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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220111T110000
DTEND;TZID=America/New_York:20220111T170000
DTSTAMP:20260506T071310
CREATED:20220110T144722Z
LAST-MODIFIED:20220110T144722Z
UID:29837-1641898800-1641920400@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Sungho Kang
DESCRIPTION:PhD Dissertation Defense: Plasmonically Enhanced Infrared Sensing Microsystems \nSungho Kang \nLocation: Zoom Link \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 defense\, novel development of miniaturized IR spectroscopic sensor and maintenance-free wireless human sensors based on the two key technologies are presented: (1) multispectral resonant IR detector array and (2) plasmonically-enhanced long-wavelength infrared micromechanical photoswitch. This study shows that the demonstrated technologies can replace the traditional IR sensors with the new generation IR sensing microsystems that are characterized by their high performance\, compact form factor\, power efficiency and low cost.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-sungho-kang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220114T140000
DTEND;TZID=America/New_York:20220114T150000
DTSTAMP:20260506T071310
CREATED:20220118T143826Z
LAST-MODIFIED:20220118T143826Z
UID:29870-1642168800-1642172400@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Andac Demir
DESCRIPTION:PhD Dissertation Defense: Automated Bayesian Network Exploration for Nuisance-Robust Inference \nAndac Demir \nLocation: Zoom Link \nAbstract: A fundamental challenge in the analysis of physiological signals is learning useful features that are robust to nuisance factors e.g.\, inter-subject \& inter-session variability\, and achieve the highest nuisance-invariant classification performance. Towards resolving this problem\, we introduce 3 frameworks: AutoBayes\, which is an AutoML approach to conduct neural architecture search for research prototyping\, and GNN based frameworks: EEG-GNN and EEG-GAT.\nThe ultimate goal of the AutoBayes framework is to identify the conditional relationship between a physiological dataset\, associated task labels\, nuisance variations and potential latent variables in order to robustly infer the task labels invariant of nuisance factors. Nuisance factors in the case of physiological datasets could be variations in subjects or sessions\, but we only focus on subject variations in the experiments. AutoBayes enumerates all plausible Bayesian networks between data\, labels\, nuisance variations and potential latent variables\, detects and prunes the unnecessary edges according to Bayes-Ball Algorithm\, and then trains the resulting DNN architectures for different hyperparameter configurations in an adversarial / non-adversarial or a variational / non-variational setting to achieve the highest validation performance. Instead of hyperparameter tuning for model optimization\, AutoBayes concentrates on the architecture search of plausible Bayesian networks\, and achieves state-of-the-art performance across several physiological datasets. Furthermore\, we ensemble several Bayesian networks by stacking their posterior probability vectors in a higher level learning space\, train a shallow MLP as a meta learner\, and measure the task and nuisance classification performance on a hold-out dataset. We observe that exploration of different inference Bayesian networks has a significant benefit in improving the robustness of the machine learning pipeline\, and the parallel activity of vast assemblies of different Bayesian network models significantly reduces variation across subjects in the cross-validation setting.\nIn the second part of the dissertation\, we benchmark the performance of EEG-GNN and EEG-GAT against the AutoBayes framework. CNN’s have been frequently used to extract subject-invariant features from EEG for classification tasks\, but this approach holds the underlying assumption that electrodes are equidistant analogous to pixels of an image and hence fails to explore/exploit the complex functional neural connectivity between different electrode sites. We overcome this limitation by tailoring the concepts of convolution and pooling applied to 2D grid-like inputs for the functional network of electrode sites. Furthermore\, we develop various GNN models that project electrodes onto the nodes of a graph\, where the node features are represented as EEG channel