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DTSTART;TZID=America/New_York:20230411T120000
DTEND;TZID=America/New_York:20230411T130000
DTSTAMP:20260423T181256
CREATED:20230405T175216Z
LAST-MODIFIED:20230405T175216Z
UID:6248-1681214400-1681218000@ece.northeastern.edu
SUMMARY:Tirthak Patel's Dissertation Defense
DESCRIPTION:“Robust System Software for Quantum Computing” \nCommittee Members: \nProf. Devesh Tiwari (Advisor) \nProf. David Kaeli \nProf. Ningfang Mi \nProf. Gene Cooperman \nProf. Kenneth Brown \nAbstract: \nDespite rapid progress in quantum computing in the last decade\, the limited usability of quantum computers remains a major roadblock toward its wider adoption. Current noisy intermediate-scale quantum (NISQ) computers produce highly erroneous program outputs for quantum-advantage-proven algorithms — that is\, algorithms that are infeasible or orders of magnitude slower on classical supercomputing and high-performance computing (HPC) clusters. Unfortunately\, currently\, quantum computing programmers lack robust system software tools and methods to make meaningful use of erroneous program executions on quantum computers. \nThis lack of capability is the core motivation behind the fundamental question this dissertation poses: “can we build system software tools for programmers to make the quantum program execution and output meaningful on NISQ machines?” This dissertation answers this question in the affirmative— experimentally demonstrating on real-system quantum computers that it is possible to extract near-accurate program output from noisy executions on today’s erroneous quantum computers\, ironically using classical HPC resources and knowledge. This dissertation demonstrates how to achieve this goal without requiring user intervention\, domain knowledge about quantum algorithms\, or additional quantum hardware support. \nUnfortunately\, as this dissertation uncovers\, progressing toward making quantum computers usable is a double-edged sword. In the near future\, only a few entities in the world may have access to powerful quantum computers\, and these quantum computers will be used to solve previously-unsolved large-scale optimization problems\, possibly without an explicit trust model between the service provider and the customer. Therefore\, this dissertation envisions that the solutions to such large-scale optimization problems will be considered sensitive and will need to be protected. This dissertation takes the first few steps toward preparing us for that future by developing a novel method that intelligently obfuscates near-accurate program output and quantum circuit structure to preserve a customer’s privacy under a specified computation model and resource availability. \nThe approaches introduced in this dissertation open up new research avenues for hybrid quantum-classical computing and lower the barrier to entry for quantum computing research for the experimental computer systems and HPC community by open-sourcing multiple novel datasets and software frameworks implemented for real-system quantum computers. \nCandidate Bio: \nTirthak Patel is an incoming Assistant Professor in the Department of Computer Science at Rice University; currently\, a PhD candidate at Northeastern University\, advised by Professor Devesh Tiwari. Tirthak conducts systems-level research at the intersection of quantum computing and high-performance computing (HPC). His research contributions have appeared at rigorously peer-reviewed publication venues including ASPLOS\, Supercomputing (SC)\, HPDC\, HPCA\, and USENIX FAST\, and have been recognized with multiple award distinctions. He has received the ACM-IEEE CS George Michael Memorial HPC Fellowship\, the NSERC Alexander Graham Bell Canada Graduate Scholarship\, and the Northeastern University Outstanding Graduate Student in Research award\, for his research contributions toward making noisy quantum computing systems useful and helping HPC programmers solve computationally challenging problems.
