-2 sensors were developed and tested. The second generation was developed in order to enhance the sensitivity of the sensors. By proper functionalization of the graphene layer\, the sensitivity of the sensors increased 80\,000 times. The third generation of the sensors was fabricated with the goal of selectivity and using functional monomers. These sensors were tested against bovine serum albumin (BSA)\, water\, phosphate buffer solution (PBS)\, the Middle East respiratory syndrome (MERS)\, severe acute respiratory syndrome (SARS)\, Ebola\, and flu virus. The third generation of sensors is highly selective and consistent compared to the previous generation. While the first generation sensor was 37.5% selective and 61% sensitive\, the third generation sensor was 75% selective and 80% selective. \n 
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-shadi-emam/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210217T110000
DTEND;TZID=America/New_York:20210217T120000
DTSTAMP:20260426T205009
CREATED:20210208T214141Z
LAST-MODIFIED:20210208T214141Z
UID:4718-1613559600-1613563200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Andre Langner
DESCRIPTION:PhD Proposal Review: Multi-area Distribution State Estimation Using a Virtual Reference \nAndre Langner \nLocation: Teams Link \nAbstract: State Estimation (SE) is the backbone of modern Energy Management System due to its capability of processing real-time measurements and provide reliable information to system operators. Since its introduction to power systems in the ’70s\, SE has been widely used in transmission networks. Distribution grids\, on the other hand\, lack sufficient number of real-time measurements\, and for that reason\, SE has not been widely implemented on these systems. The recent increase in the number of renewable energy sources connected to the grid at lower voltage levels\, the advent of Distribution Automation\, and Smart Grids necessitate closer monitoring of distribution networks. Thus\, forcing utilities to upgrade their operations and deploy Advanced Distribution Management Systems. Therefore\, Distribution System State Estimation (DSSE) is paramount to provide real-time monitoring of active distribution grids. In the first part of this proposal\, a three-phase distribution system state estimator is presented\, especially for highly unbalanced networks. In the second part\, the Multi-area State Estimation (MASE) approach is proposed to distribution systems\, by a partition into non-overlapping areas\, aiming at reducing the overall execution time. Furthermore\, it is also proposed to combine MASE along with the so-called Generalized State Estimation to identify topology errors causing divergence in the state estimation process.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-andre-langner/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210209T150000
DTEND;TZID=America/New_York:20210209T160000
DTSTAMP:20260426T205009
CREATED:20210206T004624Z
LAST-MODIFIED:20210206T004624Z
UID:4715-1612882800-1612886400@ece.northeastern.edu
SUMMARY:ECE Seminar: David M. Rosen
DESCRIPTION:Title: Provably Sound Perception for Reliable Autonomy \nDavid M. Rosen \nLocation: Zoom Link \nAbstract:  Machine perception — the ability to construct accurate models of the world from raw sensor data — is an essential capability for mobile robots\, supporting such fundamental functions as planning\, navigation\, and control.  However\, the development of algorithms for robotic perception that are both *practical* and *reliable* presents a formidable challenge: such methods must be capable of solving complex estimation tasks in real-time on resource-limited mobile platforms\, while remaining robust to challenges such as sensor noise\, uncertain or misspecified perceptual models\, and potentially contaminated measurements. In this talk\, I show how one can meet these challenges through the design of practical perception methods that are both *computationally efficient* and *provably sound*\, focusing on the foundational problem of spatial perception.  I begin with a brief introduction to pose-graph optimization (PGO): this problem lies at the core of many fundamental spatial perception tasks (including robotic mapping\, sensor network localization\, and 3D visual reconstruction)\, but is high-dimensional and nonconvex\, and therefore challenging to solve in general. Nevertheless\, I show how one can leverage convex relaxation to efficiently recover *exact\, certifiably optimal* PGO solutions in a noise regime that encompasses most practical robotics and computer vision applications.  Our algorithm\, SE-Sync\, is the first practical method provably capable of recovering correct (globally optimal) PGO solutions. Next\, I address the design of machine learning methods for spatial perception\, focusing on the fundamental problem of rotation estimation.  I show that topological obstructions can actually prevent deep neural networks (DNNs) employing common rotation parameterizations (e.g. quaternions) from learning to estimate widely-dispersed rotation targets\, as is required in (for example) object pose estimation. I then describe a novel parameterization of 3D rotations that overcomes this obstruction\, and that supports an explicit notion of uncertainty in our DNNs’ predictions.  Experiments confirm that (as predicted by theory) DNNs employing this representation achieve superior accuracy and reliability when applied to object pose estimation\, and that their predicted uncertainties enable the reliable identification of out-of-distribution test examples (including corrupted inputs). Finally\, I will conclude with a discussion of future directions that aim to unify provably sound estimation and learning methods\, thereby enabling the creation of perception systems with both the *robustness* and *adaptability* necessary to support reliable long-term autonomy in the real world. \nSpeaker Bio:  David M. Rosen is a postdoctoral associate in the Laboratory for Information and Decision Systems at the Massachusetts Institute of Technology.  His research addresses the design of practical provably robust methods for machine perception\, using a combination of tools from optimization\, geometry\, algebra\, and probabilistic inference.  He holds the degrees of BS in Mathematics from the California Institute of Technology (2008)\, MA in Mathematics from the University of Texas at Austin (2010)\, and ScD in Computer Science from the Massachusetts Institute of Technology.  Prior to joining LIDS\, he was a Research Scientist at Oculus Research (now Facebook Reality Labs) in Seattle.His work has been recognized with a Best Paper Award at the 2016 International Workshop on the Algorithmic Foundations of Robotics\, an RSS Pioneer Award at Robotics: Science and Systems 2019\, and a Best Student Paper Award at Robotics: Science and Systems 2020.
