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X-ORIGINAL-URL:https://coe.northeastern.edu
X-WR-CALDESC:Events for Northeastern University College of Engineering
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20201015
DTEND;VALUE=DATE:20201231
DTSTAMP:20260523T183630
CREATED:20201015T142444Z
LAST-MODIFIED:20201015T142444Z
UID:22804-1602720000-1609372799@coe.northeastern.edu
SUMMARY:Meet Your Graduate Student Ambassadors!
DESCRIPTION:Meet your Student Ambassadors! Prospective and Admitted Graduate Students are invited to meet their Student Ambassador via Unibuddy.
URL:https://coe.northeastern.edu/event/meet-your-graduate-student-ambassadors/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201110T080000
DTEND;TZID=America/New_York:20201110T090000
DTSTAMP:20260523T183630
CREATED:20201103T153607Z
LAST-MODIFIED:20201103T153607Z
UID:23004-1604995200-1604998800@coe.northeastern.edu
SUMMARY:Civil and Environmental Engineering Webinar
DESCRIPTION:Join faculty staff and current students to learn more about graduate school options in Civil + Environmental Engineering \nTuesday\, November 10 \n8:00 AM EST \nhttps://us02web.zoom.us/webinar/register/WN_Vv1zQp56T1aTOv2k9mlFKQ
URL:https://coe.northeastern.edu/event/civil-and-environmental-engineering-webinar/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201110T120000
DTEND;TZID=America/New_York:20201110T133000
DTSTAMP:20260523T183630
CREATED:20201106T160040Z
LAST-MODIFIED:20201106T160040Z
UID:23078-1605009600-1605015000@coe.northeastern.edu
SUMMARY:GradTalk 101 - Masters Panel
DESCRIPTION:The Graduate Students of Color Collective (GSCC) and the Alliance for Diversity in Science and Engineering (ADSE) would like to invite you to join us for our GradTalk 101 – Masters Panel on Tuesday\, November 10\, 2020\, from 12-1:30 pm. We have some amazing students that offered to share their experiences about their journey through Northeastern and pass off their wisdom to anyone looking to go and obtain a Masters degree. \nVisit https://northeastern.zoom.us/meeting/register/tJckcuuvrzIoH9NlwL7J8UOiVFJXtp0p2jAF to register and submit questions that you would like for our panelist to answer.\nWe look forward to your participation.
URL:https://coe.northeastern.edu/event/gradtalk-101-masters-panel/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201110T190000
DTEND;TZID=America/New_York:20201110T203000
DTSTAMP:20260523T183630
CREATED:20201106T195249Z
LAST-MODIFIED:20201106T195249Z
UID:23094-1605034800-1605040200@coe.northeastern.edu
SUMMARY:Chemical Engineering: Careers & Jobs Discussion with the Industrial Advisory Board Members
DESCRIPTION:This is an opportunity to virtually meet with ChemE Alumni currently working in or retired from industry jobs. \nTopics for Discussion: \n\nSuggestions for job searches in the current pandemic environment\nTraditional Career Paths in Chemical Engineering\nGraduate School\nNon-Traditional Career Paths\nAssessment of Current Industrial Job Opportunities\nConsiderations for Career/ Job Decisions\nSocial and Other Topics\n\nPlease email Alyssa Ramsey at a.ramsey@northeastern.edu for the link to the event.
URL:https://coe.northeastern.edu/event/chemical-engineering-careers-jobs-discussion-with-the-industrial-advisory-board-members/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201111T090000
DTEND;TZID=America/New_York:20201111T100000
DTSTAMP:20260523T183630
CREATED:20201103T153402Z
LAST-MODIFIED:20201103T153402Z
UID:23009-1605085200-1605088800@coe.northeastern.edu
SUMMARY:Information Systems\, Software Engineering Design\, Data Architecture + Management Webinar
DESCRIPTION:Join faculty staff and current students to learn more about graduate school options in \nInformation Systems\, Software Engineering Design\, Data Architecture + Management \nWednesday\, November 11 \n9:00 AM EST \nhttps://us02web.zoom.us/webinar/register/WN_uC78rYvSTqK54AVgMEGi4Q
URL:https://coe.northeastern.edu/event/information-systems-software-engineering-design-data-architecture-management-webinar/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201112T080000
DTEND;TZID=America/New_York:20201112T090000
DTSTAMP:20260523T183630
CREATED:20201103T160901Z
LAST-MODIFIED:20201103T160901Z
UID:23011-1605168000-1605171600@coe.northeastern.edu
SUMMARY:Telecommunication Networks and Cyber Physical Systems Webinar
DESCRIPTION:Join faculty staff and current students to learn more about graduate school options in Telecommunication Networks and Cyber Physical Systems \nThursday\, November 12 \n8:00 AM EST \nhttps://us02web.zoom.us/webinar/register/WN_h3M5RJOPTYyehhi6HF5sLQ
URL:https://coe.northeastern.edu/event/telecommunication-networks-and-cyber-physical-systems-webinar/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201112T120000
DTEND;TZID=America/New_York:20201112T130000
DTSTAMP:20260523T183630
CREATED:20201102T170200Z
LAST-MODIFIED:20201102T170200Z
UID:22980-1605182400-1605186000@coe.northeastern.edu
SUMMARY:CEE Lunch & Learn Seminar Series
DESCRIPTION:The Department of Civil and Environmental Engineering’s Research Affairs Committee (RAC) is pleased to announce our newest seminar series: Lunch & Learn. This bi-monthly lunchtime event will explore interdisciplinary engineering issues\, encouraging collaboration amongst Northeastern colleagues and collaborators on transformative ideas related to CEE and beyond. \n\n\nWe would like to invite you to join us for the inaugural event in this series\, a discussion with CEE Professor Auroop Ganguly and Dr. Evan Kodra of risQ. Their presentation\, Convergent Academic Research to Socially Motivated Startup: the case of Northeastern-spinout risQ\, will explore the development of risQ as a viable business entity capable of maintaining its social mission. 30 minutes of Q&A will follow the presentation. \n\n\n\nTopic: CEE Lunch & Learn: Drs. Ganguly and Kodra \n\n\n\n\n\n\n\nTime: Nov 12\, 2020 12:00 PM Eastern Time (US and Canada) \n\nPlease RSVP to receive a link to participate in this event.