URL:https://ece.northeastern.edu/event/tirthak-patels-dissertation-defense/
LOCATION:442 Dana\, 360 Huntington Ave\, 442 DA\, Boston\, MA\, 02115\, United States
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221129T140000
DTEND;TZID=America/New_York:20221129T153000
DTSTAMP:20260423T181256
CREATED:20221122T012209Z
LAST-MODIFIED:20221122T012209Z
UID:5975-1669730400-1669735800@ece.northeastern.edu
SUMMARY:Prof. Hui Guan -  "Towards accurate and efficient edge computing via multi-task learning "
DESCRIPTION:“Towards accurate and efficient edge computing via multi-task learning ” \n\nAbstract: \n\n\nAI-powered applications increasingly adopt Deep Neural Networks (DNNs) for solving many prediction tasks\, leading to more than one DNNs running on resource-constrained devices. Supporting many models simultaneously on a device is challenging due to the linearly increased computation\, energy\, and storage costs. An effective approach to address the problem is multi-task learning (MTL) where a set of tasks are learned jointly to allow some parameter sharing among tasks. MTL creates multi-task models based on common DNN architectures and has shown significantly reduced inference costs and improved generalization performance in many machine learning applications. In this talk\, we will introduce our recent efforts on leveraging MTL to improve accuracy and efficiency for edge computing. The talk will introduce multi-task architecture design systems that can automatically identify resource-efficient multi-task models with low inference costs and high task accuracy. \n\n\nBio:\n \n\n\n\nHui Guan is an Assistant Professor in the College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst\, the flagship campus of the UMass system. She received her Ph.D. in Electrical Engineering from North Carolina State University in 2020. Her research lies in the intersection between machine learning and systems\, with an emphasis on improving the speed\, scalability\, and reliability of machine learning through innovations in algorithms and programming systems. Her current research focuses on both algorithm and system optimizations of deep multi-task learning and graph machine learning.
URL:https://ece.northeastern.edu/event/prof-hui-guan-towards-accurate-and-efficient-edge-computing-via-multi-task-learning/
LOCATION:442 Dana\, 360 Huntington Ave\, 442 DA\, Boston\, MA\, 02115\, United States
GEO:42.3387508;-71.0923044
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221129T100000
DTEND;TZID=America/New_York:20221129T130000
DTSTAMP:20260423T181256
CREATED:20221104T010151Z
LAST-MODIFIED:20221104T010151Z
UID:5952-1669716000-1669726800@ece.northeastern.edu
SUMMARY:Research Presentations On Bendable Electronics and Sustainable Technologies (BEST)
DESCRIPTION:Professor Ravinder Dahiya will be joining Northeastern’s ECE Department on January 2023. Please join us for an interactive mini-symposium featuring projects from the BEST Lab directed by Professor Dahiya. \n  \nThe presenters are: \nDr. Dhayalan Shakthivel\, Research Associate\, Inorganic Nanowires for Flexible and Large Area Electronics \nDr. Gaurav Khandelwal\, Post-doc\, Functional Materials based Triboelectric Nanogenerators for Selfpowered Sensors and Systems \nDr. Fengyuan Liu\, Post-doc\, “Hebbian-like” learning in electronic skin \nDr. Abhishek S. Dahiya\, Research Associate\, Towards energy autonomous electronic skin using sustainable technologies \nAyoub Zumeit\, PhD candidate\, Inorganic nanostructures-based high-performance flexible electronics \nAdamos Christou\, PhD candidate\, Novel Technologies for High-Performance Printed Electronics \nRadu Chirila\, PhD candidate\, Electronic Skin and Holographic Systems for Socially Intelligent Robots \nJoão Neto\, PhD candidate\, Hardware building for neuromorphic electronic skin \nLuca De Pamphilis\, PhD candidate\, Nanowire-based electronic layers for flexible neuromorphic devices \nMake sure to RSVP & specify inperson or virtual attendance. See you soon!
URL:https://ece.northeastern.edu/event/research-presentations-on-bendable-electronics-and-sustainable-technologies-best/
LOCATION:442 Dana\, 360 Huntington Ave\, 442 DA\, Boston\, MA\, 02115\, United States
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221003T130000
DTEND;TZID=America/New_York:20221003T140000
DTSTAMP:20260423T181256
CREATED:20221103T185029Z
LAST-MODIFIED:20221103T185029Z
UID:5928-1664802000-1664805600@ece.northeastern.edu
SUMMARY:Vedant Sumaria's PhD Dissertation Defense
DESCRIPTION:“Exploring Micro-Machined Glass Shell Resonators For Sensor Application” \nAbstract: \nThis work presents the exploration of the chip-scale glass blowing technique for novel sensing methods. On-chip microspherical glass shells (MSG) of hundreds of micrometers in diameter with ultra-smooth surfaces and sub-micrometer wall thicknesses have been fabricated and have been shown to sustain optical whispering gallery mode resonance with high Q-factors of greater than 25 million. These resonators exhibit a temperature sensitivity of 1.17 GHz K−1 and can be configured as ultra-high-sensitivity thermal sensors for a wide range of applications. \nWe demonstrate a thermal infrared (IR) detector based on a high-quality factor (Q) whispering gallery mode (WGM) borosilicate glass microspherical shell resonator and investigate its performance in detecting IR radiation in the wavelength range of 1 –20 μ m. The resonator exhibits a temperature sensitivity of 1.17 GHz/K with a Q-factor of 3 million and can be