samples collected over a trial\, and nodes can be connected by weighted/unweighted edges according to a flexible policy formulated by a neuroscientist. The empirical evaluations show that our proposed GNN-based framework\, EEG-GNN\, outperforms standard CNN classifiers across ErrP and RSVP datasets\, as well as allowing neuroscientific interpretability and explainability to deep learning methods tailored to EEG related classification problems. Besides that\, EEG-GAT employs multi-head attention mechanism in conjunction with the GNN architecture to learn the graph topology of observations instead of utilizing a graph shift operator that is heuristically constructed by a domain expert. This implicitly allows the exploration of the functional neural connectivity peculiar to a cognitive task between pairs of EEG electrode sites as well as EEG channel selection\, which is critical for reducing computational cost\, and designing portable EEG headsets.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-andac-demir/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220119T180000
DTEND;TZID=America/New_York:20220119T190000
DTSTAMP:20260506T071310
CREATED:20211215T192248Z
LAST-MODIFIED:20211215T192248Z
UID:29742-1642615200-1642618800@coe.northeastern.edu
SUMMARY:COE Global Co-op Info Session
DESCRIPTION:Join the College of Engineering Global Co-op team in learning about global co-op opportunities for Summer II/Fall 2022. \nTopics discussed will include: \n\nSearch techniques and global positions in your field\nWhat to consider when interested in a global co-op\nLogistics for moving and living abroad\nTips and resources for self-developing global positions\n\nAttendance to one of these sessions is required if you plan to do a global co-op in Summer II/Fall 2022. \nRSVP on the NUworks Events Calendar. \nPlease reach out to Sally Conant\, Global Co-op Coordinator\, s.conant@northeastern.edu or Kristina Kutsukos\, Global Co-op Coordinator\, k.kutsukos@northeastern.edu for additional information
URL:https://coe.northeastern.edu/event/coe-global-co-op-info-session-5/
LOCATION:Raytheon Amphitheater (240 Egan)\, 360 Huntington Ave\, 240 Egan\, Boston\, MA\, 02115\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220120T110000
DTEND;TZID=America/New_York:20220120T120000
DTSTAMP:20260506T071310
CREATED:20220106T144246Z
LAST-MODIFIED:20220106T144246Z
UID:29829-1642676400-1642680000@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Vedant Sumaria
DESCRIPTION:PhD Proposal Review: Exploring Micro-Machined Glass Shell Resonators For Sensor Application \nVedant Sumaria \nLocation: Zoom \nAbstract: Optical resonators have been playing an important role in modern optics. They are fundamental in any laser device\, etalon for optical filtering\, accurate measurement for non-linear optics. Bulk optical resonators that use two or more mirrors are usually used in all branches of modern linear and non-linear optics. There are many limitations in using such systems because they cannot provide high performance (high quality (Q) factor) and their size\, weight\, and alignment\, creates stability problems. To solve these problems\, there was an emerging class of miniaturized dielectric cavity based optical resonators that exploited the light confinement phenomenon through internal reflection. These resonators have a circular symmetry\, and they sustain modes known as the Whispering Gallery Modes (WGM) that is nothing but electromagnetic waves that circulate and are confined within the structure. Fabrication of these dielectric optical resonators is simpler and comparatively inexpensive. They demonstrate higher mode stability and higher performance. \nIn this proposal review\, I will discuss the working principles of a WGM resonator and study the various loss mechanisms to improve the quality factor. Further I will discuss the fabrication of on chip glass-blown microspherical shell resonators. These on-chip spherical glass shells are micrometers to millimeters in diameter with ultra-smooth surfaces and micrometer wall thicknesses which can sustain optical resonance modes with high Q-factors up to 50 million. Further we discuss various methods used to etch the backside silicon to create a liquid core optical resonator. This etching leads to increase in the surface roughness leading to loss of resonance. We optimized etching methods and parameters to keep the resonance as high as 18 million. By etching