URL:https://coe.northeastern.edu/event/cee-lunch-learn-seminar-series/
ORGANIZER;CN="Civil & Environmental Engineering":MAILTO:civilinfo@coe.neu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201112T130000
DTEND;TZID=America/New_York:20201112T140000
DTSTAMP:20260523T183630
CREATED:20201103T150924Z
LAST-MODIFIED:20201103T150924Z
UID:22987-1605186000-1605189600@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Yaoshen Yuan
DESCRIPTION:PhD Proposal Review: Enhancements for Monte Carlo Light Modeling Method and Its Applications in Near-infrared-based Brain Techniques \nYaoshen Yuan \nLocation: Microsoft Teams Link \nAbstract: Studying light propagation in biological tissues is critical for developing biophotonics techniques and its applications. Monte Carlo (MC) method\, a stochastic solver for radiative transfer equation\, has been recognized as the gold standard for modeling light propagation in turbid media. However\, due to the stochastic nature of MC method\, millions even billions of photons are usually required to achieve accurate results using MC method\, leading to a long computational time even with the acceleration using graphical processing units (GPU).\nFurthermore\, due to the rapid advances in multi-scale optical imaging techniques such as optical coherence tomography (OCT) and multiphoton microscopy (MPM)\, there is an increasing need to model light propagation in extremely complex tissues such as vessel networks. The mesh-based Monte Carlo (MMC) is usually superior than the voxel-based MC method for such modeling since unlike grid-like voxels\, tetrahedral meshes can represent arbitrary shapes with curved boundaries. However\, the mesh density can be excessively high when the tissue structure is extremely complex\, resulting in high computational costs and memory demand. The goal of this proposal is to focus on solving the challenges mentioned above. \nTo tackle the first challenge\, we came up with a filtering approach with GPU acceleration to improve the signal-to-noise ratio (SNR) of the results while keeping the simulated photons low. The adaptive non-local means (ANLM) filter is selected to suppress the stochastic noise in MC results because 1) the filtering process on each voxel is mutually independent\, making it possible for parallel computing; 2) it has high performance in denoising and a strong capacity in edge-preserving. For the second problem\, a novel method\, implicit mesh-based Monte Carlo (iMMC)\, was proposed to significantly reduce the mesh density. The iMMC utilizes the edge\, node and face of the tetrahedral mesh to model tissue structures with shapes of cylinder\, sphere and thin layer. The typical applications for edge\, node and face-based iMMC are vessel networks\, porous media and membranes\, respectively. Lastly\, we applied MC simulations and aforementioned filter on segmented brain models derived from MRI neurodevelopmental atlas to estimate the light dosage for transcranial photobiomodulation (t-PBM)\, a technique for treating major depressive disorder using near infrared\, across lifespan. Furthermore\, a new approach that can improve the penetration depth in optical brain imaging as well as PBM is proposed. In this approach\, the possibility of placing light sources in head cavities is investigated using MC simulations. The preliminary results demonstrate a better performance in deep brain monitoring compared to the standard transcranial approach using 10-20 EEG positioning system. \n 
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-yaoshen-yuan/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201113T080000
DTEND;TZID=America/New_York:20201113T090000
DTSTAMP:20260523T183630
CREATED:20201103T153515Z
LAST-MODIFIED:20201103T215256Z
UID:23007-1605254400-1605258000@coe.northeastern.edu
SUMMARY:Mechanical and Industrial Engineering Webinar
DESCRIPTION:Join faculty staff and current students to learn more about graduate school options in Mechanical + Industrial Engineering \nTuesday\, November 13 \n8:00 AM EST \nhttps://us02web.zoom.us/webinar/register/WN_zBf8jdeiQICLL16poUut1w
URL:https://coe.northeastern.edu/event/mechanical-and-industrial-engineering-webinar/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201113T090000
DTEND;TZID=America/New_York:20201113T100000
DTSTAMP:20260523T183630
CREATED:20201103T160724Z
LAST-MODIFIED:20201103T160724Z
UID:23013-1605258000-1605261600@coe.northeastern.edu
SUMMARY:Electrical and Computer Engineering Webinar
DESCRIPTION:Join faculty staff and current students to learn more about graduate school options in Electrical + Computer Engineering \nFriday\, November 13 \n9:00 AM EST \nhttps://us02web.zoom.us/webinar/register/WN_sBbUcJBJQ_eroL2ll-mjbQ
URL:https://coe.northeastern.edu/event/electrical-and-computer-engineering-webinar-2/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201113T120000
DTEND;TZID=America/New_York:20201113T130000
DTSTAMP:20260523T183630
CREATED:20201113T192414Z
LAST-MODIFIED:20201113T192508Z
UID:23169-1605268800-1605272400@coe.northeastern.edu
SUMMARY:SPIRAL Seminar: Fault-tolerant federated and distributed machine learning
