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X-WR-CALDESC:Events for Northeastern University College of Engineering
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201113T120000
DTEND;TZID=America/New_York:20201113T130000
DTSTAMP:20260424T223315
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:20260424T223315
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:20260424T223315
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:20260424T223315
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:20260424T223315
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:20260424T223315
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:20260424T223315
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:20260424T223315
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:20260424T223315
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:20260424T223315
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:20260424T223315
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:20260424T223315
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:20260424T223315
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:20260424T223315
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:20260424T223315
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:20260424T223315
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:20260424T223315
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:20260424T223315
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:20260424T223315
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:20260424T223315
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
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201202T100000
DTEND;TZID=America/New_York:20201202T110000
DTSTAMP:20260424T223315
CREATED:20201116T203509Z
LAST-MODIFIED:20201116T203509Z
UID:23195-1606903200-1606906800@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Leili Hayati
DESCRIPTION:PhD Dissertation Defense: Ceramic Magnetic Wires at Wireless Communication Frequencies \nLeili Hayati \nLocation: Online \nAbstract: Ferrite magnetic devices play an important role in modern wireless telecommunication systems. They generally require permanent magnets in order to magnetically polarize the ferrite material component used in these devices. The permanent magnets are bulky and take up most of the size and weight of a magnetic circuit. The aim of this research is to do away with permanent magnet bias circuits as utilized in circulators and ferrite planar devices\, especially in wireless communication systems operating below 2 GHz. Recently\, ferromagnetic nanowires (NWs) have been embedded into porous templates\, are used to design various microwave magnetic and electronics devices. The main advantage of magnetic NWs is that in zero magnetic field\, the microwave absorption frequency can be easily tuned over a large range of frequencies. Clearly\, the metallic nature of the magnetic NWs contributed to the high loss. It is expected that insulating magnetic NWs will improve the insertion loss sufficiently to produce viable ferrite devices at wireless communication frequencies below 2GHz and at higher frequencies. There are no pure insulating magnetic materials. However\, there are ferrites that are nearly insulating and are ferrimagnetic. Their saturation magnetization is much lower than the metallic ferromagnetic counterpart. This is a desirable property for magnetic device operating below 2 GHz. Of all the ferrite materials yttrium iron garnet (YIG) exhibits the lowest FMR linewidth ever measured and low saturation magnetization. In this work\, an array of high-purity YIG NWs embedded in a porous silicon membrane\, were synthesized using sol-gel method and the magnetic properties of the pure YIG Nanoparticles and the composite substrate were characterized by utilizing vibrating sample magnetometer (VSM) technique. From the ferromagnetic resonance (FMR) spectra\, it has been found that the measurements are characterized by a uniaxial magnetic anisotropy energy due to the high aspect ratio of the NWs. Based on the magnetic parameters of the composite substrate and characterizing YIG NWs\, a coplanar waveguide was designed by HFSS software. By applying a small external magnetic field and changing the internal magnetic H field by ±8%\, the phase of S21 parameter shifts up to 30̊ degrees near 1.7GHz.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-leili-hayati/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201202T100000
DTEND;TZID=America/New_York:20201202T110000
DTSTAMP:20260424T223315
CREATED:20201118T212728Z
LAST-MODIFIED:20201118T212728Z
UID:23232-1606903200-1606906800@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Bilgehan Donmez
