BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Electrical & Computer Engineering - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://ece.northeastern.edu
X-WR-CALDESC:Events for Department of Electrical & Computer Engineering
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20190310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20191103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20200308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20201101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210322T100000
DTEND;TZID=America/New_York:20210322T110000
DTSTAMP:20260427T132000
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:20260427T132000
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:20260427T132000
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:20260427T132000
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:20260427T132000
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:20260427T132000
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:20260427T132000
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:20260427T132000
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:20260427T132000
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:20260427T132000
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:20260427T132000
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:20260427T132000
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:20260427T132000
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:20260427T132000
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:20260427T132000
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210121T150000
DTEND;TZID=America/New_York:20210121T160000
DTSTAMP:20260427T132000
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210121T113000
DTEND;TZID=America/New_York:20210121T123000
DTSTAMP:20260427T132000
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210119T123000
DTEND;TZID=America/New_York:20210119T133000
DTSTAMP:20260427T132000
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210115T110000
DTEND;TZID=America/New_York:20210115T120000
DTSTAMP:20260427T132000
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210113T110000
DTEND;TZID=America/New_York:20210113T120000
DTSTAMP:20260427T132000
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210112T100000
DTEND;TZID=America/New_York:20210112T110000
DTSTAMP:20260427T132000
CREATED:20201222T022618Z
LAST-MODIFIED:20201222T022618Z
UID:4647-1610445600-1610449200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Haoqing Li
DESCRIPTION:PhD Proposal Review: Robust Processing against Interferences in GNSS Navigation \nHaoqing Li \nLocation: Zoom Link \nAbstract: Satellite-based navigation is prevalent as positioning applications among our lives\, how-ever\, this high reliance brings potential threats when different interferences and jamming signals are considered. Jamming devices\, although illegal in many countries\, can be easily to get. Those devices can broadcast high-power jamming signals in Global Navigation Satellite System (GNSS) frequency band to destroy receiver’s performance. While jamming signals are illegal and we may get rid of it with the power of law\, other kinds of interferences will cannot even be avoided. Distance Measuring Equipment (DME) signal is applied to measure the distance between aircraft and ground station\, significant in aircraft transport but interference in GNSS processing. Besides\, the GNSS signal itself can also be a interference after reflection and refraction. Since we couldn’t simply re-move those from the source\, methods to mitigate influences of interferences is necessary for stable performance of receiver. There are three main blocks in GNSS receiver: acquisition block\, tracking block and positioning block\, where influence of interferences could be eliminate to get an accurate Position\, Velocity\, and Time (PVT) solution. In this article\, robust statistics processing is applied as one of the interference mitigation methods. This method aims to lower influence of outliers\, which is the presence of many kinds of interferences in either time domain or transformed domain. Robust statistics processing can be used in pre-correlation in both acquisition block and tracking block\, while a robust Kalman filter is designed in positioning block to get rid of interferences. Deep learning\, achieving extraordinary performance in many application domains\, also provides improvement to tracking block against multipath problem. A deep neural network is built to substitute the whole tracking loop to bring robustness to receiver.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-haoqing-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210108T140000
DTEND;TZID=America/New_York:20210108T150000
DTSTAMP:20260427T132000
CREATED:20210107T213951Z
LAST-MODIFIED:20210107T213951Z
UID:4660-1610114400-1610118000@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Sungho Kang