DESCRIPTION:Speaker: Sanmi Koyejo (University of Illinois at Urbana-Champaign)\nTitle: Fault-tolerant federated and distributed machine learning\nTime: Friday\, 11/13\, 12 pm ET/ 11 am CST/ 9 am PT\nLocation: https://northeastern.zoom.us/j/95550946164\nStudent Host: Peng Wu\nFaculty Host: Stratis loannidis \nAbstract:\nMachine learning (ML) models are routinely trained and deployed among devices that are susceptible to hardware/software/communication errors and/or security concerns. Examples include geo-distributed datacenters with non-negligible communication latency\, groups of mobile devices or Internet of Things (IoT)\, and volunteer ML computing. For such settings\, distributed training typically consists of separate updates interleaved with aggregation. To this end\, I will outline novel aggregation schemes for fault-tolerant federated learning and distributed training via stochastic gradient descent. The proposed aggregation schemes are shown to be provably robust to worst-case errors from a large fraction of arbitrarily malicious workers (aka Byzantine errors)\, with minimal effect on convergence rates. Empirical evaluation in a variety of real-world setting further highlights the performance of the proposed aggregation strategies. \nBiography:\nSanmi Koyejo an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Koyejo’s research interests are in developing the principles and practice of adaptive and robust machine learning. Additionally\, Koyejo focuses on applications to neuroscience and biomedical imaging. Koyejo has been the recipient of several awards including a best paper award from the conference on uncertainty in artificial intelligence (UAI)\, a Kavli Fellowship\, an IJCAI early career spotlight\, and a trainee award from the Organization for Human Brain Mapping (OHBM). Koyejo serves on the board of the Black in AI organization. http://sanmi.cs.illinois.edu/bio.html
URL:https://coe.northeastern.edu/event/spiral-seminar-fault-tolerant-federated-and-distributed-machine-learning/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201113T140000
DTEND;TZID=America/New_York:20201113T150000
DTSTAMP:20260523T183630
CREATED:20201109T214923Z
LAST-MODIFIED:20201109T214923Z
UID:23104-1605276000-1605279600@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Yumin Liu
DESCRIPTION:PhD Proposal Review: Learning from Spatio-Temporal Data with Applications in Climate Science \nYumin Liu \nLocation: Zoom Link \nAbstract:Climate change is one of the major challenges to human beings and many other species in our time. In the recent decade\, the number of disasters related to climate change such as wildfires\, storms\, floods and droughts are increasing\, and the casualty and economic losses caused by them are larger compared to those of decades ago. This calls for better and efficient ways to predict climate change in order to better prepare and reduce losses. Predicting climate change involves using historical observational data and model simulated data\, both of which usually involve multiple locations and timestamps and are spatio-temporal. With the rapid development and progress of machine learning\, these methods have achieved several impactful contributions in many domains; we would like to translate its impact to climate science.\nIn this thesis we addressseveral problems in climate science. This challenging complex domain enable us to develop\, innovate\, adapt\, and advance machine learning in the following ways. 1) We develop a multi-task learning method to estimate relationships between tasks and learn the basis tasks in different locations especially for nearby locations which may share similar climate patterns. This method assumes that the weights of an observed task is a linear combination of several latent basis tasks and that the task relationships can be learnt by imposing a regularized precision matrix. 2) We propose a nonparameteric mixture of sparse linear regression models to cluster and identify important climate models for prediction. This model incorporates Dirichlet Process (DP) to automatically determine the number of clusters\, imposes Markov Random Field (MRF) constraints to guarantee spatio-temporal smoothness\, and selects a subset of global climate models (GCMs) that are useful for prediction within each spatio-temporal cluster with a spike-and-slab prior. We derive an effective Gibbs sampling method for this model. 3) We adapt image super resolution method to climate downscaling — increasing spatial resolution for climate variables for local impact analysis. The proposed method is called YNet which is a novel deep convolutional neural network (CNN) with skip connections and fusion capabilities to perform downscaling for climate variables on multiple GCMs directly rather than on reanalysis data.
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-yumin-liu/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20201116
DTEND;VALUE=DATE:20201123
DTSTAMP:20260523T183630
CREATED:20201105T183211Z
LAST-MODIFIED:20201105T183211Z
UID:23076-1605484800-1606089599@coe.northeastern.edu
SUMMARY:Global Entrepreneurship Week 2020
DESCRIPTION:Global Entrepreneurship Week (GEW) is the largest celebration of innovators and entrepreneurs in the world. Sponsored by the Kauffman Foundation\, more than 130 countries participate in GEW each year. Through various programs and events held by businesses\, organizations\, and academic institutions\, GEW week stimulates the entrepreneurial spirit\, inspiring people to create their own startup companies and giving existing entrepreneurs an opportunity to share their expertise. \nVisit the GEW website to view and register for GEW events hosted by groups like IDEA\, Entrepreneurs Club\, NU-Impact\, McCarthy(s) Venture Mentoring Network\, Women Who Empower\, Center for Research Innovation and Health Science Entrepreneurs.