URL:https://ece.northeastern.edu/event/ece-seminar-david-m-rosen/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210129T150000
DTEND;TZID=America/New_York:20210129T160000
DTSTAMP:20260426T205009
CREATED:20210129T000716Z
LAST-MODIFIED:20210129T000716Z
UID:4713-1611932400-1611936000@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Sadjad Asghari Esfeden
DESCRIPTION:PhD Proposal Review: SpatioTemporal Prediction of Object Handover for Human Robot Collaboration \nSadjad Asghari Esfeden \nLocation: Zoom Link \nAbstract: Predicting human behaviour in video is one of the challenging problems in computer vision. In order for robots to be able to interact with humans they need to understand human intent. We study the problem of object handover\, where a robot tries to follow its collaborator’s movement as well as the object of interest to grasp the object in a human-like behavior. Therefore\, the robot should predict a moving object’s time and location of handover. We propose a computer vision based algorithm to help robot understand its environment\, detect\, track\, and predict object and human motions during the task of handover. The perception system enables robot to move towards the locus of handover before it occurs\, and refine its motion when there is a change in human intention. \n 
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-sadjad-asghari-esfeden/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210129T111500
DTEND;TZID=America/New_York:20210129T124500
DTSTAMP:20260426T205009
CREATED:20210120T012526Z
LAST-MODIFIED:20210120T012526Z
UID:4695-1611918900-1611924300@ece.northeastern.edu
SUMMARY:ECE Seminar: Dr. Yi Li
DESCRIPTION:Seminar Title: Coherent Information Processing with Onchip Hybrid Magnonics \nLocation: Zoom Link \nAbstract: Hybrid dynamic systems have recently attracted great attention due to their applications in quantum computing\, communications\, and sensing. In particular\, they provide a new paradigm for combining platforms and devices that can perform different tasks such as storing\, processing\, and transmitting coherent states. In this talk\, I will discuss the potential in quantum information processing brought by magnon—the collective excitations of magnetization. Magnons exhibit a few key features making them highly competitive in quantum information processing\, namely their strong coupling to microwave photons\, their extraordinary tunability and flexibility for chip-based circuit integration\, as well as their excellent compatibility for coupling with various dynamic media such as mechanical excitations and optical photons for coherent quantum transduction. In the first part\, I will demonstrate strong coupling between magnons and microwave photons by integrating magnetic devices with coplanar superconducting resonators on Si substrate. The on-chip integration of such superconducting hybrid magnonic circuits provides great flexibility in circuit design\, device scalability as well as being extended to the circuit quantum electrodynamics for qubit controls. In the second part\, I will cultivate the dynamic interaction between magnons and excitations with far different frequencies\, such as mechanical excitations and optical photons\, for advanced sensing of magnetic excitations. The device level coupling between diverse excitations suggest a compelling candidate of magnons for building a universal coherent transducer in bridging different quantum systems for extended functionality. \nSpeaker Bio: Dr. Yi Li is currently a postdoc in the Superconductivity and Magnetism Group at Argonne National Laboratory. He has obtained his B.S. degree in Physics from Peking University (2009) and his Ph.D. degree in Materials Science & Engineering from Columbia University (2015). Prior to Argonne he has been a postdoc at CEA Saclay in France for two years (2015-2017). Yi Li’s research focuses on building hybrid quantum magnonic circuits based on microwave superconducting circuits and magnetic devices for their applications in quantum information processing. Yi was the recipient of the Postdoctoral Performance Awards (2020) at Argonne National Laboratory and the IEEE Chicago “Distinguished R&D” Award (2020) for his pioneering work on magnon-photon coupling in superconducting resonator for Quantum Information Science. For more information about his work please visit: https://sites.google.com/view/prc1988.
