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X-WR-CALDESC:Events for Northeastern University College of Engineering
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DTSTART;VALUE=DATE:20201015
DTEND;VALUE=DATE:20201231
DTSTAMP:20260514T030109
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/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201113T080000
DTEND;TZID=America/New_York:20201113T090000
DTSTAMP:20260514T030109
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
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201113T090000
DTEND;TZID=America/New_York:20201113T100000
DTSTAMP:20260514T030109
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
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201113T120000
DTEND;TZID=America/New_York:20201113T130000
DTSTAMP:20260514T030109
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/
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DTSTART;TZID=America/New_York:20201113T140000
DTEND;TZID=America/New_York:20201113T150000
DTSTAMP:20260514T030109
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/
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