DESCRIPTION:PhD Proposal Review: Metamaterial Absorbers for Infrared Sensing Microsystems \nSungho Kang \nLocation: Zoom \nAbstract: Infrared (IR) spectroscopic sensing has become a key technique in multidisciplinary environments such as military applications\, industrial safety control\, and smart homes\, by providing an accurate and non-disruptive analysis of the target objects. Recently the demand for high performance and compact IR spectroscopy systems has been steadily growing due to the advent of Internet of Things and the burgeoning development of miniaturized sensors. The key challenge lies in realizing high performance IR detectors that have low noise\, high IR throughput\, and spectral sensitivity in a miniaturized form factor. This challenge has been tackled in the study of micro-electromechanical sensing systems and metamaterial absorbers\, in which the ultra-high resolution sensing capability and the near-perfect IR absorption properties can be simultaneously exploited in a minimized footprint. The metal-insulator-metal (MIM) IR absorbers\, in particular\, are characterized by the near-unity absorptance with lithographically tunable peak absorption wavelength and spectral selectivity in an ultra-thin form factor\, suitable for the implementation of miniaturized spectroscopic IR microsystems. The exceptional IR absorption characteristics realized by the MIM IR absorbers and their sub-wavelength form factor allow for seamless integration with the existing IR sensing microsystem and the unprecedented IR sensing performance for the next generation IoT sensing solutions. In this proposal\, novel development of zero-power long-wavelength IR (LWIR) detector and miniaturized IR spectroscopic sensor based on the two key technologies are presented: (1) plasmonically-enhanced LWIR micromechanical photoswitch and (2) multispectral resonant IR detector array.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-sungho-kang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201217T140000
DTEND;TZID=America/New_York:20201217T150000
DTSTAMP:20260427T132000
CREATED:20201210T000522Z
LAST-MODIFIED:20201210T000522Z
UID:4619-1608213600-1608217200@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Kaidi Xu
DESCRIPTION:PhD Proposal Review: Towards Empirical Implementation and Theoretical Analysis in Adversarial Machine Learning \nKaidi Xu \nLocation: Zoom Link \nAbstract: Deep learning or deep neural networks (DNNs) have achieved extraordinary performance in many application domains such as image classification\, object detection and recognition\, natural language processing and medical image analysis. It has been well accepted that DNNs are vulnerable to adversarial attacks\, which raises concerns of DNNs in security-critical applications and may result in disastrous consequences. Adversarial attacks are usually implemented by generating adversarial examples\, i.e.\, adding sophisticated perturbations\nonto benign examples\, such that adversarial examples are classified by the DNN as target (wrong) labels instead of the correct labels of the benign examples. The adversarial machine learning aims to study this phenomenon and leverage it to build robust machine learning systems and explain DNNs.\nIn this dissertation\, we present the mechanism of adversarial machine learning in both empirical and theoretical ways. Specifically\, we first introduce a uniform adversarial attack generation framework\, structured attack (StrAttack)\, which explores group sparsity in adversarial perturbations by sliding a mask through images aiming for extracting key spatial structures. Second\, we discuss the feasibility of adversarial attacks in the physical world and introduce a powerful framework\, Expectation over Transformation (EoT). Utilize EoT with Thin Plate Spline (TPS) transformation\, we can generate Adversarial T-shirts\, a robust physical adversarial example for evading person detectors even if it could undergo non-rigid deformation due to a moving person’s pose changes.\nThird\, we stand on the defense side and propose the first adversarial training method based on Graph Neural Network.\nFinally\, we introduce Linear relaxation based perturbation analysis (LiRPA) for neural networks\, which computes provable linear bounds of output neurons given a certain amount of input perturbation.\nLiRPA studies the adversarial example in a theoretical way and can guarantee the test accuracy of a model by given perturbation constraints.\nIn the future\, we plan to study a novel patch transformer network to truthfully model real-world physical transformations empirically. In addition\, at the formal robustness direction\, we plan to explore the complete verification\, that given sufficient time\, the verifier should give a definite “yes/no” answer for a property under verification. Our LiRPA framework combining with GPUs may accelerate this procedure.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-kaidi-xu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201216T140000
DTEND;TZID=America/New_York:20201216T150000
DTSTAMP:20260427T132000
CREATED:20201210T000756Z
LAST-MODIFIED:20201210T000756Z
UID:4621-1608127200-1608130800@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Amirreza Farnoosh