configured as a high-sensitivity infrared sensor. The microspherical shell IR sensor achieved a noise-equivalent power (NEP) of 944.89 pW/√Hz experimentally. A laser Doppler vibrometer (LDV) is used to measure the physical expansion of the microspherical glass resonator when IR radiation is absorbed. A dimensional change of ≈100 fm is shown to be resolved. \nA comparison of two calorimetric biosensing systems with relatively high-throughput sample analysis is also reported. The calorimetric biosensor system compared are a thin (20 μm) micro-machined Y-cut quartz crystal resonator (QCR) and a MSG (6 μm thick) Whispering Gallery Mode (WGM) resonator as a temperature sensor placed close to a chemical reaction chamber with the immobilized enzyme. The enzymes (urease and glucose oxidase) were immobilized on superparamagnetic nanoparticles using covalent bonding. This configuration enables a sensing system where the reaction chamber is physically separated from the analyte solution of interest and thereby free from fouling effects typically associated with biochemical reactions occurring on the sensor surface. The performance of this biosensing system is compared by the detection of 0-250 mM urea and glucose in phosphate buffer. \nFurther\, we present MSG WGM resonator-based thermal sensor array which is configured with a 3D printed reaction chamber that utilizes the backside silicon of the resonator for sensitive calorimetric biosensing applications. The coupling of heat from the reaction chamber to the WGM resonator is achieved via conduction from the analyte medium. The sensor was aligned to the opening of the 3D-printed reaction chamber\, and the device was mounted using a thermo-elastic epoxy. This sensor configuration allows for a very robust sensing platform with no fouling of the sensor surface or degradation in its performance metrics. Resonance frequency tracking using the Pound-Drever- Hall locking method was used for enzymatic activity measurements. Results of the catalytic reaction of glucose with glucose oxidase and the hydrolysis reaction of urea by urease are reported. In addition\, body fluids such as blood plasma\, serum\, and blister fluid are tested\, which match very well with the experimental results. From the analysis of the signal-to-noise ratio of the glucose sensor\, a resolution of 100 nM could be obtained\, improving the detection limit by a factor of 10\,000 compared to QCR sensors. \nCommittee: \nProf. Srinivas Tadigadapa (Advisor) \nProf. Matteo Rinaldi \nProf. Yongmin Liu \nProf. Rosemary Smith
URL:https://ece.northeastern.edu/event/vedant-sumarias-phd-dissertation-defense/
LOCATION:442 Dana\, 360 Huntington Ave\, 442 DA\, Boston\, MA\, 02115\, United States
GEO:42.3387508;-71.0923044
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220209T120000
DTEND;TZID=America/New_York:20220209T130000
DTSTAMP:20260423T181256
CREATED:20220208T001833Z
LAST-MODIFIED:20220208T001833Z
UID:5413-1644408000-1644411600@ece.northeastern.edu
SUMMARY:ECE Seminar: Derya Aksaray
DESCRIPTION:ECE Seminar: Reinforcement Learning for Dynamical Systems with Temporal Logic Specifications \nDerya Aksaray \nLocation: 442 Dana or Zoom Link \nAbstract: In many applications\, dynamical systems such as drones\, mobile robots\, or autonomous cars need to achieve complex specifications on their trajectories which may include spatial (e.g.\, regions of interest)\, temporal (e.g.\, time bounds)\, and logical (e.g.\, priority\, preconditions\, concurrency among tasks) requirements. As these specifications become more complex\, encoding them via algebraic equations become intractable. Alternatively\, such specifications can be compactly expressed and used in control synthesis by utilizing the framework of temporal logics. In this talk\, I will address the problem of learning optimal control policies for satisfying temporal logic (TL) specifications in the face of uncertainty. Standard reinforcement learning (RL) algorithms\, which aim to maximize the expected sum of discounted rewards\, are not directly applicable when the objective is to satisfy a TL specification. To overcome this limitation\, I will formulate an approximate problem that can be solved via reinforcement learning and present the suboptimality bound of the proposed solution. Then\, I will consider the case where a TL specification is given as the constraint rather than the objective and present a novel approach for satisfying the TL constraint with a desired probability throughout the learning process. I will motivate this part by multi-use of autonomous systems\, e.g.\, a drone executing a pick-up and delivery mission as its primary task (constraint) while learning to fly over regions of interest (aerial monitoring) as its secondary task (objective). Finally\, I will conclude my talk by discussing some future directions toward the resilience and safety of autonomous systems with complex specifications. \nBio: Derya Aksaray is currently an Assistant Professor in the Department of Aerospace Engineering and Mechanics at the University of Minnesota (UMN). Before joining UMN\, she held post-doctoral researcher positions at the Massachusetts Institute of Technology from 2016-2017 and at Boston University from 2014-2016. She received her Ph.D. degree in Aerospace Engineering from the Georgia Institute of Technology in 2014. Her research interests lie primarily in the areas of control theory\, formal methods\, and machine learning with applications to autonomous systems and aerial robotics.