DESCRIPTION:PhD Dissertation Defense: Topology Error Detection in Power System State Estimation \nBilgehan Donmez \nLocation: Teams Link \nAbstract: Growth of renewable energy\, changes in weather patterns\, and increases in cyber- and physical-attacks are examples of recent challenges in power system operation. To keep up with these rapid transformations\, it is imperative to improve the tools used in modern-day control centers.\nAs the centerpiece of system operations\, improvements in state estimation (SE) accuracy would result in better situational awareness for system operators. The state estimate can often be compromised when there are errors in the assumed topology of the network. Therefore\, topology error detection plays a key role in SE. In the first part of this dissertation\, topology errors in the external systems\, which are the neighboring control areas\, are investigated. When a subset of measurements coming from an external area is lost\, some parts of the system can become unobservable. Since SE cannot be carried out for the unobservable portion of the system\, the topology of the external system cannot be tracked in its usual way. This dissertation offers a computationally efficient external line outage detection algorithm that uses only the internal bus phase angles\, any available phasor measurement units (PMUs)\, and the pre-contingency system topology of the system. Coupled with a post-verification step\, this method is shown to be effective in detecting external line outages.\nThe second part of the dissertation focuses on topology errors in the internal system. The conventional SE implementations use the simplified bus-branch (BB) electrical network provided by the topology processor (TP). When the status of circuit breakers are not reported correctly to the TP\, the electrical equivalent it creates will be inaccurate. Therefore\, topology errors usually result in SE convergence problems or yield significantly biased estimates. To properly detect these types of errors\, rather than using the typical BB representation\, the network model is expanded to include circuit breakers and other switching devices in substations. SE is then reformulated to work with this detailed node-breaker (NB) model.\nAlthough the expansion of the model introduces operational and computational challenges\, several strategies are employed to counter these issues. The proposed innovations include the formulations of two separate equality-constrained SE algorithms\, the development of optimal meter placement algorithms\, and utilization of parallel processing. As demonstrated through the simulations conducted\, the methods developed in this dissertation are practical enough for adaptation to real-world systems.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-bilgehan-donmez/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201202T120000
DTEND;TZID=America/New_York:20201202T130000
DTSTAMP:20260424T223315
CREATED:20200930T184350Z
LAST-MODIFIED:20200930T184350Z
UID:22484-1606910400-1606914000@coe.northeastern.edu
SUMMARY:BioE Seminar Series Presents: Jessica Wagenseil
DESCRIPTION:Jessica Wagenseil\, Ph.D. \nAssociate Professor\, Department of Mechanical Engineering and Materials Science\, Washington University\, St. Louis MO \n“Elastin mechanobiology in aortic development” \nABSTRACT:   \nThe extracellular matrix protein\, elastin\, provides reversible extensibility to the aorta that is critical for proper function of the cardiovascular system. Elastin is deposited during late embryonic and early postnatal growth\, at the same time that blood pressure and flow are increasing. This relationship suggests that mechanobiological signals for elastin deposition are linked to hemodynamic forces. I will discuss how reduced or absent elastin affects aortic mechanics\, cardiovascular hemodynamics\, and aortic wall development in genetically modified mouse models. I will also discuss how altered hemodynamics\, specifically reduced blood flow\, affects elastin amounts\, aortic mechanics\, and wall development in developing chick embryos. I will introduce mathematical models that we use to better understand the cause and effect relationships between elastin amounts and cardiovascular hemodynamics. The combination of experimental work in diverse animal models and mathematical modeling will advance our understanding of how the aortic wall is constructed to provide appropriate extensibility for normal cardiovascular function. The knowledge will aid in designing tissue-engineered arteries and in the treatment of cardiovascular diseases associated with elastin defects \nBIOGRAPHY: \nDr. Wagenseil joined the Mechanical Engineering and Materials Science Department at Washington University in 2013. She was previously in the Biomedical Engineering Department at Saint Louis University. She got her B.S. at UC San Diego and did her doctoral and postdoctoral training at Washington University. Dr. Wagenseil studies vascular mechanics focusing on the extracellular matrix in development and disease. Dr. Wagenseil received the 2020 Renato Iozzo Award from the American Society for Matrix Biology \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-jessica-wagenseil/