URL:https://ece.northeastern.edu/event/ece-seminar-dr-yi-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210128T141000
DTEND;TZID=America/New_York:20210128T153000
DTSTAMP:20260426T205009
CREATED:20210120T012302Z
LAST-MODIFIED:20210120T012302Z
UID:4691-1611843000-1611847800@ece.northeastern.edu
SUMMARY:ECE Seminar: Dr. Xufeng Zhang
DESCRIPTION:Seminar Title: Advancing Quantum Information Science With Hybrid Cavity Magnonics \nLocation: Zoom Link \nAbstract: With recent demonstration of quantum computers and quantum communication\, quantum technologies have started to change our world in an unprecedented way. To fully explore the power of quantum information science and technology\, it is critical to further combine discrete quantum elements and build distributed quantum networks. However\, this poses significant technical challenges because the quantum coherence can be easily destroyed as the weak quantum signal propagates across different systems. In this talk\, I will show that magnons — the collective excitations of magnetization — provide a promising solution for efficiently transducing quantum information among different systems while preserving the delicate quantum coherence. Specifically\, cavity magnonics can be conveniently hybridized with other physical platforms that operate in the microwave\, mechanical and optical domains because of its exceptional compatibilities with them. Moreover\, thanks to the large spin density in our magnonic system\, the interactions between magnons and the information carriers used in other systems (such as photons and phonons) are drastically boosted\, providing elevated protection for the signal coherence. Most importantly\, the excellent tunability of magnons permits unparalleled manipulation for the signal transduction. Therefore\, high-fidelity magnon-based signal transduction can be achieved. I will finish the talk by describing opportunities and our efforts toward quantum operations and on-chip integration of hybrid cavity magnonics. \nSpeaker Bio: Dr. Xufeng Zhang has been working as an assistant scientist at the Center for Nanoscale Materials\, Argonne National Laboratory\, and CASE fellow of the University of Chicago since 2018. Dr. Zhang received his Ph.D. in Engineering from Yale University in 2016\, where he worked on hybrid magnonic devices in Prof. Hong Tang’s group. He is the winner of the Henry Prentiss Becton Graduate Prize for his exceptional graduate research at Yale University. After graduation he joined Argonne National Laboratory as the Nikola-Tesla postdoctoral fellow. His research interests include hybrid quantum devices\, magnon spintronics\, integrated photonics\, nanomechanics\, and high frequency devices.
URL:https://ece.northeastern.edu/event/ece-seminar-dr-xufeng-zhang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210128T123000
DTEND;TZID=America/New_York:20210128T133000
DTSTAMP:20260426T205009
CREATED:20210125T194810Z
LAST-MODIFIED:20210125T194810Z
UID:4703-1611837000-1611840600@ece.northeastern.edu
SUMMARY:ECE Seminar: Seungmoon Song
DESCRIPTION:Seminar Title: Toward predictive simulation of human movement – for assistive devices and rehabilitation treatment  \nLocation: Zoom Link \nAbstract: I will present my research towards predictive simulations of human movement for assistive devices and rehabilitation treatment. First\, I will talk about a neuromechanical control model based on simple reflexes. The model can generate diverse locomotion behaviors\, react to perturbations similarly to humans\, and explain why walking performance declines with age. However\, as the model was focused on low-level motor control primarily for steady locomotion behaviors\, extending and verifying the model for more complex movements and reactions is necessary for producing reliable predictions for novel scenarios. In the later part\, I will present recent projects on conducting a human experiment with gait assistive exoskeletons and using deep reinforcement learning to developing complex control models. In the experimental study\, we found using human-in-the-loop optimization that it is possible to substantially increase self-selected walking speed with ankle exoskeletons. Regarding deep reinforcement learning\, we organized the Learn to Move competition\, where participants developed controllers for a human musculoskeletal simulation model. The competition has been organized at the NeurIPS conference since 2017 and has attracted over 1300 teams from around the world. At last\, I will discuss my plan of incorporating rigorous experimental validations and advanced computational techniques toward neuromechanical models that could change the way we design rehabilitation treatment and study human movement. \nSpeaker Bio: Seungmoon Song is a postdoctoral researcher in the Mechanical Engineering Department of Stanford University. He is also a recipient of an NIH K99 award and the lead organizer of the NeurIPS: Learn to Move competition. His research focuses on modeling the neuromechanics of human movement and applying it to rehabilitation and robotics. As a postdoc\, he is working on improving human walking performance with exoskeleton assistance using human-in-the-loop optimization. During his Ph.D. at the Robotics Institute of Carnegie Mellon University\, he proposed a reflex-based control model that could explain various aspects of human locomotion including diverse locomotion behaviors of healthy adults\, responses to unexpected disturbances\, and performance degradation in aging.
