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DTSTART;TZID=America/New_York:20210504T183000
DTEND;TZID=America/New_York:20210504T193000
DTSTAMP:20260510T172411
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SUMMARY:CEE Seminar: Cycling for Sustainable Cities
DESCRIPTION:Please consider joining the Department of Civil and Environmental Engineering for this seminar on sustainable urban travel. \nABSTRACT: Cycling is the most sustainable means of urban travel\, practical for most short- and medium-distance trips—commuting to and from work and school\, shopping\, visiting friends—as well as for recreation and exercise. Cycling promotes physical\, social\, and mental health\, helps reduce car use\, enhances mobility and independence\, and is economical for both public and personal budgets. \nThis presentation explores how to make city cycling—the most sustainable means of travel—safe\, practical\, and convenient for all. Buehler and Pucher discuss the latest cycling trends and policies around the world and consider specific aspects of cycling. Taken together\, the presentation demonstrates that successful promotion of cycling depends on a coordinated package of mutually supportive infrastructure\, programs\, and policies. Cycling should be made feasible for everyone and not limited to especially fit\, daring\, well-trained cyclists riding expensive bicycles. \nBios: \nRalph Buehler: Ph.D. is Professor and Chair of Urban Affairs and Planning in the School of Public and International Affairs at Virginia Tech’s Research Center in Arlington\, VA. Most of his research has an international comparative perspective\, contrasting transport and land-use policies\, transport systems\, and travel behavior in Western Europe and North America. Between 2012 and 2018\, he served as chair of the Committee for Bicycle Transportation of the Transportation Research Board (TRB). His research interests include: (1) the influence of transport policy\, land use\, and socio-demographics on travel behavior; (2) active travel and public health; and (3) public transport demand\, supply\, regional coordination\, and financial efficiency. \nJohn Pucher: Ph.D. is professor emeritus at the Bloustein School of Planning and Public Policy at Rutgers University\, in New Jersey.  He was a professor at Rutgers University from 1978 to 2014\, conducting research on urban transportation in the United States\, Canada\, Australia\, and Europe.  Over the past 25 years\, John’s research has focused on walking and bicycling\, and how to improve their safety and convenience for all age groups\, for women as well as men\, and for all levels of physical ability. John has published four books and over 200 articles in academic and professional journals.  His most recent book\, “Cycling for Sustainable Cities\,” was published by MIT Press in February 2021.
URL:https://coe.northeastern.edu/event/cee-seminar-cycling-for-sustainable-cities/
ORGANIZER;CN="Civil & Environmental Engineering":MAILTO:civilinfo@coe.neu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210507T110000
DTEND;TZID=America/New_York:20210507T120000
DTSTAMP:20260510T172411
CREATED:20210506T193919Z
LAST-MODIFIED:20210506T193919Z
UID:25832-1620385200-1620388800@coe.northeastern.edu
SUMMARY:ECE Faculty Seminar: Sumientra Rampersad
DESCRIPTION:Faculty Seminar: Is temporal interference the key to noninvasive deep brain stimulation? Answers from simulation studies in mice and humans. \nSumientra Rampersad \nLocation: Zoom Link \nAbstract: Transcranial current stimulation (tCS) has been used for two decades to noninvasively investigate and influence brain function in both healthy volunteers and clinical populations. While many positive effects have been found\, the goals of high focality\, accurate targeting and deep stimulation are yet to be achieved. Transcranial temporal interference stimulation (tTIS) is a new form of tCS that might improve the method on all three fronts. tTIS uses two alternating currents to create an amplitude-modulated electric field that can peak deep in the brain. A recent murine study showed promising effects of tTIS and concluded that the technique may be used as a noninvasive form of deep brain stimulation in humans\, but results from human experiments have not yet been published. In this talk I will present results of finite element simulations with realistic head models to investigate the electric fields induced by tTIS in the brain\, comparing results in murine and human head models for tTIS and conventional tCS. Due to the nonlinear nature of tTIS\, conventional methods to optimize tCS fields for a specific brain target cannot be used. I will present two nonconvex optimization methods for tTIS and compare their efficiency and results. Finally\, I will discuss the implications of the results of these simulation and optimization studies for potential applications of tTIS in humans. \nBio: Sumientra Rampersad is an Assistant Research Professor in the Department of Electrical and Computer Engineering at Northeastern University in Boston\, where she leads the Brain Stimulation & Simulation Lab. Dr. Rampersad’s research aims to improve understanding of the working mechanisms behind neuromodulation and improve its application using computational methods and experiments with human subjects. She investigates invasive (ECoG\, sEEG) and noninvasive (tCS\, TMS) brain stimulation\, as well as peripheral stimulation\, and is especially interested in bridging the gap between modeling and experiments through model-based experimentation. Her research in collaboration with various academic and clinical partners has been awarded funding by NIA\, NINDS and NIMH. Dr. Rampersad was previously a research scientist in Northeastern’s Cognitive Systems Lab and obtained her PhD at the Radboud University Donders Institute in Nijmegen\, the Netherlands.