the silicon resonator’s temperature sensitivity is improved from -1.15 GHz/K to 2.23 GHz/K. This optical WGM sensor is then novel biosensor consisting of a chip-scale whispering gallery mode resonators with High-Q factor and a micro-caloric system. The silicon released shell resonator is elastically coupled to a kapton tubing system. Temperature change in the system induces thermal expansion and thermorefractive changes which can be sensitively monitored through changes in the optical resonance characteristics. We demonstrate a measurement resolution less than 10mK and a method of measuring temperature change to eliminate background noise that shows a great potential for detection of various biomolecules such as urea. We also discuss the possibility to use the sensor as an extremely sensitive IR sensor. Finally\, we talk about the future work in immobilization of urease and glucose oxidase to test for analytes like urea and glucose with concentrations in micro-mole.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-vedant-sumaria/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220120T153000
DTEND;TZID=America/New_York:20220120T163000
DTSTAMP:20260506T071310
CREATED:20220111T151529Z
LAST-MODIFIED:20220111T151529Z
UID:29839-1642692600-1642696200@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Hamed Mohebbi Kalkhoran
DESCRIPTION:PhD Proposal Review: Machine Learning Approaches for Classification of Myriad Underwater Acoustic Events Over Continental-shelf Scale Regions with Passive Ocean Acoustic Waveguide Remote Sensing \nHamed Mohebbi Kalkhoran \nLocation: Zoom Link \nAbstract: Underwater acoustic data contain a myriad of sound sources that include bioacoustics related to marine life such as marine mammals and fishes; man-made such as ships\, sonar\, and airguns; as well as natural geophysical processes such as earthquake\, hurricane\, and volcanic eruption. Among underwater acoustic events\, marine mammal vocalization classification is one of the most challenging problems due to their transient broadband calls\, high variation in the calls of a specie (intra-class variation)\, and high similarity between the calls of some species. In this thesis\, we investigate machine learning approaches for classifying marine mammal vocalizations for real-time applications. We utilize acoustic data from a 160-element coherent hydrophone array and employ the passive ocean acoustic waveguide remote sensing technique to enable sensing and detections over instantaneous wide areas more than 100 km in diameter from the array. A variety of computational accelerating approaches\, combining hardware and software\, that make the methods desirable for real-time applications are also developed.\nHumpback whale behavior\, population distribution and structure can be inferred from long term underwater passive acoustic monitoring of their vocalizations. Here we employ machine learning approaches to classify humpback whale vocalizations into song and non-song calls. We use wavelet signal denoising and coherent array processing to enhance the signal-to-noise ratio. To build features vector for every time sequence of the beamformed signals\, we employ Bag of Words approach to time-frequency features. Finally\, we apply Support Vector Machine (SVM)\, Neural Networks\, and Naive Bayes to classify the acoustic data and compare their performances. Best results are obtained using Mel Frequency Cepstrum Coefficient (MFCC) features and SVM which leads to 94% accuracy and 72.73% F1-score for humpback whale song versus non-song vocalization classification.\nTo classify a large variety of whale species by their calls\, we extracted time-frequency features from Power Spectrogram Density (PSD) of the beamformed signals. Then we used these features to train three classifiers\, which are SVM\, Neural Networks\, and Random forest to classify six whale species: Fin\, Sei\, Blue\, Minke\, Humpback\, and general Odontocetes. Best results were obtained with Random forest classifier\, which achieved 95% accuracy\, and 85% F1 score. To detect transient sound sources\, first we applied Per-Channel Energy Normalization (PCEN) on the PSD of the beamformed signals. We applied thresholding on the PCEN data followed by morphological image opening to find potential sound sources and reduce noisy detections. Then we applied connected component analysis to obtain the final detected sounds for each bearing. To estimate the Direction of Arrival (DoA) of detected sounds\, we applied non-maximum suppression (NMS)\, which is widely used in object detection applications in computer vision\, on the detected sounds. We used mean power of each detected sound as the scores for NMS. To speed up the data processing\, we investigated a variety of accelerating approaches\, such as analyzing the effect of floating point precision\, applying parallel processing\, and implementing fast algorithms to run on GPU.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-hamed-mohebbi-kalkhoran/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220121T150000