ORGANIZER;CN="Bioengineering":MAILTO:bioe@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201202T140000
DTEND;TZID=America/New_York:20201202T150000
DTSTAMP:20260424T223315
CREATED:20201130T151126Z
LAST-MODIFIED:20201130T151212Z
UID:23312-1606917600-1606921200@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Kathan Vyas
DESCRIPTION:MS Thesis Defense: Data-Efficient analysis of Human Behavior by Spatio-Temporal Pose Generation and Inference \nKathan Vyas \nLocation: Zoom Link  \nPasscode: 474462 \nAbstract: Identifying human pose over time provides critical information towards understanding human behavior and their physical interaction with the environment surrounding them. In the past few decades\, the human pose estimation topic has witnessed groundbreaking research in the computer vision field thanks to the powerful deep learning models. These models are trained using several thousands of labeled sample images if not more. Such extensive data requirement posed a fundamental problem for domains (i.e. Small Data domains)\, in which data collection or labeling is expensive or limited due to privacy or security concerns such as healthcare. In this thesis\, we present a data-efficient learning pipeline to address small data problem in a healthcare-related human pose estimation application. In particular\, we infer spatio-temporal human poses to analyze typical vs. atypical behaviors in children with Autism spectrum disorder (ASD). To mitigate data limitation\, we propose two thrusts in our learning pipeline. The first thrust is a data-efficient machine learning approach\, in which a pre-trained (on adult pose images) pose estimation model with deep structure is fine-tuned on a small set of children pose videos\, provided to us by our collaborators. We implement a non-linear particle filter interpolation to deal with any missing body keypoints in the estimated poses and employ a novel PoTion (pose motion) based temporal aggregation technique to evaluate poses over time. The second thrust is a synthetic data augmentation approach\, in which we build a framework to create synthetic 3D humans with articulated bodies in order to render more pose images/videos in our application contexts. We use a novel 3D registration approach based on RANSAC and implement iterative closest point (ICP) to obtain 3D meshes from the scanned point clouds from both adult and kid mannequins\, which is then rigged and articulated in the Blender to generate our human avatars. We then infuse these avatars in various synthetic environments to create contexts similar to the target application\, which is a kid with both typical and atypical behaviors in a home-like environment.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-kathan-vyas/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201202T140000
DTEND;TZID=America/New_York:20201202T160000
DTSTAMP:20260424T223315
CREATED:20201116T145518Z
LAST-MODIFIED:20201116T145518Z
UID:23183-1606917600-1606924800@coe.northeastern.edu
SUMMARY:3 Minute Thesis Competition
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/
ORGANIZER;CN="GWiSE%3A Graduate Women in Science and Engineering":MAILTO:gwise.neu@gmail.com
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201203T080000
DTEND;TZID=America/New_York:20201203T090000
DTSTAMP:20260424T223315
CREATED:20201103T160053Z
LAST-MODIFIED:20201103T160053Z
UID:23031-1606982400-1606986000@coe.northeastern.edu
SUMMARY:IEM-sponsored virtual event: OGS + Visa Compliance
DESCRIPTION:December 3: 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/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201203T180000
DTEND;TZID=America/New_York:20201203T200000
DTSTAMP:20260424T223315
CREATED:20201125T173252Z
LAST-MODIFIED:20201125T173252Z
UID:23297-1607018400-1607025600@coe.northeastern.edu
SUMMARY:ADSE Trivia Night
DESCRIPTION:Join ADSE for a graduate student virtual trivia night on December 3rd from 6:00-8:00 pm and help us support a local Black-owned business as well as raise money for the Read in Color program from Little Free Library! Form a team of up to 4 members and answer a total of 30 questions on variable topics. \n1st place: $25 to each team member\n2nd place: $20 to each team member\n3rd place: $15 to each team member \n*$5 gift cards from Delectable Desires will be sent to your email address after you attend the event (first 25 people). \nRSVP here: https://docs.google.com/forms/d/e/1FAIpQLSfXwFoSYPkKZCf8wXfmq2qnVrhRIU2GXL5jERp3bIVYpdM9Eg/viewform
URL:https://coe.northeastern.edu/event/adse-trivia-night/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201204T150000
DTEND;TZID=America/New_York:20201204T160000
DTSTAMP:20260424T223315
CREATED:20201123T155204Z