URL:https://coe.northeastern.edu/event/global-entrepreneurship-week-2020/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201116T130000
DTEND;TZID=America/New_York:20201116T143000
DTSTAMP:20260523T183630
CREATED:20201113T202104Z
LAST-MODIFIED:20201113T202104Z
UID:23172-1605531600-1605537000@coe.northeastern.edu
SUMMARY:H-1B and Additional After Graduation Employment-Based Visa Options: Immigration Attorneys present.
DESCRIPTION:This session is only offered once per semester and is open to both NU students and alumni. Available Virtually (not recorded) \nRegistration on NUworks Encouraged: https://northeastern-csm.symplicity.com/students/ \nWant to know your immigration options for after graduation employment? Heard about the H-1B- cap and cap exempt\, L\, E\, TN\, O or other options\, including self-sponsored options and the National Interest Waiver. Learn about employment-based visa options\, including the H-1B\, L\, E\, TN\, O\, and self-sponsored options and the National Interest Waiver.\nWondering what all of those mean and whether there have been any changes to these options\, as well as which ones may work for you? We’ve all been hearing about changes to the L\, as well as possible changes to the H-1B. Hear directly from Immigration Attorneys Richard Iandoli\, Prasant Desai and Attorney Mary Walsh of Iandoli\, Desai & Cronin P.C. and ask questions. Learn what is actually changing versus what has not changed and consider your options for how you can best navigate. Get the right information for you and position yourself for success. This session is only offered once per semester (virtual via zoom). \nHow to attend:\nThis presentation is virtual. Please click on the following link: https://northeastern.zoom.us/j/98862750317 \nThis program connects to the SAIL domain Personal and Professional Effectiveness using strategic thinking\, organizational\, and planning skills. Co-Sponsored with Office of Global Services (OGS). This is part of International Education Week.\nQuestions? Please contact Ellen Zold Goldman: e.goldman@northeastern.edu
URL:https://coe.northeastern.edu/event/h-1b-and-additional-after-graduation-employment-based-visa-options-immigration-attorneys-present/
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20201117
DTEND;VALUE=DATE:20201118
DTSTAMP:20260523T183630
CREATED:20201116T145321Z
LAST-MODIFIED:20201116T145321Z
UID:23179-1605571200-1605657599@coe.northeastern.edu
SUMMARY:3 Minute Thesis Competition - Announcement
DESCRIPTION:The annual GWiSE 3 Minute Thesis Competition 2020 is finally here! The 3MT is an academic competition that challenges Ph.D. students to describe their research within three minutes. This is a great opportunity to practice pitching your research to a non-specialist audience and to improve your science communication. Northeastern GWiSE and the Northeastern University Library have partnered to make 3 Minute Thesis possible with some pretty cool prizes: \n\nFirst place: 100$ Grubhub card\, an interview on the Dean’s podcast\, 100$ credit for 3D printing at the library\nSecond place: 50$ Grubhub card\, an interview on the Dean’s podcast\, 50$ credit for 3D printing at the library\nThird place: 25$ Grubhub card\, an interview on the Dean’s podcast\n\nRSVP to participate here. \nMore details for submission will be sent to those who RSVP. The deadline for video submission is Tuesday\, November 24th via email to gwise.neu@gmail.com. Video requirements\, 3-minute recording over : \n\n1st slide: title and author’s name\n2nd slide: thesis content\n\nThe live event will take place on Wednesday\, December 2nd from 2 PM to 4 PM ET on Zoom! All grad students are welcome to attend and/or present. The event will work in this way: \n\nGWiSE will host the event on Zoom and play prerecorded videos of participants’ explaining their thesis in under 3 minutes\nAfter each video is shown\, the judges will have time to discuss the presentations and assign scores\nGWiSE will proclaim the winners and offer the prizes!\n\nReminder\, please RSVP to participate here. The deadline for video submission is on the 24th of November. To submit your video\, send a video file to gwise.neu@gmail.com. The actual event is on Wednesday\, December 2nd.