DTEND;TZID=America/New_York:20220121T160000
DTSTAMP:20260506T071310
CREATED:20211220T144658Z
LAST-MODIFIED:20211220T144658Z
UID:29778-1642777200-1642780800@coe.northeastern.edu
SUMMARY:Disability rights with Mrs. Christine Griffin
DESCRIPTION:Learn about disability rights with a Nationally recognized lawyer on Friday\, 21st January at 3 p.m. in ISEC 655\, 6th floor or virtually through zoom – https://northeastern.zoom.us/j/95320296228 \n 
URL:https://coe.northeastern.edu/event/disability-rights-with-mrs-christine-griffin/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220124T130000
DTEND;TZID=America/New_York:20220124T140000
DTSTAMP:20260506T071310
CREATED:20220118T182834Z
LAST-MODIFIED:20220118T182834Z
UID:29883-1643029200-1643032800@coe.northeastern.edu
SUMMARY:ECE Seminar: Nathan Lazarus
DESCRIPTION:ECE Seminar: Stretchable Magnetics for Soft Robotics \nNathan Lazarus \nLocation: Zoom Link \nAbstract: Recent innovations in making robots from softer biofriendly materials have opened broad new applications ranging from medicine to agriculture. Due to the reliance of much of the field on pneumatic actuation\, heavy and rigid pumps\, and control circuitry for driving pressure chambers have become a major limitation for fully soft\, untethered soft robots. In my talk\, I will discuss all aspects of creating soft electromagnets\, inductors and power circuits for electromagnetic actuation and power management in stretchable systems. Using unconventional materials like room temperature liquid metals and ferrofluids\, we demonstrate record performance for a stretchable inductor. These stretchable inductors are then used to create flexible and stretchable pumps with flow rates nearly two orders of magnitude higher than past demonstrations in the literature and integrated into a simple soft robot demonstrator. \nBio: Nathan Lazarus has worked extensively in areas ranging from mixed signal electronics to MEMS fabrication\, with his Ph.D. at Carnegie Mellon culminating in 2012 with the demonstration of the highest recorded fractional sensitivity to date for a capacitive chemical sensor topology integrated with CMOS electronics. Since joining US Army Research Laboratory in May 2012\, Dr. Lazarus’s research has focused on stretchable power electronics\, soft robotics and 3D printing. He has received numerous awards including ARL’s Honorary Award for Engineering and the Rookie of the Year Excellence in Federal Career Award (Gold) from the Baltimore Federal Executive Board. In 2019\, Dr. Lazarus was selected for the Presidential Early Career Award for Scientists and Engineers (PECASE)\, the highest honor given by the US government for researchers beginning their independent research careers.
URL:https://coe.northeastern.edu/event/ece-seminar-nathan-lazarus/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220125T120000
DTEND;TZID=America/New_York:20220125T130000
DTSTAMP:20260506T071310
CREATED:20211215T192318Z
LAST-MODIFIED:20211215T192318Z
UID:29746-1643112000-1643115600@coe.northeastern.edu
SUMMARY:COE Global Co-op Info Session
DESCRIPTION:Join the College of Engineering Global Co-op team in learning about global co-op opportunities for Summer II/Fall 2022. \nTopics discussed will include: \n\nSearch techniques and global positions in your field\nWhat to consider when interested in a global co-op\nLogistics for moving and living abroad\nTips and resources for self-developing global positions\n\nAttendance to one of these sessions is required if you plan to do a global co-op in Summer II/Fall 2022. \nRSVP on the NUworks Events Calendar. Location- Curry Student Center 333. \nPlease reach out to Sally Conant\, Global Co-op Coordinator\, s.conant@northeastern.edu or Kristina Kutsukos\, Global Co-op Coordinator\, k.kutsukos@northeastern.edu for additional information.