URL:https://ece.northeastern.edu/event/ece-seminar-seungmoon-song/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210127T150000
DTEND;TZID=America/New_York:20210127T170000
DTSTAMP:20260426T205009
CREATED:20210126T230728Z
LAST-MODIFIED:20210126T230728Z
UID:4707-1611759600-1611766800@ece.northeastern.edu
SUMMARY:AIX Seminar
DESCRIPTION:Please join us for our first AIX Seminar. \nLocation: Zoom Link \nSeminar 1 title: Allostasis and Interoception: Brain-Body Interactions and Implications for Robotics \nSpeakers: Dr. Karen Quigley\, Northeastern University\, and Dr. Erin Reilly\, Veteran Affairs \nAbstract: For much of the history of psychology\, sensation\, perception\, action\, emotion\, and cognition were studied as if they were separate\, biologically-defined faculties — they are not. A prominent current neuroscientific perspective (and variants thereof) suggest that a brain runs an internal\, predictive model or simulation of itself in the world. This model supports all functions achieved by a brain\, and in this view\, predictions constitute the internal model. Our lab has marshaled neuroanatomical evidence that predictions arise from visceromotor control regions in the brain to support anticipated action and other metabolically-costly functions such as learning. Collectively\, these anticipatory regulatory processes are called allostasis. Allostasis is the major task of a brain\, which utilizes 20% of the energetic budget of a human. A brain also requires a body\, which is the effector by which the brain supports maintenance of its own energetic needs. The internal model also is modified by prediction error arising from unanticipated inputs from both exteroceptive (e.g.\, vision) and interoceptive (e.g.\, viscerosensory) sources. Interoceptive sensations provide critical information to the brain about the status of the body\, enabling motor and visceromotor actions that can most efficiently support the brain’s energetic needs. Understanding these biological realities can bring new ideas to both the design of robots\, and also to our understanding of how to optimize humans-robot interactions. \n  \nSeminar 2 title: Improving Interaction using Intelligence \nSpeaker: Dr. Jaime Ruiz\, University of Florida \nAbstract: Adding intelligence to user interfaces provides unique opportunities to improve the way users interact with computing systems. In this talk\, I will give a broad overview of the types of projects undertaken by may lab. I will also highlight several projects that aim to use neration of multimodal interfaces.
URL:https://ece.northeastern.edu/event/aix-seminar/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210127T130000
DTEND;TZID=America/New_York:20210127T142000
DTSTAMP:20260426T205009
CREATED:20210120T012431Z
LAST-MODIFIED:20210120T012431Z
UID:4693-1611752400-1611757200@ece.northeastern.edu
SUMMARY:ECE Seminar: Dr. Jun Xiao
DESCRIPTION:Seminar Title: 2D Materials For Next-Generation Information Technology: From Functional Material Miniaturization To Energy-Efficient Phase Engineering \nLocation: Zoom Link \nAbstract: The emergence of artificial intelligence and 5G technology is transforming our world with novel applications such as the Internet of Things\, smart manufacturing\, and AI-empowered medical care. However\, this information revolution sets a massive demand for information capacity and energy supply. Such a big challenge urges innovations in device engineering and its material building blocks to boost information capacity and reduce energy consumption. In this talk\, I will focus on the exciting progress of the emergent 2D layered materials and their device engineering in this direction. First\, I will introduce our discovery of intrinsic 2D out-of-plane ferroelectricity in semiconducting In2Se3\, which holds great promise for ferroelectric device miniaturization. I will then present our electrostatic doping control innovation as a new energy-efficient mechanism for structural phase engineering in layered materials. I will further show how we utilize such technology to invent the non-volatile Berry curvature memory\, a new type of energy-efficient quantum devices. Inspired by these findings and techniques\, I will also briefly discuss the exciting future opportunities of leveraging the structure-property relationship and light-matter interactions in layered quantum materials and devices to boost the translation of novel quantum notions into technological advantages for energy-efficient neuromorphic computing\, robust quantum processing\, and biosensing. \nSpeaker Bio: Dr. Jun Xiao is a postdoctoral scholar working with Prof. Aaron Lindenberg in the Department of Materials Science & Engineering and Prof. Tony Heinz in the Department of Applied Physics at Stanford University. He earned his Ph.D. in Applied Science and Technology from UC Berkeley (2018) under Prof. Xiang Zhang’s supervision. His research experience and interests focus on leveraging quantum materials and devices for energy-efficient neuromorphic engineering\, robust quantum computing\, THz sensing\, and high-throughput manufacturing.