URL:https://coe.northeastern.edu/event/ece-faculty-seminar-sumientra-rampersad/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210507T120000
DTEND;TZID=America/New_York:20210507T130000
DTSTAMP:20260510T172411
CREATED:20210427T210636Z
LAST-MODIFIED:20210427T210636Z
UID:25608-1620388800-1620392400@coe.northeastern.edu
SUMMARY:Urban-scale Measurements and Modeling Fate & Transport of PFAS Across Media
DESCRIPTION:Join the Department of Civil and Environmental Engineering and the PROTECT Center for a seminar with the EPA’s Dr. Kiran Alapaty\, who will deliver a talk titled “Urban-scale Measurements and Modeling Fate & Transport of PFAS Across Media.” \nSeminar Abstract: \nPer- and Polyfluoroalkyl Substances (PFAS) have gained attention due to their adverse health effects as well as unknown exposures to legacy and novel compounds. As many of these compounds are stable and persistent\, many PFAS compounds have been detected worldwide across different media in the total environment. Thus\, comprehensive multi-media PFAS chemical concentration data are needed to study PFAS human exposure and health impacts. While some PFAS measurements and exposure studies are available\, no comprehensive PFAS measurement data exist at a continental scale. Also\, it is not clear to the local and federal government agencies as to how to account for the spatiotemporal distributions of PFAS contamination and associated long-term health impacts. Such issues are acute at local to urban scales. Thus\, the in-depth understanding of fate and transport of PFAS across media is much needed and may provide critical information for stakeholders. \nThe Washington Works plant in Parkersburg\, West Virginia has emitted long-chain perfluorooctanoic acid (PFOA) into the environment for decades and at present\, it continues to emit hexafluoropropylene oxide dimer acid [(HFPO-DA)\, GenX]. A database for PFOA at Parkersburg was developed and these PFOA measurements in air\, water\, and soil provide a good opportunity to validate the multi-media modeling system. \nWe are tailoring a robust and efficient suite of modeling tools to simulate PFAS fate and transport in air\, water\, and soil at urban scales. For air\, a state-of-the-art dispersion model (QUIC) is being tested for PFAS air modeling. For other media\, we are testing two state-of-the-art USGS models (MODFLOW and MT3D) for groundwater\, the BreZo model for surface water\, and EPA’s model PRZM-5 for vadose zone. These modeling tools can be used at seasonal to decadal timescales\, and their PFOA estimations can be provided as input data to a high throughput physiologically based pharmacokinetic (PBPK) model to estimate human exposure to PFAS. The combination of multi-media modeling system and PBPK model bridges the gaps between PFAS emissions and human exposure estimates and thus can provide the basis for epidemiological studies. This research opens doors to study the association between human exposure to PFAS and specific human diseases. \nSpeaker Bio:  \nKiran Alapaty is the Senior Science Advisor in the Atmospheric & Environmental Systems Modeling Division in the ORD of US EPA. His research interests are in air quality modeling and model development\, PFAS life cycle modeling\, integrated assessment modeling\, convective cloud parameterization development\, boundary layer modeling\, climate change and exposure science\, and socio-economics. In the past\, he was the Chief of the Climate Branch of the AMA Division with research interests in improving regional climate data for use with exposure science research. \nBefore joining EPA in 2011\, for several years Kiran was at DOE HQ as a Program Director for the DOE’s national climate program managing DOE’s National Labs and research grants.  Prior to that\, he was also a Program Director at the National Science Foundation managing climate research at NCAR and academia. \nKiran holds an MS in Aerospace Engineering from the Indian Institute of Sciences and a PhD in Atmospheric Science from North Carolina State University. \n  \nAbout the Lunch & Learn Seminar Series: \nA new Bimonthly seminar series from the Department of Civil and Environmental Engineering (CEE)\, focusing on convergent research\, bringing together Northeastern colleagues and collaborators to think big/bold\, explore ideas that build cooperation\, and foster transformative innovation within CEE and across disciplines beyond CEE. \nDue to COVID restrictions\, this event remains virtual-only. Please bring your own lunch and join us online.