URL:https://ece.northeastern.edu/event/ece-seminar-derya-aksaray/
LOCATION:442 Dana\, 360 Huntington Ave\, 442 DA\, Boston\, MA\, 02115\, United States
GEO:42.3387508;-71.0923044
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220209T103000
DTEND;TZID=America/New_York:20220209T120000
DTSTAMP:20260423T181256
CREATED:20220209T213347Z
LAST-MODIFIED:20220209T213347Z
UID:5436-1644402600-1644408000@ece.northeastern.edu
SUMMARY:ECE Seminar: Qiushi Guo
DESCRIPTION:ECE Seminar: Emergent Active Photonic Platforms for Next-generation Mid-infrared and Ultrafast Photonics \nQiushi Guo \nLocation: 442 Dana or Zoom Link \nAbstract: As two basic properties of light\, wavelength and timescale are central to numerous photonic applications. Compared to visible and near-infrared\, the longer wavelength mid-infrared spectral regime contains unique thermal visual information and chemical fingerprints of the environment.  On a different front\, femtosecond light sources and systems can enable ultrafast information processing\, sensing\, and computing. Yet\, current chip-scale photonic devices and systems are facing tremendous challenges in detecting\, generating\, and processing light of long wavelength and ultrashort timescale. Overcoming these challenges requires new materials and clever device architectures\, and these technologies stand poised to revolutionize fields such as biomedical sensing\, free-space communication\, and photonic computing in both classical and quantum domains. \nIn this talk\, I will show that by engineering the carrier and nonlinear dynamics in emergent active photonic materials\, we can detect photons beyond the regimes accessible to conventional laser sources and detectors\, and process information in an ultrafast manner. In the first half of my talk\, I will first briefly introduce the discovery of black phosphorus (BP) mid-infrared photonics\, highlighting the world’s first BP mid-infrared detectors with high internal gain\, as well as BP’s electrically tunable spectral response due to its unique bandgap tunability. Then\, I will discuss a new strategy for detecting longer wavelength mid-infrared radiations at 12 µm. This is achieved by harnessing the intrinsic mid-infrared plasmons in large-scale graphene. \nThe second half of my talk will cover my recent work on integrated lithium niobate (LN) ultrafast photonics in both classical and quantum domains. I will discuss the realization of ultra-strong nonlinear optical interactions and dynamics in dispersion-engineered and quasi-phase-matched integrated LN devices\, which have enabled 100 dB/cm optical parametric amplification\, ultra-wide bandwidth quantum squeezing\, as well as femtosecond and femtojoule all-optical switching. Finally\, I will outline promising pathways toward realizing chip-scale ultrafast light sources and microsystems for on-chip spectroscopic sensing\, mid-infrared free-space communication\, coherent all-optical computing\, and next-generation thermal vision technologies. \nBio: Dr. Qiushi Guo is currently a postdoctoral scholar at the California Institute of Technology with Prof. Alireza Marandi. He received his Ph.D. in Electrical Engineering from Yale University in Dec. 2019\, advised by Prof. Fengnian Xia. He received his M.S. degree in Electrical Engineering from the University of Pennsylvania in 2014\, and his B.S. degree in Electrical Engineering from Xi’an Jiaotong University in 2012. Qiushi is the winner of the 2021 Henry Prentiss Becton Graduate Prize for his exceptional research achievements at Yale University. His research interests include integrated nonlinear and quantum photonics\, mid-infrared photonics\, and 2-D materials optoelectronics. He has published 36 peer-reviewed research papers in leading scientific journals with citations more than 2700 times. He is serving on the editorial board of the journal Micromachines.