URL:https://coe.northeastern.edu/event/3-minute-thesis-competition-announcement/
ORGANIZER;CN="GWiSE%3A Graduate Women in Science and Engineering":MAILTO:gwise.neu@gmail.com
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201117T080000
DTEND;TZID=America/New_York:20201117T090000
DTSTAMP:20260523T183630
CREATED:20201103T160612Z
LAST-MODIFIED:20201103T160612Z
UID:23015-1605600000-1605603600@coe.northeastern.edu
SUMMARY:IEM-sponsored virtual event: Student Experience: Chinese Community
DESCRIPTION:IEM-sponsored virtual event: Student Experience: Chinese Community \nAudience: All admits for Spring\, 2021 including deferrals from a previous term. \nJoin link: https://northeastern.zoom.us/j/99685828902
URL:https://coe.northeastern.edu/event/iem-sponsored-virtual-event-student-experience-chinese-community/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201117T120000
DTEND;TZID=America/New_York:20201117T130000
DTSTAMP:20260523T183630
CREATED:20201110T194535Z
LAST-MODIFIED:20201110T194535Z
UID:23113-1605614400-1605618000@coe.northeastern.edu
SUMMARY:Founder's Roundtable
DESCRIPTION:Founder’s Roundtable inspires faculty entrepreneurship in conjunction with Global Entrepreneurship Week at Northeastern. \nThe event features professors Thomas Webster\, Rupal Patel\, and Sidi Bencherif who will discuss the motivation behind their ventures\, the challenges they face bringing tech to industry\, and the incentives powering their success. James Sherley\, Founder and Director of Asymmetrex\, will moderate the roundtable. \nEvent Details \n\nTuesday\, November 17 \, 2020\nMicrosoft Teams\n12:00 – 1:00 EST\n\nLinks \n\nFounder’s Roundtable LinkedIn Post\nFounder’s Roundtable event page
URL:https://coe.northeastern.edu/event/founders-roundtable/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201117T120000
DTEND;TZID=America/New_York:20201117T130000
DTSTAMP:20260523T183630
CREATED:20201116T150341Z
LAST-MODIFIED:20201116T150341Z
UID:23192-1605614400-1605618000@coe.northeastern.edu
SUMMARY:Faculty Learning Circles: Sharing Strategies & Tips for Teaching in NUflex
DESCRIPTION:For many of us\, this past year has challenged us to quickly adapt to new technologies and modalities of teaching. In the spring\, we made a rapid transition to remote teaching\, and this fall we have tackled Hybrid NUflex. Some have also forayed into the fully online teaching of NU Start courses. What works? Faculty Learning Circles provide an opportunity for us to come together\, pooling our firsthand experience to share strategies and tips. There will also be time to brainstorm solutions to the challenges that we are still in the process of figuring out. \nRegister
URL:https://coe.northeastern.edu/event/faculty-learning-circles-sharing-strategies-tips-for-teaching-in-nuflex/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201118T080000
DTEND;TZID=America/New_York:20201118T090000
DTSTAMP:20260523T183630
CREATED:20201103T160522Z
LAST-MODIFIED:20201103T160522Z
UID:23017-1605686400-1605690000@coe.northeastern.edu
SUMMARY:IEM-sponsored virtual event: Student Experience: Indian Community
DESCRIPTION:November 18: IEM-sponsored virtual event: Student Experience: Indian Community \nAudience: All admits for Spring\, 2021 including deferrals from a previous term. \nJoin link: https://northeastern.zoom.us/j/91065781764
URL:https://coe.northeastern.edu/event/iem-sponsored-virtual-event-student-experience-indian-community/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201118T120000
DTEND;TZID=America/New_York:20201118T130000
DTSTAMP:20260523T183630
CREATED:20200930T184251Z
LAST-MODIFIED:20200930T184251Z
UID:22482-1605700800-1605704400@coe.northeastern.edu
SUMMARY:BioE Seminar Series Presents: Christoph Juchem
DESCRIPTION:Christoph Juchem\, Ph.D. \nAssociate Professor in the Departments of Biomedical Engineering and Radiology\, Columbia University\, New York New York \n“Magnetic Resonance Imaging and B0 Shimming with the Dynamic Multi-Coil Technique (DYNAMITE)” \nABSTRACT:   \nIn my talk\, I will present a technique for B0 magnetic field control that is based on the combination of fields generated by a matrix of small\, individually driven generic coils. This multi-coil approach enables the accurate generation of simple and complex magnetic field shapes in a flexible fashion. B0 shimming with the dynamic multi-coil technique (DYNAMITE) outperforms conventional methods based on spherical harmonic functions and provides unrivaled magnetic field homogeneity in mouse\, rat and human brain. Along with the efficiency gains of DYNAMITE shimming compared to spherical harmonic approaches\, the multi-coil concept has the potential to replace conventional shim systems that are based on sets of dedicated SH coils and allow optimal object-specific shim solutions. The technology furthermore lends itself to spatial encoding. I will present MRI results\, including concomitant imaging and B0 shimming\, in which all fields are purely DYNAMITE-based and conclude with the first realization of DYNAMITE MRI of the in vivo human brain. The obtained image fidelity is comparable to MRI with conventional gradient coils\, paving the way for full-fledged human DYNAMITE MRI systems. \nBIOGRAPHY: \nDr. Juchem is an Associate Professor in the Departments of Biomedical Engineering and Radiology at Columbia University. In his research\, he develops technology and methods to realize the full clinical potential of magnetic resonance applications. Dr. Juchem has 18 years of experience in developing and conducting in vivo MR experiments at 3.0-11.7 Tesla field in humans and animal models. He served as Co-Director of Yale University’s 7T Brain MR Spectroscopy Core\, Chair of the ISMRM Engineering Study group\, and he serves on the editorial board of NMR in Biomedicine. \nIf interested in attending\, please email Elizabeth Chesley at e.chesley@northeastern.edu for the Zoom link.