URL:https://coe.northeastern.edu/event/urban-scale-measurements-and-modeling-fate-transport-of-pfas-across-media/
ORGANIZER;CN="Civil & Environmental Engineering":MAILTO:civilinfo@coe.neu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20210510
DTEND;VALUE=DATE:20210515
DTSTAMP:20260510T172411
CREATED:20210421T202724Z
LAST-MODIFIED:20210421T203805Z
UID:25558-1620604800-1621036799@coe.northeastern.edu
SUMMARY:Huskies Wellness Week
DESCRIPTION:Welcome to Huskies Wellness Week\, a personal retreat that will leave you feeling refreshed and empowered from the comfort of your own home. Build some ‘you time’ into your days through a lineup of exclusive programming\, hosted by the Northeastern Boston Community. Share your progress with our Instagram gameboard—designed for us to stay on track together—for the chance to win a prize. \nHow do I play? \nOn Sunday\, May 9 we’re posting a gameboard on our Instagram account for the chance to win a $100 gift card to the bookstore. Complete 7 squares for one entry into our challenge\, and 10 squares for two entries. Send us a screenshot of the gameboard with your completed tiles checked off or with pictures of your activity overlayed on top by the following Sunday\, May 16 at 12pm EST to be entered into the raffle. \nThe winner will be selected on Monday\, May 17 by 12pm EST and contacted via email by Ilana Gensler\, MA’19\, Assistant Director\, Affinity and Domestic Engagement. \nYour completed board can be sent directly to @northeastern_alumni through direct message on Instagram. Be sure to share your progress throughout the week on Instagram by tagging @northeastern_alumni. \nWhat if I have a private account?\nUpon registering you will be asked to provide your Instagram handle. You will receive a follow-request ahead of Huskies Wellness Week. \nSessions\nViewing in Eastern Time \n\n\n\nActivate your Full Potential\n5/10/21\n8:30 AM-9:30 AM ET \nLeena Prabhoo\, MEd’90\nManaging Partner\nPath to Prajñā\nSolutions LLP \nEnhance your personal and professional wellness by learning how to activate your full potential. This session will explore what it means to be living your full potential and give you the tools to act so you can make it a reality. Learn about some of the benefits you can gain from this process and develop your personal game-plan for moving forward on this journey.\n\n\n\nMeditation to Cultivate Peace of Mind\n5/12/21\n3:00 PM-4:00 PM ET \nStacy Hernandez\, AS’98\, MS’01\nOwner/College\nCounselor\nThe Best U \nSettle in for a 40-minute meditation session to cultivate peace of mind in service of your mental health. When you dedicate time exploring within\, you learn to listen to your inner voice rather than the influences outside of you. This internal reconnection can help you activate the power you have inside of yourself to stay grounded and bring enhanced mindfulness to every element of your life as you move through each day.\n\n\n\nMOVE by The Handle Bar\n5/14/21\n8:00 AM-8:45 AM ETAnthony Charter\nIndoor Cycling Instructor\nThe Handle Bar Indoor Cycling Studio \nMOVE is a 45-minute\, total-body workout that combines high-intensity plyometric movement with slow-burning kettlebell strength work. It fuses The Handle Bar’s passion for music-driven exercise with thoughtful programming that complements and enhances the studio’s work on the bike. Class will be accessible for 48 hours after it goes Live at the time of the event.\n\n\n\nVinyasa Flow Yoga\n5/14/21\n12:00 PM-12:30 PM ET \nH Alex Harrison\, JD’11\nYoga Instructor\nBeacon Hill Yoga \nMake space both physically and mentally as we explore and connect with our bodies through the synthesis of yoga\, movement\, breath\, and mindfulness exercises. Expect to flow through traditional yoga postures as we explore the intricacies of skeletal alignment and the muscular engagement required to link pose to pose\, and end with a restful savasana.\n\n\n\n\nRegister Now
URL:https://coe.northeastern.edu/event/huskies-wellness-week/
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20210513
DTEND;VALUE=DATE:20210515
DTSTAMP:20260510T172411
CREATED:20210513T192140Z
LAST-MODIFIED:20210513T192140Z
UID:25946-1620864000-1621036799@coe.northeastern.edu
SUMMARY:AMGEN Lecture Series: Biotechnology Edge