DESCRIPTION:PhD Proposal Review: Unsupervised Learning of Low-Dimensional Dynamical Representations from Spatiotemporal Data \nAmirreza Farnoosh \nLocation: Zoom Link \nAbstract: Ever-improving sensing technologies offer a fast and accurate collection of large-scale spatiotemporal data\, recorded from multimodal sensors of heterogeneous natures\, in various application domains\, ranging from medicine and biology to robotics and traffic control. In this proposal\, we are learning the underlying representation of these data in an unsupervised manner\, tailored towards several emerging applications\, namely indoor navigation and mapping\, neuroscience hypothesis testing\, and time series segmentation and forecasting.\nAs such\, (1) we present an unsupervised framework for real-time depth and view-angle estimation from an inertially augmented video recorded from an indoor scene by employing geometric-based machine learning and deep learning models. (2) We introduce a hierarchical deep generative factor analysis framework for temporal modeling of neuroimaging datasets. Our model approximates high dimensional data by a product between time-dependent weights and spatially dependent factors which are in turn represented in terms of lower dimensional latents. This framework can be extended to perform clustering in the low dimensional temporal latent or perform factor analysis in the presence of a control signal. (3) We present a deep switching dynamical system for dynamical modeling of multidimensional time-series data. Specifically\, we employ a deep vector auto-regressive latent model switched by a chain of discrete latents to capture higher-order multimodal latent dependencies. This results in a flexible model that (i) provides a collection of potentially interpretable states abstracted from the process dynamics\, and (ii) performs short- and long-term vector time series prediction in a complex multi-relational setting.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-amirreza-farnoosh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201216T140000
DTEND;TZID=America/New_York:20201216T150000
DTSTAMP:20260427T132000
CREATED:20201205T015159Z
LAST-MODIFIED:20201205T015159Z
UID:4614-1608127200-1608130800@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Pu Zhao
DESCRIPTION:PhD Proposal Review: Towards Robust Image Classification with Deep learning and Real-Time DNN Inference on Mobile \nPu Zhao \nLocation: Zoom Link \nAbstract: As the rapidly increasing popularity of deep learning\, deep neural networks (DNN) have become the fundamental and essential building blocks in various applications such as image classification and object detection. However\, there are two main issues which potentially limit the wide application of DNNs: 1) the robustness of DNN models raises security concerns\, and 2) the large computation and storage requirements of DNN models lead to difficulties for its wide deployment on popular yet resource-constrained devices such as mobile phones.\nTo investigate the DNN robustness\, we explore the DNN attack\, robustness evaluation and defense. More specifically\, for DNN attack\, we achieve various attack goals (e.g. adversarial examples and fault sneaking attacks) with different algorithms (e.g. alternating direction method of multipliers (ADMM) and natural gradient descent (NGD) attacks) under various conditions (white-box and black-box attacks). For robustness evaluation\, we propose a fast evaluation method to obtain the model perturbation bound such that any model perturbation within the bound does not alter the model classification outputs or incur model mis-behaviors. For the DNN defense\, we investigate the defense performance with model connection techniques and successfully mitigate the fault sneaking and backdoor attacks.\nWith a deeper understanding of the DNN robustness\, we further explore the deployment problem of DNN models on edge devices with limited resources. To satisfy the storage and computation limitation on edge devices\, we adopt model pruning to remove the redundancy in models\, thus reducing the storage and computation during inference. Besides\, as some applications have real-time requirements with high inference speed sensitivities such as object detection on autonomous cars\, we further try to implement real-time DNN inference for various DNN applications on mobile devices with pruning and compiler optimization. To summary\, we mainly investigate the DNN robustness and implement real-time DNN inference on the mobile.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-pu-zhao/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201216T100000
DTEND;TZID=America/New_York:20201216T110000
DTSTAMP:20260427T132000
CREATED:20201214T194420Z
LAST-MODIFIED:20201214T194420Z
UID:4631-1608112800-1608116400@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Hongjia Li
DESCRIPTION:PhD Proposal Review: Automation Design and DNN Acceleration Algorithms: From Software Implementation to Hardware Physical Design \nHongjia Li \nLocation: Zoom Link \nAbstract: Deep learning has been growing at a fast pace in recent years and has been expanded into many application fields\, with a wide range from image recognition\, object detection to medical applications. Meanwhile\, edging devices such as mobile devices are rapidly becoming the central computer and carrier for deep learning tasks. However\, real-time execution has been limited due to the computation/storage resource constraints on these devices.\nIn this proposal review\, I will dive into some aspects of DNN acceleration methods\, including model compression techniques and software implementation optimizations. The goal is to achieve an unprecedented\, real-time performance of large-scale neural network inference on edging devices. Additionally\, an efficient physical design automation design is introduced for Adiabatic Quantum-Flux-Parametron (AQFP) circuits\, meeting the unique features and constraints.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-hongjia-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201216T100000