URL:https://ece.northeastern.edu/event/ece-seminar-dr-jun-xiao/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210126T130000
DTEND;TZID=America/New_York:20210126T142000
DTSTAMP:20260426T205009
CREATED:20210120T002827Z
LAST-MODIFIED:20210120T002827Z
UID:4689-1611666000-1611670800@ece.northeastern.edu
SUMMARY:ECE Seminar: Dr. Subhanshu Gupta
DESCRIPTION:Seminar Title: The Coming Together of Silicon Circuits and AI for Next Generation Wireless Communications\, IOT\, and Quantum Applications \nLocation: Zoom Link \nAbstract: The ubiquity of silicon-based devices around us have paved the way for fast communications\, personalized healthcare\, and terabits/sec computing unthinkable few decades ago. The question of what’s next for silicon-based circuits and systems gets interesting with the end (or as some say\, slowing) of technology scaling but the emphasis on wireless infrastructure\, internet-of-things\, and quantum computing leveraging advances in Artificial Intelligence (AI) has brought forward several new fundamental challenges. This talk will harness recent research in silicon-based circuits and systems and AI to bridge the fundamental gap in the underlying physics of large-scale wireless communications and cryogenics harmoniously with the outside environment. The first part of the talk will present reconfigurable spatial signal processors for large-scale antenna arrays that can achieve unprecedented resolution both in near-field and far-field. Introducing discretetime delay compensating techniques with large range-to-resolution ratios and AI-optimized radio frontend solutions\, we will demonstrate high data-rates with wide modulated bandwidths suited to 5G/Beyond-5G wireless communications. The second part of the talk will present AI optimizers to solve practical issues in well-known high-speed and high-resolution superconducting/quantum circuits for the first time. We will look into the design of an energy-efficient and low-latency optimizer that greatly reduces the calibration time and enabling heterogeneous cryogenic platforms coupling speed and energy-efficiency of Josephson Junctions with area-efficiency of CMOS. The third part of the talk will present silicon-based systems-on-chip that enables large-scale IoT networks combining advances in self powered radios with energy harvesters tapping into the surrounding environments. We will conclude this talk with custom integrated cryoelectronics and multi-antenna testbeds for modeling and design of high-speed cryoelectronic processors and spatial signal processors with diverse spatial functions such as beam training and RFI cancellation for future quantum computing and distributed antenna arrays of tomorrow. \nSpeaker Bio: Dr. Subhanshu Gupta received the B.E. degree from the National Institute of Technology (NIT) at Tiruchirappalli\, Tiruchirappalli\, India\, in 2002\, and the M.S. and Ph.D. degrees from the University of Washington\, Seattle\, WA\, USA\, in 2006 and 2010\, respectively. He is currently an Assistant Professor of electrical engineering and computer science with Washington State University\, Pullman\, WA\, USA. He has held industrial positions at Maxlinear (Irvine\, CA) where he worked on wideband transceivers for SATCOM and infrastructure applications. Subhanshu is a recipient of the National Science Foundation CAREER Award in 2020\, the Department of Defense DURIP award in 2021\, and the Cisco Faculty Research Award in 2017. He and his group has also been nominated and awarded multiple student awards including Analog Devices Outstanding Student Designer Award in 2008\, the IEEE RFIC Symposium Best Student Paper Award (third place in 2011 and nominee in 2020)\, and the IEEE Applied Superconductivity Conference (nominee in 2020). Subhanshu serves as an Associate Editor for the IEEE Transactions on Circuits and Systems – I for the term 2020-21 and also served as a guest editor for IEEE Design & Test of Computers in 2019. His research interests include large-scale phased arrays and wideband transceivers\, low-power time-domain circuits and systems\, and statistical hardware optimization for next-generation wireless communications\, internet-of-things\, and quantum applications.