URL:https://ece.northeastern.edu/event/ece-seminar-qiushi-guo/
LOCATION:442 Dana\, 360 Huntington Ave\, 442 DA\, Boston\, MA\, 02115\, United States
GEO:42.3387508;-71.0923044
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220208T110000
DTEND;TZID=America/New_York:20220208T122000
DTSTAMP:20260423T181256
CREATED:20220208T001931Z
LAST-MODIFIED:20220208T001931Z
UID:5416-1644318000-1644322800@ece.northeastern.edu
SUMMARY:ECE Seminar: Sunwoo Lee
DESCRIPTION:ECE Seminar: Autonomous Microsystems Based on Heterogeneously Integrated CMOS for Biological Big Data \nSunwoo Lee \nLocation: 442 Dana or Zoom Link \nAbstract: Minimally invasive and chronic physiological monitoring can provide an effective means of disease prevention and early detection while the cumulative big data can unveil hidden patterns in our physiology. Yet\, current physiological monitoring tools are often bulky\, invasive\, and expensive\, limiting their sensitivity and applicability. In this talk\, I will discuss autonomous microsystems based on heterogeneously integrated CMOS\, a platform on which ideal physiological sensors and actuators can be built.\nA micro-scale optoelectronically transduced electrode (MOTE)\, an exemplary microsystem I have designed and built for tetherless neural recording\, is powered and communicates optically through a vertically integrated AlGaAs micro-scale light emitting diode (µLED)\, eliminating the needs for a battery or a RF coil; the MOTE is smaller than a human hair (~60 µm × 30 µm × 330 µm) and weighs about one 1 µg (cf. a grain of sand is about 670 µg). I will review the unique challenges and considerations in developing such heterogeneous systems in terms of device fabrication\, circuit design\, integration\, and handling/manipulation.\nWhile the MOTE is designed for neural recording\, its design methodologies can also be used to monitor other physiological parameters such as temperature\, pH\, glucose-level\, etc. I will introduce future autonomous microsystems with expanded modalities and how to interface them with existing wearables. As such microsystems become more accessible\, the resulting biological big data will help enable personalized healthcare and produce a physiological ‘digital twin’ (like the architectural digital twins of select cities) that can add a new dimension to epidemiological and aging studies. \nBio: Sunwoo Lee (Member\, IEEE) received the B.S. degree in Electrical and Computer Engineering from Cornell University\, Ithaca\, NY in 2010\, and the M.S. and Ph.D. degrees in Electrical Engineering from Columbia University\, New York\, NY in 2012 and 2016\, respectively\, working on graphene synthesis and graphene-based nano-electro-mechanical systems for signal processing and sensing applications. In 2016\, he joined the Molnar Group in the School of Electrical and Computer Engineering at Cornell University as a post-doctoral researcher and has been working on heterogeneously integrated CMOS for physiological monitoring. Sunwoo was a recipient of Qualcomm Innovation Fellowship (QInF) 2012 as well as QInF 2013\, and a recipient of Pi-Star Award for Young Researcher Presentation at CARBONHAGEN 2015.