URL:https://coe.northeastern.edu/event/coe-global-co-op-info-session-6/
LOCATION:Curry Student Center\, 360 Huntington Ave.\, Boston\, MA\, 02115\, United States
GEO:42.3394629;-71.0885286
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220126T120000
DTEND;TZID=America/New_York:20220126T130000
DTSTAMP:20260506T071310
CREATED:20220120T190850Z
LAST-MODIFIED:20220120T202413Z
UID:29913-1643198400-1643202000@coe.northeastern.edu
SUMMARY:Materials Exhibiting Biomimetic Carbon Fixation: Kinetic Analysis\, Mechanistic Insights\, and Material Design
DESCRIPTION:ChE Seminar Series Presents: \nDorsa Parviz\, Ph.D. \nDepartment of Chemical Engineering\, Massachusetts Institute of Technology \n Abstract: \nPopulation growth and climate change necessitate a paradigm shift from current chemical and materials production methods to more sustainable approaches with a negative carbon footprint. In view of this\, I will introduce carbon fixing materials (CFM) as a new synthetic platform that\, like plants\, utilize sunlight to photocatalytically reduce ambient CO2 and add to an ever-extending carbon backbone. First\, I will describe a mathematical framework enveloping the main functions of carbon fixing materials to answer basic questions about the kinetics regimes of operation\, photocatalytic requirements\, and limits of functional materials in CFMs. I will also present mechanistic insights on the photocatalytic reduction of CO2 to C1 intermediates as desired intermediates for producing value-added products from CO2. In the second part of my talk\, I will focus on state-of-the-art 2D nanomaterials and strategies for surface engineering these materials in the colloidal state\, addressing challenges in their characterization for applications in photocatalysis. \nBio: \nDorsa Parviz is a postdoctoral researcher at the Massachusetts Institute of Technology\, working with Prof. Michael Strano in the Department of Chemical Engineering. She earned her Ph.D. in 2016 from Texas A&M University under the guidance of Prof. Micah Green\, where she pioneered techniques for high-yield production of 2D nanomaterials\, investigated their colloidal interactions and assembly\, and designed tailored nanosheet-based polymer composites and 3D networks for structural and electrode applications. During her postdoc\, she developed carbon fixing materials at MIT\, establishing a high-throughput photocatalytic reaction screening system to accomplish this vision. In addition\, she has led the research on the preparation and characterization of biocompatible engineered 2D nanomaterials with tailored structure and properties for nanotoxicity studies at NIEHS Nanosafety Center. \nIf unable to attend in person\, please contact a.ramsey@northeastern.edu for the seminar link.
URL:https://coe.northeastern.edu/event/materials-exhibiting-biomimetic-carbon-fixation-kinetic-analysis-mechanistic-insights-and-material-design/
LOCATION:024 East Village\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
GEO:42.3396156;-71.0886534
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=024 East Village 360 Huntington Ave Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave:geo:-71.0886534,42.3396156
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220127T110000
DTEND;TZID=America/New_York:20220127T120000
DTSTAMP:20260506T071310
CREATED:20220125T181649Z
LAST-MODIFIED:20220125T181649Z
UID:29951-1643281200-1643284800@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Meruyert Assylbekova
DESCRIPTION:PhD Proposal Review: Aluminum Nitride and Scandium-doped Aluminum Nitride Materials and Devices for Beyond 6 GHz Communication \nMeruyert Assylbekova \nLocation: Zoom Link \nAbstract: With almost all of the sub-­6 GHz spectrum now being allocated\, current bandwidth shortage has motivated the exploration of untapped frequencies beyond 6 GHz for future broadband wireless communication. Shift to higher frequency spectra is expected to deliver a significant performance improvement in network capacity\, data rates\, latency\, and coverage. These refinements will enable the development of new life­changing technologies such as Vehicle to Everything (V2V to V2X)\, ubiquitous Internet of Things (IoT)\, and Augmented and Virtual reality (AR and VR). Among a variety of novel 5G applications\, the implementation of 5G mobile broadband imposes especially demanding specifications on Radio Frequency Front­End (RFFE) architectures. 5G smartphones are expected to carry over the legacy sub-­6 GHz bands\, which translates into an increased number of filters.\nIn this context\, the first part of this work will introduce lithographically defined Aluminum Nitride (AlN) piezoelectric microacoustic resonators as a promising solution for the implementation of future minituarized adaptive RFFEs.\nWhile AlN has been a material of choice for acoustic filters for over two decades\, future technologies are calling for a material with superior piezoelectric strength. It has been shown that the piezoelectric activity of AlN can be enhanced by partially substituting Al with Sc to form AlScN. Thus\, the second part of this work will explore material properties of AlScN along with the challenges that need to be addressed to take full advantage of its piezoelectric and ferroelectric strength. Last\, AlScN resonators and filters will be demonstrated as promising candidates for the future beyond 6GHz technologies.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-meruyert-assylbekova/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220127T170000