DESCRIPTION:This two-day lecture series is presented by AMGEN scientists\, in partnership with the School of Pharmacy and Bouvé College of Health Sciences Dean’s office. It will provide participants with an overview of the drug development process\, as well as the biotechnology industry and potential career paths. Lectures will be presented live online.  Recording is not allowed. See the full program below. \nThe lectures will be in real time on WebEx.  A link will be sent to everyone who has registered. Attendees of the two-day lecture series will be eligible to earn a Badge. \nRSVP \n\n\n\n\n\n\n\nThursday May 13th 2021\, 9.00 am – 4.30 pm\n\n\n9.00\nIntroduction\n\n\n9.10\nLife of a Drug\, Roger Hart\, PhD\n\n\n10.00\nDrug Discovery at Amgen: A Multi‐Modality Approach\, Roger Hart\, PhD\n\n\n10.50\nBreak and Informational Session\n\n\n11.10\nProcess Development from Clinic to Approval\, Jennifer Litowski\,Process Development Principal Scientist\n\n\n12.00\nLunch and Learn\n\n\n1.00\nTarget Identification & Validation\, John Ferbas\, PhD\,\n\n\n1.50\nIntroduction to Pharmaceutical Solid-State Chemistry and MaterialsScience\, Hyunsoo Park\, Process Development Principal  Scientist\n\n\n2.40\nBreak\n\n\n2.50\nFrom Bench to Bedside: Discovery of the First FDA‐ ApprovedAntibody Therapeutic for Migraine\, Cen Xu\, PhD\n\n\n3.40\nThe Role of Continuous Manufacturing to Advance Amgen’s SyntheticPortfolio\, Matt Beaver\, Principal Scientist\n\n\n\n\n\n\nFriday\, May 14th 2021\, 9.00 am – 4.30 pm\n\n\n9.00\nIntroduction\n\n\n9.10\nDrug Safety: An Industry Perspective Oluwadamilola Ogunyankin\,MD\,MPH\n\n\n10.00\nModeling of Processes\, Products and Devices for Drug Development &Manufacturing\, Pablo Rolandi\, Director Data Sciences\n\n\n10.50\nBreak and Informational Session\n\n\n11.10\nRaw Material Selection and Control for ManufacturingPharmaceuticals\, Susan Burke\, PhD\n\n\n12.00\nLunch and Learn\n\n\n1.00\nOncology\, Kristin Tarbell\, Principal Scientist\n\n\n1.50\nInnovations in Device Technologies for Delivering Biologics to Patients\,Shirish Ingawale\, PhD\n\n\n2.40\nBreak\n\n\n2.50\nDigital Transformation in Biopharmaceutical Operations\, Myra Coufal\,PhD\n\n\n3.40\nCareers in Biotech\, Jessica  Smith\,  Process  Development  AssociateScientist
URL:https://coe.northeastern.edu/event/amgen-lecture-series-biotechnology-edge/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210513T140000
DTEND;TZID=America/New_York:20210513T150000
DTSTAMP:20260510T172411
CREATED:20210503T135624Z
LAST-MODIFIED:20210510T135607Z
UID:25648-1620914400-1620918000@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Siyue Wang
DESCRIPTION:PhD Proposal Review: Towards Robust and Secure Deep Learning Models and Beyond \nSiyue Wang \nLocation: Zoom Link \nAbstract: Modern science and technology witness the breakthroughs made by deep learning during the past decades. Fueled by rapid improvements of computational resources\, learning algorithms\, and massive amount of data\, deep neural networks (DNNs) have played a dominant role in more and more real-world applications. Nonetheless\, there is a spring of bitterness mingling with this remarkable success – recent studies reveals that there are two main security threats of DNNs which limit its widespread usage: 1) the robustness of DNN models under adversarial attacks\, and 2) the protection and verification of intellectual properties of well-trained DNN models. \nIn this dissertation\, we fist focus on the security problems of how to build robust DNNs under adversarial attacks\, where deliberately crafted small perturbations added to the clean input can lead to wrong prediction results with high confidence. We approach the solution by incorporating stochasticity into DNN models. We propose multiple schemes to harden the DNN models when facing adversarial threats\, including Defensive Dropout (DD)\, Hierarchical Random Switching (HRS)\, and Adversarially Trained Model Switching (AdvMS). \nThe second part of this dissertation focuses on how to effectively protect the intellectual property for DNNs and reliably identify their ownership. We propose Characteristic Examples (C-examples) for effectively fingerprinting DNN models\, featuring high-robustness to the well-trained DNN and its derived versions (e.g. pruned models) as well as low-transferability to unassociated models. The generation process of our fingerprints does not intervene with the training phase and no additional data are required from the training/testing set.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-siyue-wang/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210521T110000