URL:https://ece.northeastern.edu/event/ece-seminar-sunwoo-lee/
LOCATION:442 Dana\, 360 Huntington Ave\, 442 DA\, Boston\, MA\, 02115\, United States
GEO:42.3387508;-71.0923044
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220131T130000
DTEND;TZID=America/New_York:20220131T140000
DTSTAMP:20260423T181256
CREATED:20220128T024437Z
LAST-MODIFIED:20220128T024437Z
UID:5401-1643634000-1643637600@ece.northeastern.edu
SUMMARY:ECE Seminar: Michael Everett
DESCRIPTION:ECE Seminar: Deployable Learning Machines: From cost-to-go estimation to certification \nMichael Everett \nLocation: 442 Dana and Zoom Link \nAbstract: Autonomous robots have the potential to transform our everyday lives\, yet most of these systems struggle outside of the lab or carefully designed warehouses. This talk will first describe our work toward a new generation of robots that learn to handle the highly dynamic and uncertain nature of human environments. In particular\, I will highlight the importance of obtaining accurate cost-to-go models\, which we show can be learned from self-play or aerial imagery for a variety of applications\, from navigation among pedestrians to last-mile delivery. The talk will then dive into the challenges of certifying the safety and robustness properties of machines that learn. I will describe our work that uses convex relaxations and set partitioning to simplify the analysis of highly nonlinear neural networks used across AI. These analysis tools led to the first framework for deep reinforcement learning that is certifiably robust to adversarial attacks and noisy sensor data. The tools also enable reachability analysis — the calculation of all states that a system could reach in the future — for systems that employ neural networks in the feedback loop\, which provides another notion of safety for learning machines that interact with uncertain environments. Finally\, I will discuss my long-term vision that aims to spark a new era of learning machines that can be deployed in any environment without human supervision. \nBio: Michael Everett is currently a Research Scientist in the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology (MIT). He received the S.B.\, S.M.\, and Ph.D. degrees in mechanical engineering in 2015\, 2017\, and 2020\, respectively\, at MIT. His research lies at the intersection of machine learning\, robotics\, and control theory. His papers have been recognized as one of the Editors’ Top 5 Articles of 2021 in IEEE Access\, Best Paper Award on Cognitive Robotics at IROS 2019\, Best Student Paper Award and Finalist for Best Paper Award on Cognitive Robotics at IROS 2017\, and Finalist for Best Multi-Robot Systems Paper Award at ICRA 2017. He has been interviewed live on the air by BBC Radio and his team’s robots were featured by Today Show and the Boston Globe.
URL:https://ece.northeastern.edu/event/ece-seminar-michael-everett/
LOCATION:442 Dana\, 360 Huntington Ave\, 442 DA\, Boston\, MA\, 02115\, United States
GEO:42.3387508;-71.0923044
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200227T110000
DTEND;TZID=America/New_York:20200227T120000
DTSTAMP:20260423T181256
CREATED:20200221T194910Z
LAST-MODIFIED:20200221T194910Z
UID:4120-1582801200-1582804800@ece.northeastern.edu
SUMMARY:Electrical and Computer Engineering Seminar: Kai Sun
DESCRIPTION:Location: Dana 442 \nSeminar Title: A More Resilient Power Grid with Faster-Than-Real-Time Stability \nAbstract: \nSince the northeast blackout of 1965\, cascading blackouts have continued to happen on power grids in the North America and other countries. For a grid operator\, it is vitally important to be aware of real-time stability and reliability margin for the current grid state under possible disturbances. However\, a real-world power grid is an extremely complex\, nonlinear network system. For instance\, the bulk electric system of a US power grid is typically modeled by nonlinear DAEs on 5\,000+ electric machines and 50\,000+ nodes. Fast stability analysis and simulation of such a large-scale dynamical system subject even to a single disturbance is quite challenging. In the next decade\, renewable generation\, such as power electronics-interfaced distributed energy resources\, will reach 30%-50% in power grids of many countries. That can further increase the complexity of a power grid\, change its dynamic characteristics and bring more uncertainties and challenges to real-time grid operations. For a more resilient grid\, the power industry is looking forward to emerging technologies that enable “faster-than-real-time” stability assessment and adaptive\, distributed control to prevent and mitigate cascading power outages. The speaker will share his visions and research in this field and introduce two promising enabling approaches established with ongoing supports from NSF and DOE: 1) faster-than-real-time grid simulation using a semi-analytical approach\, and 2) grid stability assessment and control based on a new method named “Nonlinear Modal Decoupling” and the utilization of wide-area measurements and distributed energy resources. \nBio:\nKai Sun is an associate professor with the Department of Electrical Engineering and Computer Science in the University of Tennessee\, Knoxville. He is also a faculty member with the NSF/DOE Engineering Research Center for Ultra-Wide-Area-Resilient Electric Energy Transmission Networks (CURENT). He received his Bachelor’s degree in automation in 1999 and his Ph.D. degree in control science and engineering in 2004 both from Tsinghua University\, Beijing. He received the National Top 100 Doctoral Dissertations Award in 2006 from the Ministry of Education of China. Before joining the University of Tennessee\, Dr. Sun was a project manager with the Electric Power Research Institute (EPRI) from 2007 to 2012 for R&D programs in the area of grid operations\, planning and renewable integration. Earlier\, he worked as a research associate at Arizona State University\, Tempe.\nDr. Sun received EPRI Chauncey Award\, the institute’s highest honor\, in 2009\, two best papers awards from IEEE Power & Energy Society General Meetings in 2014 and 2015\, NSF CAREER Award in 2016\, the “Most Valuable Players” Award by North American Synchrophasor Initiative and DOE in 2016\, and the Professional Promise in Research Award twice in 2016 and 2019 by the College of Engineering\, the University of Tennessee. Dr. Sun authored one book titled Power System Control under Cascading Failures and 70+ journal publications. He is currently an associate editor with four IEEE journals including IEEE Transactions on Power Systems\, IEEE Transactions on Smart Grid\, IEEE Access and IEEE Open Access Journal of Power and Energy.