URL:https://coe.northeastern.edu/event/bioe-seminar-series-presents-christoph-juchem/
ORGANIZER;CN="Bioengineering":MAILTO:bioe@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201118T120000
DTEND;TZID=America/New_York:20201118T130000
DTSTAMP:20260523T183630
CREATED:20201112T201337Z
LAST-MODIFIED:20201112T201337Z
UID:23140-1605700800-1605704400@coe.northeastern.edu
SUMMARY:ChE Seminar Series Presents: Matthew J. Eckelman
DESCRIPTION:Title: TBA \nMatthew J. Eckelman\, Ph.D.\nAssociate Professor\, Civil and Environmental Engineering\nAffiliated Faculty\,  Chemical Engineering\nAffiliated Faculty\,  Marine and Environmental Sciences\nAffiliated Faculty\,  School of Public Policy and Urban Affairs
URL:https://coe.northeastern.edu/event/che-seminar-series-presents-matthew-j-eckelman/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201119T080000
DTEND;TZID=America/New_York:20201119T090000
DTSTAMP:20260523T183630
CREATED:20201103T160421Z
LAST-MODIFIED:20201103T160421Z
UID:23019-1605772800-1605776400@coe.northeastern.edu
SUMMARY:IEM-sponsored virtual event: OGS + Visa Compliance
DESCRIPTION:November 19: IEM-sponsored virtual event: OGS + Visa Compliance \n8:00 AM EST \nJoin link: This event will be run via Unibuddy. Connect with our ambassadors + learn the platform here. \nAudience: All admits for Spring\, 2021 including deferrals from a previous term.
URL:https://coe.northeastern.edu/event/iem-sponsored-virtual-event-ogs-visa-compliance-2/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20201124
DTEND;VALUE=DATE:20201125
DTSTAMP:20260523T183630
CREATED:20201116T145202Z
LAST-MODIFIED:20201116T150907Z
UID:23175-1606176000-1606262399@coe.northeastern.edu
SUMMARY:3 Minute Thesis Competition - Video Submission
DESCRIPTION:The annual GWiSE 3 Minute Thesis Competition 2020 is finally here! The 3MT is an academic competition that challenges Ph.D. students to describe their research within three minutes. This is a great opportunity to practice pitching your research to a non-specialist audience and to improve your science communication. Northeastern GWiSE and the Northeastern University Library have partnered to make 3 Minute Thesis possible with some pretty cool prizes: \n\nFirst place: 100$ Grubhub card\, an interview on the Dean’s podcast\, 100$ credit for 3D printing at the library\nSecond place: 50$ Grubhub card\, an interview on the Dean’s podcast\, 50$ credit for 3D printing at the library\nThird place: 25$ Grubhub card\, an interview on the Dean’s podcast\n\nRSVP to participate here. \nMore details for submission will be sent to those who RSVP. The deadline for video submission is Tuesday\, November 24th via email to gwise.neu@gmail.com. Video requirements\, 3-minute recording over : \n\n1st slide: title and author’s name\n2nd slide: thesis content\n\nThe live event will take place on Wednesday\, December 2nd from 2 PM to 4 PM ET on Zoom! All grad students are welcome to attend and/or present. The event will work in this way: \n\nGWiSE will host the event on Zoom and play prerecorded videos of participants’ explaining their thesis in under 3 minutes\nAfter each video is shown\, the judges will have time to discuss the presentations and assign scores\nGWiSE will proclaim the winners and offer the prizes!\n\nReminder\, please RSVP to participate here. The deadline for video submission is on the 24th of November. To submit your video\, send a video file to gwise.neu@gmail.com. The actual event is on Wednesday\, December 2nd.
URL:https://coe.northeastern.edu/event/3-minute-thesis-competition-video-submission/
ORGANIZER;CN="GWiSE%3A Graduate Women in Science and Engineering":MAILTO:gwise.neu@gmail.com
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201124T140000
DTEND;TZID=America/New_York:20201124T150000
DTSTAMP:20260523T183630
CREATED:20201103T160959Z
LAST-MODIFIED:20201103T160959Z
UID:22989-1606226400-1606230000@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Joseph Robinson
DESCRIPTION:PhD Dissertation Defense: Automatic Face Understanding: Recognizing Families in Photos \nJoseph Robinson \nLocation: Zoom Link \nAbstract: Visual kinship recognition has an abundance of practical uses. For this\, we built the largest database for kinship recognition\, FIW. Built entirely in-house with no cost using a semi-automatic labeling scheme. Specifically\, we first aligned faces detected in family photos with names in the corresponding text metadata to mine the label proposals with high confidence. The remaining data were labeled using a novel clustering algorithm that used label proposals as side information to guide more accurate clusters. Great savings in time and human input was had. Statistically\, FIW shows enormous gains over its predecessors. We have several benchmarks in kinship verification\, family classification\, tri-subject verification\, and large-scale search & retrieval. We also trained CNNs on FIW and deployed the model on the renowned KinWild I and II to gain state-of-the-art (SOTA). Most recently\, we further augmented FIW with multimedia (MM) for 200 of its 1\,000 families- a labeled collection we dubbed FIW-MM. Now\, video dynamics\, audio\, and text captions can be used in the decision making of kinship recognition systems. \nFIW continues to pave the way for this research track: (1) advanced SOTA (e.g.\, marginalized denoising auto-encoder based on metric learning that preserves intrinsic structures of kin-data and encapsulates discriminating information in learned features); (2) introduced generative models to predict a child’s appearance from a parent pair (i.e.\, proposed an adversarial autoencoder conditioned on age and gender to map between facial appearance and these higher-level features for control of age and gender); (3) designed evaluations with benchmarks to support challenges\, workshops\, and tutorials at top tier conferences (e.g.