DTEND;TZID=America/New_York:20210521T120000
DTSTAMP:20260510T172411
CREATED:20210503T135740Z
LAST-MODIFIED:20210503T135740Z
UID:25650-1621594800-1621598400@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Daniel Uvaydov
DESCRIPTION:MS Thesis Defense titled DeepSense: Fast Wideband Spectrum Sensing Through Real-Time In-the-Loop Deep Learning \nDaniel Uvaydov \nLocation: Microsoft Teams \nAbstract: Spectrum sharing will be a key technology to tackle spectrum scarcity in the sub-6 GHz bands. To fairly access the shared bandwidth\, wireless users will necessarily need to quickly sense large portions of spectrum and opportunistically access unutilized bands. The key unaddressed challenges of spectrum sensing are that (i) it has to be performed with extremely low latency over large bandwidths to detect tiny spectrum holes and to guarantee strict real-time digital signal processing (DSP) constraints; (ii) its underlying algorithms need to be extremely accurate\, and flexible enough to work with different wireless bands and protocols to find application in real-world settings. To the best of our knowledge\, the literature lacks spectrum sensing techniques able to accomplish both requirements. In this paper\, we propose DeepSense\, a software/hardware framework for real-time wideband spectrum sensing that relies on real-time deep learning tightly integrated into the transceiver’s baseband processing logic to detect and exploit unutilized spectrum bands. DeepSense uses a convolutional neural network (CNN) implemented in the wireless platform’s hardware fabric to analyze a small portion of the unprocessed baseband waveform to automatically extract the maximum amount of information with the least amount of I/Q samples. We extensively validate the accuracy\, latency and generality performance of DeepSense with (i) a 400 GB dataset containing hundreds of thousands of WiFi transmissions collected “in the wild” with different Signal-to-Noise-Ratio (SNR) conditions and over different days; (ii) a dataset of transmissions collected using our own software-defined radio testbed; and (iii) a synthetic dataset of LTE transmissions under controlled SNR conditions. We also measure the real-time latency of the CNNs trained on the three datasets with an FPGA implementation\, and compare our approach with a fixed energy threshold mechanism. Results show that our learning-based approach can deliver a precision and recall of 98% and 97% respectively and a latency as low as 0.61ms.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-daniel-uvaydov/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210525T100000
DTEND;TZID=America/New_York:20210525T110000
DTSTAMP:20260510T172411
CREATED:20210517T134657Z
LAST-MODIFIED:20210517T134657Z
UID:25994-1621936800-1621940400@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Mohammad Hossein Hajkazemi
DESCRIPTION:PhD Dissertation Defense: High-performance Translation Layers for Cloud Immutable Storage \nMohammad Hossein Hajkazemi \nLocation: Zoom Link \nAbstract: Most storage interfaces support in-place updates: blocks can be rewritten\, files can be modified at byte granularity\, fields may be updated in database table rows. Yet internally these layers often rely on out-of-place (immutable) writes. In some cases\, this may be necessary to use media\, such as flash\, SMR (shingled magnetic recording) and IMR (interlaced magnetic recording) disk\, which do not allow overwrites. In others\, it is used to simplify the implementation of transactions and/or crash consistency\, in the form of journaling\, write-ahead logging\, shadow paging\, etc. \nIn a storage system\, translation layers perform out-of-place writes\, and they are implemented in different layers of storage stack from the file system to the storage device firmware depending on the application. In this dissertation I focus on translation layers for cloud immutable storage technologies to improve the cloud I/O performance. As a part of my thesis\, I focus on translation layers for state-of-the-art immutable storage media such as SMR and IMR used in cloud environments\, proposing several novel algorithms to improve their efficiency. I also introduce FSTL\, a framework to design and implement SMR translation layer. Finally\, I describe Collage\, a virtual disk I developed over S3 (could be implemented over a similar object storage) using a translation layer which performs large\, sequential\, out-of-place writes for high performance. It optionally uses fast local storage for write logging and as a write-back cache\, guaranteeing prefix consistency under all failure conditions and recovery of all acknowledged writes if the local cache is not lost. Collage supports snapshots and cloned volumes\, performs well over erasure-coded storage\, and allows consistent asynchronous volume replication over geographic distances. I show that Collage can achieve massive performance improvements (e.g.