DTEND;TZID=America/New_York:20220127T180000
DTSTAMP:20260506T071310
CREATED:20211215T192337Z
LAST-MODIFIED:20211215T192337Z
UID:29749-1643302800-1643306400@coe.northeastern.edu
SUMMARY:COE Global Co-op Info Session
DESCRIPTION:Join the College of Engineering Global Co-op team in learning about global co-op opportunities for Summer II/Fall 2022. \nTopics discussed will include: \n\nSearch techniques and global positions in your field\nWhat to consider when interested in a global co-op\nLogistics for moving and living abroad\nTips and resources for self-developing global positions\n\nAttendance to one of these sessions is required if you plan to do a global co-op in Summer II/Fall 2022. \nRSVP on the NUworks Events Calendar. Location- Curry Student Center 333. \nPlease reach out to Sally Conant\, Global Co-op Coordinator\, s.conant@northeastern.edu or Kristina Kutsukos\, Global Co-op Coordinator\, k.kutsukos@northeastern.edu for additional information
URL:https://coe.northeastern.edu/event/coe-global-co-op-info-session-7/
LOCATION:Curry Student Center\, 360 Huntington Ave.\, Boston\, MA\, 02115\, United States
GEO:42.3394629;-71.0885286
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Curry Student Center 360 Huntington Ave. Boston MA 02115 United States;X-APPLE-RADIUS=500;X-TITLE=360 Huntington Ave.:geo:-71.0885286,42.3394629
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220127T170000
DTEND;TZID=America/New_York:20220127T180000
DTSTAMP:20260506T071310
CREATED:20220124T145449Z
LAST-MODIFIED:20220124T145449Z
UID:29926-1643302800-1643306400@coe.northeastern.edu
SUMMARY:How to Make Compelling Figures- a Data Visualization Workshop
DESCRIPTION:In this interactive virtual workshop\, we’ll walk you through the steps of creating and revising compelling data visualizations and graphics. \nTo get the most out of the workshop\, please bring a visualization that you would like to improve and use in your own work. The visualization can be anything from a table of numbers\, to a graph\, to an illustration or diagram. The visualization does not have to be polished and can even be an informal sketch of a visual you would like to make in the future. \nClick HERE to Register via Zoom \nThis workshop is sponsored by the CommLab and presented by Kate Kryder\, the Data Analysis and Visualization Specialist at Northeastern University Library.
URL:https://coe.northeastern.edu/event/how-to-make-compelling-figures-a-data-visualization-workshop/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220127T173000
DTEND;TZID=America/New_York:20220127T190000
DTSTAMP:20260506T071310
CREATED:20220124T145646Z
LAST-MODIFIED:20220124T145646Z
UID:29921-1643304600-1643310000@coe.northeastern.edu
SUMMARY:Galante Engineering Business Program Info Session
DESCRIPTION:Northeastern University’s Galante Engineering Business Program offers a progressive opportunity for engineering students to complement their technical engineering education with business skills by earning a graduate certificate in engineering business. Galante is founded on the values of student engagement and leadership to strengthen interpersonal and professional skills. Programmatic elements are offered to students such as workshops\, speaker series\, site visits\, seminars\, and other related personal and professional development activities as a connected cohort. \nThe Info Session Event is an opportunity for CoE students to learn about the Galante Engineering Business Program\, the opportunities it provides\, the benefits offered\, the application process\, and more. This event will be hosted on Thursday\, January 27th (01/27/22) from 5:30-7:00pm in Egan 440. Attire is business casual. Please be sure to RSVP\, and please be sure to reach out to Program Assistant Bradley Miller (b.miller@northeastern.edu) for questions and additional information\, or visit our website.
URL:https://coe.northeastern.edu/event/galante-engineering-business-program-info-session/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220128T180000
DTEND;TZID=America/New_York:20220128T200000
DTSTAMP:20260506T071310
CREATED:20211206T192907Z
LAST-MODIFIED:20211206T192907Z
UID:29676-1643392800-1643400000@coe.northeastern.edu
SUMMARY:Graduate Student of Color Collective 6th Annual Multicultural Mixer
DESCRIPTION:Mark your calendars for a cultural mix and mingle! GSCC will be hosting its 6th Annual Multicultural Mixer on Friday\, January 28\, 2022.
URL:https://coe.northeastern.edu/event/graduate-student-of-color-collective-6th-annual-multicultural-mixer/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220131T130000
DTEND;TZID=America/New_York:20220131T140000
DTSTAMP:20260506T071310
CREATED:20220127T214437Z
LAST-MODIFIED:20220127T214437Z
UID:29977-1643634000-1643637600@coe.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://coe.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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