URL:https://ece.northeastern.edu/event/ece-seminar-dr-subhanshu-gupta/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210125T123000
DTEND;TZID=America/New_York:20210125T133000
DTSTAMP:20260426T205009
CREATED:20210114T234911Z
LAST-MODIFIED:20210114T234911Z
UID:4685-1611577800-1611581400@ece.northeastern.edu
SUMMARY:ECE Seminar: Kris Dorsey
DESCRIPTION:Seminar Title: Challenges and opportunities in design in tunable\, soft mechanical sensors. \nLocation: Zoom Link \nAbstract: Physically-soft mechanical sensors are poised to unlock exciting new applications in wearable devices\, robotics\, and human-machine interfaces. Typically with these sensors\, tuning their properties through the device geometry is a challenge. A promising development in soft mechanical sensors is hierarchically-patterned structures within the sensor\, which enables both deformation selectivity and the ability to tune\, and potentially reconfigure\, sensing properties. Dorsey will discuss challenges and recent work related to designing and fabricating hierarchically-patterned sensors\, including origami-patterned sensors. Dorsey will also present work in enhancing the stability and mechanical selectivity of stretchable sensors\, and discuss applications for such sensors in wearable healthcare applications and soft robotics.    \nSpeaker Bio: \nKris Dorsey is an assistant professor of engineering in the Picker Engineering Program at Smith College. She was a President’s Postdoctoral Fellow at the University of California\, Berkeley and University of California\, San Diego. Dr. Dorsey graduated from Carnegie Mellon University with a Ph.D. in Electrical and Computer Engineering and earned her Bachelors of Science in Electrical and Computer Engineering from Olin College. She founded The MicroSMITHie Lab at Smith College to investigate micro- and miniature-scale sensor design and to prepare undergraduates for graduate study in engineering. Her current research interests include novel morphology soft sensors\, stability concerns for soft-material sensors\, and sensors for soft robots and wearable medical devices.  Dr. Dorsey has co-authored several publications on hyper–elastic strain sensors\, novel soft lithography processes\, and the stability of gas chemical sensors. In 2019\, she received the NSF CAREER award.  
URL:https://ece.northeastern.edu/event/ece-seminar-kris-dorsey/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210121T150000
DTEND;TZID=America/New_York:20210121T160000
DTSTAMP:20260426T205009
CREATED:20210114T234813Z
LAST-MODIFIED:20210114T234813Z
UID:4683-1611241200-1611244800@ece.northeastern.edu
SUMMARY:ECE Seminar: Mojtaba Sharifi
DESCRIPTION:Seminar Title: Research Background and Experience in Medical Robotics\,Human-Robot Interaction\, and Collaborative/Assistive Devices \nLocation: Zoom Link \nAbstract: In this talk\, Mojtaba Sharifi will go over the research projects he has done in the field of Medical Robotics\, Human-Robot Interaction (HRI)\, and Collaborative/Assistive Robotics during the past ten years. His presentation is organized in three sections\, which cover his research achievements chronologically from his MSc to the current Postdoc position. The first one is devoted to his main research area during the MSc and Ph.D. programs on the “Control of HRI: Medical Robotic and Tele-Robotic Systems”. After that\, he will touch upon his recent contribution made on the “Interaction Learning and Autonomy for Collaborative Robots and Assistive Exoskeletons”\, during the postdoctoral research. The last part of this presentation is dedicated to his past and ongoing projects on the “Human Musculoskeletal Modeling & Soft Exoskeletons for Safe HRI”\, for biomedical applications. Throughout this presentation\, the theoretical and experimental aspects of these studies will be elaborated on.   \n Biography: Mojtaba Sharifi received the B.Sc. degree in Mechanical Engineering from Shiraz University\, Shiraz\, Iran\, in 2010 and the M.Sc. degree in Mechanical Engineering from Sharif University of Technology\, Tehran\, Iran\, in 2012. He conducted a collaborative project in the Telerobotic and Biorobotic Systems Lab of the University of Alberta\, Canada\, from 2015 to 2016 as a visiting doctoral researcher. Then\, he earned a Ph.D. degree in the School of Mechanical Engineering at Sharif University of Technology\, Tehran\, Iran\, in 2017. Mojtaba also performed an interdisciplinary research project on the design and fabrication of new soft robotic actuators in 2019 as a research associate at the University College London\, UK. He has published more than 40 papers and chapters in high-quality journals\, conferences\, and books on his interdisciplinary theoretical-experimental research. His research interests include the design and implementation of autonomous control systems\, physical human-robot interaction (pHRI)\, medical robotics (rehabilitation\, surgery\, and imaging)\, control of musculoskeletal systems\, impedance control and learning\, haptics\, collaborative– and tele-robotics\, soft robotics\, wearable\, and assistive mechatronic systems (exoskeleton and prosthesis). Mojtaba is the recipient of a postdoctoral fellowship award\, working at the Department of Electrical and Computer Engineering and the Department of Medicine\, University of Alberta\, Canada. He is now investigating new autonomous control policies employing adaptive learning rules for the Central Pattern Generation (CPG) to update and personalize the human locomotion\, which is to be tracked by a lower-limb powered exoskeleton with optimized torque and FES inputs. He is also leading a project that aims to design\, fabricate\, and implement soft robotic systems for safely assisting people with upper-limb weakness.  
