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X-WR-CALDESC:Events for Department of Electrical & Computer Engineering
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210209T150000
DTEND;TZID=America/New_York:20210209T160000
DTSTAMP:20260509T020023
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/
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DTSTART;TZID=America/New_York:20210216T100000
DTEND;TZID=America/New_York:20210217T190000
DTSTAMP:20260509T020023
CREATED:20210121T004629Z
LAST-MODIFIED:20210201T200240Z
UID:4697-1613469600-1613588400@ece.northeastern.edu
SUMMARY:NSF Workshop on Predictive Intelligence for Pandemic Prevention
DESCRIPTION:NSF Predictive Intelligence for Pandemic Prevention (PIPP) Workshop – Pandemic Readiness for Emerging Pathogens (PREP)  \nThe goal is to foster scientific discussion and catalyze innovation and partnerships to significantly enhance our understanding of the challenges and potential solutions to rapid detection and assessment of emerging pathogens and infectious disease dynamics from the molecular to the ecological scale. We invite participants from academia\, government\, industry and non-governmental organizations from varied disciplines: Engineering (ENG)\, Biological Sciences (BIO)\, Computer and Information Science and Engineering (CISE)\, and Social\, Behavioral and Economic Sciences (SBE). \nPREP has four topical thrusts: \n1) Rapid and Accurate Detection and Assessment of Emerging Pathogens \n2) Monitoring Environmental Change\, Animal Movements\, and High-Risk Interfaces for Disease Transmission \n3) Monitoring Human Movements and At-risk Communities for Disease Transmission and Spread \n4) Data-Intensive Machine Learning and Modeling for Pandemic Preparedness. \nEach topical thrust has vision talks\, panels\, and breakout sessions.\nThe goal of the workshop is a roadmap for research investments to address key technical and scientific challenges for pandemic prevention. \nFor more information\, agenda\, list of invited speakers\, and registration: https://thepipp.org \nDirect registration \n\nOrganized by ECE Professor Nian X. Sun with support from NSF grant.
URL:https://ece.northeastern.edu/event/nsf-workshop-on-predictive-intelligence-for-pandemic-prevention/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210217T110000
DTEND;TZID=America/New_York:20210217T120000
DTSTAMP:20260509T020023
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/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210218T150000
DTEND;TZID=America/New_York:20210218T160000
DTSTAMP:20260509T020023
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/
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DTSTART;TZID=America/New_York:20210222T150000
DTEND;TZID=America/New_York:20210222T170000
DTSTAMP:20260509T020023
CREATED:20210217T030133Z
LAST-MODIFIED:20210217T030301Z
UID:4748-1614006000-1614013200@ece.northeastern.edu
SUMMARY:AIX SEMINAR SERIES
DESCRIPTION:We cordially invite you to join the \nAIX SEMINAR\nMonday\, Feb 22\, 3:00 PM EST \nZoom Link: https://northeastern.zoom.us/j/96209636039 \n__________________________________________________________________________________ \nAssistive Robots for People with Severe Motor Impairments \nDr. Maria Kyrarini\, Postdoctoral Research Fellow\, the University of Texas at Arlington \nIntroducing the Open Mocap Project \nDr. Jon Matthis\, Northeastern University\, Professor of Biology  \n__________________________________________________________________________________ \n \nAssistive Robots for People with Severe Motor Impairments  \nDr. Maria Kyrarini | Postdoctoral Research Fellow\, the University of Texas at Arlington \nAbstract: Assistive robotic manipulators have the potential to support individuals with severe motor impairments to regain some of their independence in performing Activities of Daily Living. For individuals with tetraplegia\, which is the paralysis of all limbs\, interaction with assistive robotic manipulators is a very challenging task. In this talk\, I will present two interaction approaches to enable a person with tetraplegia to drink with the assistance of a robotic manipulator. The first approach focuses on enabling people with tetraplegia to teach the robot how to assist them with drinking. The second approach focuses on an autonomous multi-sensory robotic system\, which assists with straw-less drinking. Experimental results for both approaches will be presented. Furthermore\, I will conclude the talk with a brief discussion of future research challenges. \nBio: 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. \nWebpage: https://sites.google.com/view/mariakyrarini/home \n__________________________________________________________________________________ \nIntroducing the Open Mocap Project  \nDr. Jon Matthis | Professor of Biology\, Northeastern University \nAbstract: I will present an update to the Open Mocap project\, which aims to develop a free\, open-source\, camera hardware and tracking software agnostic system for 3D motion capture of humans\, non-human animals\, and robots. The current iteration looks something like this: \nhttps://twitter.com/JonMatthis/status/1351531974364688385 \nBio: Jon Matthis is an Assistant Professor in the Biology Department at Northeastern University. Jon’s research focuses primarily on the visual control of human walking\, with an emphasis on the way that the biomechanics of bipedal gait shapes the use of visual information during locomotion over real world rough terrain. To this end\, he has developed an apparatus that accurately records full-body motion capture and eye tracking data of people walking outdoors over real-world rocky terrain. Using this data\, he hopes to explain the way that humans use eye movements to extract information from their environment in order to facilitate stable and efficient locomotion over complex and difficult terrain. \n_________________________________________ \nPlease sign up to receive further AIX seminar notifications \nPresented by the Institute for Experiential Robotics at Northeastern University
URL:https://ece.northeastern.edu/event/aix-seminar-series/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210225T110000
DTEND;TZID=America/New_York:20210225T120000
DTSTAMP:20260509T020023
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/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210226T123000
DTEND;TZID=America/New_York:20210226T133000
DTSTAMP:20260509T020023
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/
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