URL:https://ece.northeastern.edu/event/electrical-and-computer-engineering-seminar-kai-sun/
LOCATION:442 Dana\, 360 Huntington Ave\, 442 DA\, Boston\, MA\, 02115\, United States
GEO:42.3387508;-71.0923044
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=442 Dana 360 Huntington Ave 442 DA Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave\, 442 DA:geo:-71.0923044,42.3387508
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200225T160000
DTEND;TZID=America/New_York:20200225T170000
DTSTAMP:20260423T181256
CREATED:20200224T200126Z
LAST-MODIFIED:20200224T200126Z
UID:4123-1582646400-1582650000@ece.northeastern.edu
SUMMARY:ECE Seminar: Faculty Openings in a Unique International Institute for Global Impact in Multidisciplinary Engineering
DESCRIPTION:Philip Krein\, Executive Dean\, Zhejiang University/University of Illinois at Urbana-Champaign Institute \nOverview: \nCome learn about the many open faculty positions at a new campus in China (bring resumes!).  The Zhejiang University/University of Illinois at Urbana-Champaign Institute (ZJUI) is a joint engineering college on the new Zhejiang University (ZJU) International Campus in Haining\, China\, about 120 km southwest of Shanghai. ZJUI is a unique peer partnership of two leading global universities. The programs and research themes build on more than 100 years of University of Illinois involvement in China\, and decades of active research collaborations between the two partners. ZJUI conducts teaching and research in broad program themes of advanced materials and devices engineering sciences; information and data sciences; and energy\, environment\, and infrastructure sciences. Undergraduate and graduate degrees are offered in civil engineering\, computer engineering\, electrical engineering\, and mechanical engineering.  Numerous faculty positions are open in these disciplines\, computer science\, materials engineering\, mathematics\, and related fields. \nA short talk will be given that describes how projects and issues in China illustrate fundamental global development challenges.  Examples are presented in terms of infrastructure\, energy\, environmental impact\, advanced manufacturing\, data sciences\, and other major topics.  In each case\, innovations in the United States and in China have huge potential global impact if they can be scaled up and applied broadly.  The talk discusses how a new generation of science and engineering faculty with multidisciplinary interests and global aspirations will be developed to lead global impact. It will also be an opportunity for Northeastern graduate students to learn more about how to apply for faculty positions at ZJU-UIUC. \nBiography: Philip Krein holds the Grainger Endowed Chair Emeritus in Electric Machinery and Electromechanics at the University of Illinois at Urbana-Champaign. He is also Executive Dean of the Zhejiang University/University of Illinois Institute in Haining\, China\, and a faculty member at Zhejiang University in Hangzhou\, China.  From 2003 to 2014 he was a Founder and Director of SolarBridge Technologies\, Inc.\, Austin\, TX\, a developer of long-life integrated inverters for solar energy. He holds 42 U.S. patents. His current research interests include power electronics\, machines\, electric transportation\, and renewable energy\, with an emphasis on nonlinear control approaches.  Dr. Krein received the IEEE William E. Newell Award in Power Electronics and is a past President of the IEEE Power Electronics Society and a past Chair of the IEEE Transportation Electrification Community.  He is a member of the U.S. National Academy of Engineering\, a fellow of the National Academy of Inventors\, and a Foreign Expert under the China 1000 Talents Program.
URL:https://ece.northeastern.edu/event/ece-seminar-faculty-openings-in-a-unique-international-institute-for-global-impact-in-multidisciplinary-engineering/
LOCATION:442 Dana\, 360 Huntington Ave\, 442 DA\, Boston\, MA\, 02115\, United States
GEO:42.3387508;-71.0923044
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=442 Dana 360 Huntington Ave 442 DA Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave\, 442 DA:geo:-71.0923044,42.3387508
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END:VCALENDAR