DTEND;TZID=America/New_York:20201216T110000
DTSTAMP:20260427T132000
CREATED:20201210T000641Z
LAST-MODIFIED:20201210T000641Z
UID:4620-1608112800-1608116400@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Majid Sabbagh
DESCRIPTION:PhD Proposal Review: The perils of shared computing: A hardware security perspective \nMajid Sabbagh \nLocation: Teams Link \nAbstract: The enormous computation power of modern processors and accelerators has rendered them shared computing resources for multiple users and applications\, both in the cloud and on the edge. Despite software techniques for security such as virtualization and containers\, recently a new attack surface is emerging that pertains to the hardware vulnerabilities of shared computing resources\, posing serious threats to shared computing.\nFault attacks (FAs) and Side-Channel Attacks (SCAs) are two hardware-oriented attacks that target the system implementations. FAs aim to tamper the integrity of application execution through different fault injection methods\, to compromise the data or disrupt computation at run-time. SCAs exploit the information leakage of sensitive applications in physical parameters\, such as power consumption\, electromagnetic emanations\, and timing\, to breach the confidentiality of the application. \nIn this dissertation\, we introduce a new class of FAs against Graphics Processing Units (GPUs)\, called overdrive fault attacks. We discover the security vulnerability of GPU’s voltage-frequency scaling (VFS) mechanism\, a common feature to balance power consumption and performance. An out-of-specification configuration of GPU voltage and frequency can be set by an adversary on the host CPU\, through the software interfaces to GPU’s power management units. This setting will cause timing violations for the computation and result in silent data corruptions (SDCs). We apply the overdrive fault attacks on two common victim applications. One is cryptographic applications accelerated by GPU. We launch a differential fault analysis (DFA) attack on an AES kernel running on an AMD RX 580 GPU and successfully recover the secret key. The other victim is deep neural network (DNN) inference. In modern GPUs that support multiple kernels\, the adversary is able to track the execution of the victim DNN through shared resources and control the timing of fault injections precisely. We launch a successful attack on a convolutional neural network kernel running on an NVIDIA RTX 2080 SUPER GPU with misclassifications. We further study the characteristics of fault injections and the fault propagation through the network.\nWe evaluate a timing side-channel attack called Prime+Probe attack on Central Processing Units (CPUs) and propose a Side-Channel Attack DEtection Tool (SCADET). SCADET is a methodology and a tool that analyzes an x86 program’s memory accesses. It records and analyzes the memory accesses using dynamic binary instrumentation by running the program in a controlled environment to accurately identify the malicious access patterns corresponding to the Prime+Probe attack.\nFinally\, I propose an FPGA-based RISC-V processor prototype as an evaluation platform for various cache timing attacks and transient attacks\, and implement a taint tracking-based countermeasure against transient attacks. For the first phase\, we have ported spectre v1 and v2 and return-stack-buffer attack to the SonicBOOM RISC-V processor.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-majid-sabbagh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201216T093000
DTEND;TZID=America/New_York:20201216T103000
DTSTAMP:20260427T132000
CREATED:20201214T194558Z
LAST-MODIFIED:20201214T194558Z
UID:4632-1608111000-1608114600@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Jinghan Zhang
DESCRIPTION:MS Thesis Defense: Allocating One Common Accelerator-Rich Platform for Many Streaming Applications \nJinghan Zhang \nLocation: Zoom Link \nAbstract: Many demanding streaming applications share functional and structural similarities with other 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 thesis 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\, and (5) two novel algorithms\, Dynamic Score Selection (DSS) and GenetIc Domain Exploration (GIDE)\, for hardware/software partitioning of a domain-specific platform to maximize the throughput across domain applications (under certain constraints).\nThis thesis demonstrates DSS’s and GIDE’s benefits using OpenVX applications and synthetic domains. The DSS and GIDE generated domain-specific platforms improve performance over application-specific platforms by 58%\, and 75% for OpenVX\, as well as by 23% and 48% for synthetic applications. GIDE’s platforms reach 99.8% (OpenVX) and 97.6% (synthetic) throughput of the domain optimal platform obtained through exhaustive search.