LAST-MODIFIED:20201123T155204Z
UID:23279-1607094000-1607097600@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Yuxuan Cai
DESCRIPTION:MS Thesis Defense: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design \nYuxuan Cai \nLocation: Zoom Link \nAbstract: The rapid development and wide utilization of object detection techniques have aroused attention on both accuracy and speed of object detectors. However\, the current state-of-the- art object detection works are either accuracy-oriented using a large model but leading to high latency or speed-oriented using a lightweight model but sacrificing accuracy. In this work\, we propose YOLObile framework\, a real-time object detection on mobile devices via compression compilation co-design. A novel block-punched pruning scheme is proposed for any kernel size. To improve computational efficiency on mobile devices\, a GPU-CPU collaborative scheme is adopted along with advanced compiler-assisted optimizations. Experimental results indicate that our pruning scheme achieves 14× compression rate of YOLOv4 with 49.0 mAP. Under our YOLObile framework\, we achieve 17 FPS inference speed using GPU on Samsung Galaxy S20. By incorporating our proposed GPU-CPU collaborative scheme\, the inference speed is increased to 19.1 FPS\, and outperforms the original YOLOv4 by 5× speedup.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-yuxuan-cai/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201207T080000
DTEND;TZID=America/New_York:20201207T090000
DTSTAMP:20260424T223315
CREATED:20201103T160007Z
LAST-MODIFIED:20201103T160007Z
UID:23033-1607328000-1607331600@coe.northeastern.edu
SUMMARY:IEM-sponsored virtual event: About Hybrid NU-Flex
DESCRIPTION:December 7: IEM-sponsored virtual event: About Hybrid NU-Flex \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-about-hybrid-nu-flex-2/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201207T160000
DTEND;TZID=America/New_York:20201207T170000
DTSTAMP:20260424T223315
CREATED:20201130T145726Z
LAST-MODIFIED:20201130T145726Z
UID:23310-1607356800-1607360400@coe.northeastern.edu
SUMMARY:ECE PhD Proposal Review: Trinayan Baruah
DESCRIPTION:PhD Proposal Review: Improving the Virtual Memory Efficiency of GPUs \nTrinayan Baruah \nLocation: Zoom Link \nAbstract: GPUs have been adopted widely based their ability to exploit data-level parallelism found in modern-day applications\, ranging from high performance computing to machine learning. This widespread adoption has\, in part\, been accelerated by the development of more intuitive high-level programming languages\, efficient runtimes and drivers\, and easier mechanisms to manage data movement. Modern day GPUs and multi-GPU systems utilize virtual memory systems\, enabling programmers to access large address spaces that are beyond the physical memory limits a GPU. There mechanisms have built in mechanisms for memory translation\, sparing the programmer from having to reason about complex data-movement operations. Virtual memory support on a GPU includes both hardware and software support. At the hardware level\, Translation Lookaside Buffers (TLBs) are used to cache translations close to the compute units. At the software level\, the programming model supports a unified memory model which automates the movement of pages across multiple devices in a a system. Despite the improvements in programmability\, due to the inefficiency in existing TLB mechanisms for TLB management and page migration\, the performance of current virtual memory support on GPUs is sub-optimal.\nIn this dissertation\, we first identify the key challenges in virtual memory support for GPUs today. We then propose mechanisms to reduce the bottlenecks arising from virtual memory management at both a hardware level and at the runtime level. This allows GPUs to fully enjoy the benefits of virtual memory\, while ensuring high performance. We also develop simulation tools that enable researchers to explore new and novel virtual memory features in future single GPU and multi-GPU systems.\nTo enhance hardware support for virtual memory on a GPU\, we explore a mechanism that enables prefetching of page-table entries into the GPUs TLBs\, thereby reducing the number of TLB misses and improving performance. We also leverage the fact that many page-table entries can be shared across different GPU cores. We design a low-cost interconnect that enables sharing of page-table entries across the GPU cores. To improve the performance of unified memory on multi-GPU systems\, we propose a hardware/software mechanism that monitors accesses to each page\, and uses this information when making page-migration decisions. We also propose mechanisms to reduce the cost of TLB shootdowns on the GPU during page-migration in NUMA multi-GPU systems. \n 
URL:https://coe.northeastern.edu/event/ece-phd-proposal-review-trinayan-baruah/
END:VEVENT
END:VCALENDAR