\, CVPR\, MM\, FG\, ICME)\, and a premiere Kaggle Competition. We expect FIW will significantly impact research and reality. \nAdditionally\, we tackled the classic problem of facial landmark localization in images. This is a task that has been in focus for decades\, and many solutions have been proposed. However\, there are revamped interests in pushing facial landmark detection technologies to handle more challenging data with deep networks now prevailing throughout machine learning. A majority of these networks have objectives based on L1 or L2 norms\, which inherit several disadvantages. First of all\, the locations of landmarks are determined from generated heatmaps (i.e.\, confidence maps) from which predicted landmark locations (i.e.\, the means) get penalized without accounting for the spread: a high scatter corresponds to low confidence and vice-versa. To address this\, we introduced a LaplaceKL objective that penalizes for low confidence. Another issue is a dependency on labeled data\, which is expensive to collect and susceptible to error. We addressed both issues by proposing an adversarial training framework that leverages unlabeled data to improve model performance. Our method claims SOTA on renowned benchmarks. Furthermore\, our model is robust with a reduced size: 1/8 the number of channels (i.e.\, 0.0398 MB) is comparable to state-of-that-art in real-time on a CPU. Thus\, our method is of high practical value to real-life applications. \nFinally\, we built the Balanced Faces in the Wild (BFW) dataset to serve as a proxy to measure bias across ethnicity and gender subgroups\, allowing us to characterize FR performances per subgroup. We show performances are non-optimal when a single score threshold is used to determine whether sample pairs are genuine or imposter. Furthermore\, actual performance ratings vary greatly from the reported across subgroups. Thus\, claims of specific error rates only hold for populations matching that of the validation data. We mitigate the imbalanced performances using a novel domain adaptation learning scheme on the facial encodings extracted using SOTA deep nets. Not only does this technique balance performance\, but it also boosts the overall performance. A benefit of the proposed is to preserve identity information in facial features while removing demographic knowledge in the lower dimensional features. The removal of demographic knowledge prevents future potential biases from being injected into decision making. Additionally\, privacy concerns are satisfied by this removal. We explore why this works qualitatively with hard samples. We also show quantitatively that subgroup classifiers can no longer learn from the encodings mapped by the proposed. \n 
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-joseph-robinson/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201125T120000
DTEND;TZID=America/New_York:20201125T130000
DTSTAMP:20260523T183630
CREATED:20201112T163551Z
LAST-MODIFIED:20201112T163551Z
UID:23124-1606305600-1606309200@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Aykut Onol
DESCRIPTION:PhD Dissertation Defense: Planning of Contact-Interaction Trajectories Using Numerical Optimization \nAykut Onol \nLocation: Zoom Link \nAbstract: Dynamic multi-contact behaviors\, such as locomotion and item manipulation\, remain to be a challenge for today’s robotic systems. This is primarily due to the discontinuous and non-smooth dynamics introduced by contacts. For mobile manipulators (e.g.\, humanoids) to become useful for dangerous\, dirty\, and dull tasks\, such as those in disaster response\, they need to be capable of interacting with their cluttered\, constrained\, and changing environments. It is therefore essential to develop methods that would enable robots to plan and execute contact-rich motions in dynamic surroundings.\nIn this dissertation research\, we investigate the planning of contact-interaction trajectories and utilize numerical optimal control techniques to solve this problem in a generalizable and computationally-tractable way. We develop a contact-implicit trajectory optimization framework for the automatic discovery of dynamic contact-rich behaviors given only a high-level goal\, i.e.\, the desired configuration of the environment. A variable smooth contact model is introduced to improve the convergence of gradient-based optimization without compromising the physical fidelity of resulting motions. This is achieved by employing smooth virtual forces that act as a decoupled relaxation of the rigid-body contact model. Second\, we develop a sequential convex optimization procedure that provides reliable convergence characteristics while solving this non-convex problem. Third\, a penalty loop approach is proposed to generalize this method to a wide range of robotic applications.\nIn addition to these\, we develop a novel Coulomb friction model and an on-the-fly contact constraint activation method using state-triggered constraints\, STCs. STCs are a more modular alternative to complementarity constraints which are widely used to model discrete aspects in contact-related problems. Our extensive simulation experiments demonstrate that STCs hold immense promise to efficiently model a broad range of discrete elements in the planning and control of contact-interaction trajectories. As a result\, this dissertation presents methods that enable the planning of dynamic contact-rich behaviors for different robots and tasks without requiring any parameter tuning or tailored initial guess.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-aykut-onol/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201130T093000
DTEND;TZID=America/New_York:20201130T103000
DTSTAMP:20260523T183630
CREATED:20201120T214753Z