URL:https://ece.northeastern.edu/event/ece-ms-thesis-defense-jinghan-zhang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201215T110000
DTEND;TZID=America/New_York:20201215T120000
DTSTAMP:20260427T132000
CREATED:20201214T194726Z
LAST-MODIFIED:20201214T194726Z
UID:4633-1608030000-1608033600@ece.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Arjun Singh
DESCRIPTION:PhD Proposal Review: Design\, Modeling and Operation of Plasmonic Devices for Smart Communication Systems in the Terahertz Band \nArjun Singh \nLocation: Teams Link \nAbstract: The terahertz (THz) band is an attractive spectral resource for future communication systems\, for supporting very high-speed data rates and increasingly dense networks. However\, the lack of a well-developed technology that operates at these frequencies has remained a challenge for the scientific community. The very high propagation losses at THz frequencies and the decimating impact of everyday objects on THz wave propagation necessitate an up-haul of the conventional communication link\, with smart control over the radiation\, propagation\, and detection of THz signals. To overcome these obstacles\, novel plasmonic devices that exploit the attractive properties of graphene have been proposed. However\, there are several challenges\, such as low output power and high reflection losses\, that are not yet addressed. The objective of the proposed research herein is to facilitate an end-to-end communication link with graphene plasmonics as the cornerstone of the fundamental device physics. The devices designed can be utilized at both the communication endpoints\, as well as across the channel\, to effectively overcome the limited communication distance – The grand challenge of the THz band.\nTo this end\, a graphene-based plasmonic array architecture is first proposed\, explained\, and modeled. The fundamental radiating element of the array architecture\, called the plasmonic front-end\, consists of a self-sufficient plasmonic source\, a plasmonic modulator that acts as a phase controller\, and a plasmonic nano-antenna. The array designed through an integration of these front-ends is compact and provides complete beamsteering support\, with a new tailored algorithm developed for beamforming weight selection. Numerical evaluations and full-wave finite difference frequency domain (FDFD) simulations with COMSOL Multi-physics are utilized to verify array operation. The array is also demonstrated to provide a strong effective isotropic radiated power (EIRP)\, that increases exponentially with array size. To mitigate the negative effects of the channel environment\, such as unwanted blockages and high path losses for simpler devices\, a hybrid reflectarray is presented. The fundamental element is modeled as a jointly designed and integrated metal-graphene patch. Numerical and simulation results are utilized to demonstrate the attractive properties of the proposed reflectarray as compared to other proposed counterparts\, including independence from the incoming angle of the impinging wave\, dynamic phase control capability\, and a strong reflection efficiency. The unique design properties of the plasmonic array\, as well as the hybrid reflectarray\, open the option of incorporating techniques such as multi-beam beamforming design and interleaved\, independent arrays\, to boost the channel capacity.\nAs a part of the proposed work\, the impact of the design properties of these devices on the communications link will be investigated by developing the fundamental problem and considering all trade-offs. The undertaking will be significantly more robust and conclusive than those that have been performed previously\, both due to the consideration of a complete end to end link\, as well as the incorporation of the characteristics of the device design model. Finally\, preliminary fabrication results in the realization of these devices are presented\, and the roadmap ahead is outlined.
URL:https://ece.northeastern.edu/event/ece-phd-proposal-review-arjun-singh/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201212T103000
DTEND;TZID=America/New_York:20201212T113000
DTSTAMP:20260427T132000
CREATED:20201207T214335Z
LAST-MODIFIED:20201207T214335Z
UID:4617-1607769000-1607772600@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Ning Liu
DESCRIPTION:PhD Dissertation Defense: Real-World Applicable Deep Learning Techniques: From Efficient Modeling to Automated Model Optimization \nNing Liu \nLocation: Zoom Link \nAbstract: Recently\, deep neural networks (DNNs) have been widely studied and achieved tremendous success in a variety of real-world applications\, such as computer vision\, medical diagnosis and machine translation. Deep reinforcement learning (DRL)\, as an emerging powerful deep learning technique\, combines DNNs with reinforcement learning into an interactive system. DRL opens up many new applications in domains such as healthcare\, robotics and smart grids. With the rapid evolution of IT infrastructures\, cloud computing has been witnessed as the prevailing computing paradigm. The underlying infrastructure of cloud computing relies on a large amount of data centers. The energy efficiency issue from “cloud” becomes more crucial and calls for more attentions.\nIn this dissertation\, to solve the real-world energy efficiency problems\, we take advantage of the deep learning and deep reinforcement learning techniques for efficient modeling of “cloud” applications. We present a DNN-based power management framework for regulation service and a novel DRL-based hierarchical framework for solving the overall resource allocation and power management problem. On the other hand\, the powerful DNNs themselves are massive\, consuming tremendous energy. Therefore\, we explore the efficiency on deep neural networks. We propose an automatic model pruning framework to reduce the storage and computation requirements and accelerate inference. Our framework outperforms the prior work on automatic model compression by up to 33× in pruning rate (120× reduction in the actual parameter count) under the same accuracy. Significant inference speedup has been observed from the proposed framework on actual measurements on smartphone.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-ning-liu/
END:VEVENT
END:VCALENDAR