URL:https://ece.northeastern.edu/event/ece-seminar-mojtaba-sharifi/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210121T113000
DTEND;TZID=America/New_York:20210121T123000
DTSTAMP:20260426T205009
CREATED:20210119T210023Z
LAST-MODIFIED:20210119T210032Z
UID:4687-1611228600-1611232200@ece.northeastern.edu
SUMMARY:ECE Seminar: Paolo Santi
DESCRIPTION:Seminar Title: IoT: An Enabling Technology for Designing Better Cities \nLocation: Zoom Link \nAbstract:  IoT is rapidly evolving into an enabling technology with countless potential applications. In this seminar\, we will explore how IoT technology can help in the design of better cities\, starting from the re-design of city systems and infrastructures (mobility\, power grid\, etc.) based on large- scale data acquisition. We will highlight the challenges related to systems where real-time actuation is a need\, as well as those related to design problems where actuation occurs on a longer time scale\, such as urban infrastructure planning. We will then show how IoT technology can be used also to gain a deeper understanding of how humans interact with existing urban systems. This deeper comprehension of human behavior is key to design systems that are not only “algorithmically” efficient\, but that also conform to fundamental human behavioral patterns. \nBio: Paolo Santi is Principal Research Scientist at MIT Senseable City Lab and Research Director at the Istituto di Informatica e Telematica\, CNR\, Pisa\, Italy. Dr. Santi holds a “Laurea” degree and the PhD in computer science from the University of Pisa\, Italy. Dr. Santi is a member of the IEEE Computer Society and has recently been recognized as\nDistinguished Scientist by the Association for Computing Machinery. His research interest is in the modeling and analysis of complex systems ranging from wireless multi hop\nnetworks to sensor and vehicular networks and\, more recently\, smart mobility and intelligent transportation systems. In these fields\, he has contributed more than 160 scientific papers and two books.
URL:https://ece.northeastern.edu/event/ece-seminar-paolo-santi/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210119T123000
DTEND;TZID=America/New_York:20210119T133000
DTSTAMP:20260426T205009
CREATED:20210113T231029Z
LAST-MODIFIED:20210113T231029Z
UID:4674-1611059400-1611063000@ece.northeastern.edu
SUMMARY:ECE Seminar: Maria Kyrarini
DESCRIPTION:Seminar Title: Robot Learning from Demonstrations for Human-Robot Synergy \nLocation: Zoom Link \nAbstract: Imagine a world where robots support and assist us in our everyday professional and personal life. To achieve a successful Human-Robot Synergy\, robots will need to learn new tasks from humans seamlessly\, to act on the new knowledge\, and easily adapt to new situations and people around them. Robot Learning from Demonstrations (RLfD) is a method used to enhance the ability of robots to be easily teachable by people\, a vital ability for a successful Human-Robot Synergy. RLfD enables non-expert users to ‘program’ a robot by simply guiding the robot through a task. However\, current research in RLfD tends to disconnect low-level motor control and high-level symbolic reasoning capabilities. In this talk\, I will present a novel RLfD framework\, which enhances a robot’s abilities to learn and perform the sequences of actions for object manipulation tasks (high-level learning) and\, simultaneously\, learn and adapt the necessary trajectories for object manipulation (low-level learning). Then\, I will present a ‘hands-free’ human-robot interaction modality that enables individuals with severe motor impairments\, such as quadriplegia\, to teach a robot an assistive manipulation task. I will discuss how the presented RLfD framework was evaluated in a dual-arm industrial robot for assembly tasks and in an assistive robotic manipulator for providing a drink. The experimental results demonstrate the potential of the developed robot learning framework to enable continuous human-robot synergy in industrial and assistive applications. Finally\, I will conclude the talk with a brief discussion of my ongoing work and future research plans. \nSpeaker Bio: Maria Kyrarini is a postdoctoral research fellow at the University of Texas at Arlington under the advisement of Professor Dr. Fillia Makedon. She is also the assistant director of the Heracleia Human-Centered Computing Lab. In 2019\, Maria received her Ph.D. in Engineering from the University of Bremen under the supervision of Professor Dr.-Eng. Axel Gräser. The title of her Ph.D. thesis is: “Robot learning from human demonstrations for human-robot synergy”. Before that\, she received her M.Eng. degree in Electrical and Computer Engineering and her M.Sc. degree in Automation Systems both from the National Technical University of Athens (NTUA) in 2012 and 2014\, respectively. Her primary research interests are in the fields of Robot Learning from Human Demonstrations\, Human-Robot Interaction\, and Assistive Robotics with a special focus on Enhancing Human Performance.