\, over 100x for microbenchmarks and 10x for macro-benchmarks) over CEPH RBD\, a popular open-source scale-out virtual disk implementation.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-mohammad-hossein-hajkazemi/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210526T130000
DTEND;TZID=America/New_York:20210526T140000
DTSTAMP:20260510T172411
CREATED:20210524T182653Z
LAST-MODIFIED:20210524T182653Z
UID:26078-1622034000-1622037600@coe.northeastern.edu
SUMMARY:PhD Dissertation Defense: Kunpeng Li
DESCRIPTION:PhD Dissertation Defense: Visual Learning with Limited Supervision \nKunpeng Li \nLocation: Zoom Link \nAbstract: Deep learning models have achieved remarkable success in many computer vision tasks. However\, they typically rely on large amounts of carefully labeled training data whose annotating process is usually expensive\, time-consuming and even infeasible when considering the task complexity and scarcity of expert knowledge.\nIn this dissertation talk\, I will discuss several explorations along the direction of visual learning with limited supervision. They are mainly about learning from data with weak forms of annotations and learning from multi-modal data pairs. Specifically\, I will first present a guided attention learning framework to conduct semantic segmentation by mainly using image-level labels\, as such weak form of annotation can be collected much more efficiently than pixel-level labels. Under mild assumptions\, our framework can also be used as a plug-in to existing convolutional neural networks to improve their generalization performance. This is achieved by guiding the network to focus on correct things when learning concepts from a limited set of training samples. Besides\, I will also introduce models that can effectively learn from multi-modal data pairs without relying on dense annotations of visual semantic concepts. Our models incorporate relational reasoning ability into the visual representation learning process so that it can be better aligned with the supervision from corresponding text descriptions.
URL:https://coe.northeastern.edu/event/phd-dissertation-defense-kunpeng-li/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210527T090000
DTEND;TZID=America/New_York:20210527T180000
DTSTAMP:20260510T172411
CREATED:20210512T175130Z
LAST-MODIFIED:20210520T192306Z
UID:25900-1622106000-1622138400@coe.northeastern.edu
SUMMARY:NanoSI 2021 Annual Workshop on Nano Systems Innovation
DESCRIPTION:NanoSI 2021 Annual Workshop on Nano Systems Innovation \nThursday\, May 27th\, 2021 \nRegistration: Zoom Link \nLocation: Once registered\, a Zoom Link will be available. \nDescription: The goal of the Northeastern SMART Annual Workshop on Nano Systems Innovation (NanoSI 2021) is to bring together researchers\, government and industry to discuss new strategies to address the growing demand of sensing\, communication and artificial intelligence at the chip-scale while reducing the time for innovation and transition of the new foundational nano-system technologies that are going to be the at root of our nation’s economic strength\, national security and technological standing in the years to come. \nPreliminary Agenda:\n8:50 am – 9:00 am: Check-in\n9:00 am – 10:00 am: Opening remarks from University and SMART Center Leadership\, DARPA PMs\n10:00 am – 10:30 am: Plenary Talk – David Horsley\n10:30 am – 11:00 am: Intros from Industrial Partners\n11:00 am – 12:00 pm: Center Projects Presentations\n12:00 pm – 1:00 pm: Lunch Break\n1:00 pm – 3:00 pm: Center Projects Presentations\n3:00 pm – 4:30 pm: Panel Discussion with DARPA\, Industry\, and Academia: Benjamin Griffin\, Ronald Polcawich\, Amit Lal\, Troy Olsson\, David Horsley\n4:30 pm – 4:45 pm: Closing Remarks\n4:50 pm – 5:50 pm: IAB Meeting (Members Only)\n6:00 pm: Meeting adjourn \n  \n 
URL:https://coe.northeastern.edu/event/nanosi-2021-annual-workshop-on-nano-systems-innovation/
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