LAST-MODIFIED:20201123T155506Z
UID:23266-1606728600-1606732200@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Sila Deniz Calisgan
DESCRIPTION:MS Thesis Defense: MEMS Infrared Resonant Detectors With Near-Zero Power Readout For Miniaturized Low Power Systems \nSila Deniz Calisgan \nLocation: Online \nAbstract: The demand for low-cost and low-power microsystems for spectrally-selective IR sensing has been rising with the proliferation of Internet of Things (IoT) for applications such as security surveillance and natural disaster monitoring. As a result\, there is a need for low-power\, high sensitivity IR sensors with minimum deployment and maintenance cost that can detect trace levels of chemicals. This thesis reports on the first experimental demonstrations of passive integrated microsystems based on transmission spectroscopy using narrowband uncooled microelectromechanical resonant infrared (IR) detectors. Moreover\, the MEMS-CMOS integrated microsystem can turn itself ON to quantify the intensity of infrared radiation when an above-threshold IR signature is present\, but otherwise remain dormant with near-zero standby power consumption. The proposed sensor system combines the unique advantage of two recently developed technologies\, namely\, the zero-power nature of micromechanical photoswitches (MPs) and the high resolution of aluminum nitride (AlN) MEMS resonant infrared detectors\, to achieve an unprecedented IR sensing capability. Thanks to the spectral selectivity enabled by the plasmonically enhanced thermo-mechanical transduction in MEMS structures\, the proposed sensor system is capable of discriminating the spectral content of incoming IR radiation for the identification of events of interest. The prototype presented here is automatically powered up by the MP when the incoming IR radiation exceeds 440 nW showing a high IR detection resolution in active state and a near-zero power consumption (~3 nW) in standby. The ultrathin plasmonic absorber with narrow bandwidth (FWHM<17% ) and near-perfect IR absorption (η>92%) coupled with the high IR detection capability ( NEP~ 463 pW/√Hz) of the AlN resonator was exploited for a filter-free spectroscopic chemical sensor based on uncooled AlN resonant IR detectors with a minimum concentration detection limit of <0.01% (Benzonitrile in Hexane).
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-sila-deniz-calisgan/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201130T130000
DTEND;TZID=America/New_York:20201130T140000
DTSTAMP:20260523T183630
CREATED:20201123T154938Z
LAST-MODIFIED:20201123T154938Z
UID:23276-1606741200-1606744800@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Berkan Kadioglu
DESCRIPTION:PhD Proposal Review: Sample Complexity of Pairwise Ranking Regression \nBerkan Kadioglu \nLocation: Zoom \nAbstract: We consider a rank regression setting\, in which a dataset of $N$ samples with features in $\mathbb{R}^d$ is ranked by an oracle via $M$ pairwise comparisons.\nSpecifically\, there exists a latent total ordering of the samples; when presented with a pair of samples\, a noisy oracle identifies the one ranked higher w.r.t. the underlying total ordering. A learner observes a dataset of such comparisons\, and wishes to regress sample ranks from their features.\nWe show that to learn the model parameters with $\epsilon > 0$ accuracy\, it suffices to conduct $M \in \Omega(dN\log^3 N/\epsilon^2)$ comparisons uniformly at random when $N$ is $\Omega(d/\epsilon^2)$. \n 
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-berkan-kadioglu/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201130T190000
DTEND;TZID=America/New_York:20201130T200000
DTSTAMP:20260523T183630
CREATED:20201123T145333Z
LAST-MODIFIED:20201123T145333Z
UID:23273-1606762800-1606766400@coe.northeastern.edu
SUMMARY:Graduate Women in Science and Engineering (GWiSE) Game Night
DESCRIPTION:Come play jackbox games with GWiSE 11/30 @7PM on Teams! We will vote on which game to play! \nJoin here!
URL:https://coe.northeastern.edu/event/graduate-women-in-science-and-engineering-gwise-game-night/
ORGANIZER;CN="GWiSE%3A Graduate Women in Science and Engineering":MAILTO:gwise.neu@gmail.com
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201201T210000
DTEND;TZID=America/New_York:20201201T210000
DTSTAMP:20260523T183630
CREATED:20201103T160213Z
LAST-MODIFIED:20201103T160213Z
UID:23027-1606856400-1606856400@coe.northeastern.edu
SUMMARY:IEM-sponsored virtual event: Talk to a Regional Campus Advisor: Seattle + Silicon Valley Admitted Students
DESCRIPTION:December 1: IEM-sponsored virtual event: Talk to a Regional Campus Advisor: Seattle + Silicon Valley Admitted Students \n9:00 PM EST \nJoin link: This event will be run via Unibuddy. Connect with our ambassadors + learn the platform here. \nAudience: All admits for Spring\, 2021 including deferrals from a previous term.
URL:https://coe.northeastern.edu/event/iem-sponsored-virtual-event-talk-to-a-regional-campus-advisor-seattle-silicon-valley-admitted-students/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201202T080000
DTEND;TZID=America/New_York:20201202T090000
DTSTAMP:20260523T183630
CREATED:20201103T160131Z
LAST-MODIFIED:20201103T160131Z
UID:23029-1606896000-1606899600@coe.northeastern.edu
SUMMARY:IEM-sponsored virtual event: Discussion: Spring Semester\, What to Expect?
DESCRIPTION:December 2: IEM-sponsored virtual event: Discussion: Spring Semester\, What to Expect? \n8:00 AM EST \nJoin link: This event will be run via Unibuddy. Connect with our ambassadors + learn the platform here. \nAudience: All admits for Spring\, 2021 including deferrals from a previous term.
URL:https://coe.northeastern.edu/event/iem-sponsored-virtual-event-discussion-spring-semester-what-to-expect/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
END:VCALENDAR