URL:https://ece.northeastern.edu/event/ece-seminar-maria-kyrarini/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210115T110000
DTEND;TZID=America/New_York:20210115T120000
DTSTAMP:20260426T205009
CREATED:20210114T214057Z
LAST-MODIFIED:20210114T214057Z
UID:4682-1610708400-1610712000@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Lorenzo Bertizzolo
DESCRIPTION:PhD Proposal Review: Software-Defined Wireless Networking for 5G and Beyond: From Indoor Cells to Distributed Aerial Swarms \nLorenzo Bertizzolo \nLocation: MS Teams Link \nAbstract: While Software-defined Networking is a consolidated and widely adopted concept in fixed infrastructure\, its adoption to the wireless domain has been limited by some fundamental challenges. Different from wired deployments\, wireless stacks are characterized by tight inter-dependencies among their protocol stack layers (known as vertical coupling) and the nodes sharing the wireless channel (horizontal coupling). These effects combined undermine the implementation of i) Control plane / data plane separation\, and ii) Control of multiple data planes from a separate controller; the two founding principles of SDN. Recent developments in spectrum access technology\, however\, made it possible to reconfigure multiple layers of the wireless stack at once. This paved the way for the development of cross-layer algorithms toward the implementation of control plane / data plane separation for wireless. Moreover\, cross-layer algorithms can be employed together with distributed control theory to implement distributed and scalable control for wireless networks\, this way overcoming the difficulties of implementing separate control for multiple data planes.\nThis proposal exploits full-stack programmability to propose\, design\, and implement new cross-layer algorithms that reconfigure the wireless stack at multiple layers and in real-time. Through the systematic use of cross-layer optimization\, closed-loop control\, and dynamic network adaptation\, this proposal contributes to the development of a wide range of technological innovations for spectrum access\, to bring the benefits of Software-defined networking to the wireless domain. We present a closed-loop PHY/MAC cross-layer control algorithm to enable spectrally-efficient OFDM spectrum access in Wi-Fi populated bands. Then\, we exploit the technological innovations of a 5G Open-RAN infrastructure and propose a control system that enables broadband 5G connectivity for aerial cellular users that dynamically adapts to the changing network conditions like the time-changing distribution of pedestrian users in the surrounding. At millimeter-wave frequencies\, we propose a cross-domain control algorithm that reduces the initial access latency in standalone high-frequency systems and obtains higher spectral efficiency for aerial links. Finally\, we empower the SDN paradigm to bring network management to distributed aerial swarms. Through full-stack software programmability and programmable motion control\, we implement scalable wireless network management for distributed aerial swarms.\nWe conclude the proposal with an overview of the requirements and design principles for next-generation wireless testing platforms to support software-programmable spectrum access. \n 
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-lorenzo-bertizzolo/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210113T110000
DTEND;TZID=America/New_York:20210113T120000
DTSTAMP:20260426T205009
CREATED:20210112T001703Z
LAST-MODIFIED:20210112T001703Z
UID:4667-1610535600-1610539200@ece.northeastern.edu
SUMMARY:ECE Seminar: Elahe Soltanaghaei
DESCRIPTION:Location: Zoom Link \nSeminar Title: Sensing the Physical World using Pervasive Wireless Infrastructure \nAbstract: The promise of IoT and emerging applications such as smart cities\, autonomous vehicles\, and mixed reality that are tightly coupled\nwith the physical environment pushes the demand for high-fidelity sensing. Meanwhile\, we are also seeing advances in wireless technologies such as Millimeter-wave and Massive MIMO systems that can transform the role of wireless networks from a pure communication medium to a pervasive sensing infrastructure. Elahe’s research investigates the synergy of wireless and sensing by applying signal processing and machine learning techniques to low-level RF signals. This talk will focus on how to map the natural interactions of wireless signals with the environment into physical and behavioral measurements for human sensing\, device localization and object tracking. She will then discuss her ongoing research on designing an RF-equivalent of optical retro-reflectors for automotive applications and will conclude with her\nroadmap toward omni-present sensing for the wireless embedded systems of the future. \nSpeaker Bio: Elahe Soltanaghaei is a postdoctoral researcher at Carnegie Mellon University in the Wireless\, Sensing\, and Embedded Systems\n(WiSE) lab. She received her PhD in Computer Science from University of Virginia. Her research spans the areas of wireless sensing and networking with applications in IoT and Cyber-Physical Systems. Reflecting the multidisciplinary nature of her research\, her work has been published in premier conferences and journals in the areas of mobile computing\, wireless networks\, and energy and infrastructure. She is the recipient of 2020 ACM SIGMOBILE Dissertation Award\, 2019 EECS Rising Stars\, and 2019 N2-Women Young Researcher Fellowship.
URL:https://ece.northeastern.edu/event/ece-seminar-elahe-soltanaghaei/
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