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DTSTART;TZID=America/New_York:20221206T133000
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DTSTAMP:20260604T180632
CREATED:20221115T215755Z
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UID:34400-1670333400-1670340600@coe.northeastern.edu
SUMMARY:Enabling Engineering Fall Showcase
DESCRIPTION:Please come to the Enabling Engineering Fall Showcase on Tuesday\, December 6th\, 1:30 -3:30pm ET in 002 Ell Hall where students will present their design projects. \nEnabling Engineering is a Northeastern University student group that designs and builds devices to empower individuals with physical and cognitive disabilities. Our students collaborate with clients on projects that provide greater independence\, reduce medical burdens\, and increase social connectedness. We help family members\, clinicians\, and teachers care for people with disabilities. By giving students the opportunity to participate in Enabling Engineering projects\, we are training the next generation of engineers to be knowledgeable about\, and aware of\, the needs of individuals with disabilities.
URL:https://coe.northeastern.edu/event/enabling-engineering-fall-showcase/
LOCATION:002 Ell Hall\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
ORGANIZER;CN="Enabling Engineering":MAILTO:enable@coe.neu.edu
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DTSTART;TZID=America/New_York:20221206T160000
DTEND;TZID=America/New_York:20221206T173000
DTSTAMP:20260604T180632
CREATED:20221205T210005Z
LAST-MODIFIED:20221205T210005Z
UID:34698-1670342400-1670347800@coe.northeastern.edu
SUMMARY:Md Navid Akbar's PhD Dissertation Defense
DESCRIPTION:“Inference from Brain Imaging: Incorporating Domain Knowledge and Latent Space Modeling” \nAbstract:\n\nBrain imaging can probe the anatomy (structural) of our brain\, or its function (functional). A particular imaging modality (unimodal) generally provides only a particular insight into human health. Transcranial magnetic stimulation (TMS)\, though still in its infancy as a brain imaging modality\, is such a functional\, unimodal technique. TMS helps model human motor-cortical mapping\, using corresponding muscle activity captured by surface electromyography (EMG)\, but it necessitates a reliable data-driven model. Earlier works have modeled the causal direction only (from cortical representation to muscles)\, or the inverse direction (from muscles to cortical representation)\, with simple statistical regression. We modeled this motor-cortical mapping bi-directionally in this dissertation\, using deep learning. We first modeled TMS-induced 3D electric field (E-field) in a brain to causal multi-muscle activation picked up by EMG\, in a regression task using a convolutional neural network (CNN) autoencoder. By fusing neuroscience domain knowledge (e.g.\, an empirical neural response profile)\, we reduced 14% squared error\, compared to the baseline model that did not contain this. We then designed our novel inverse imaging CNN model\, to reconstruct physiologically meaningful E-field distributions (in the image domain) from a given set of muscle activations (in the sensor domain). By adopting variational inference in the CNN model\, to learn the underlying latent space better\, we were able to reduce 13% in squared error over our purely CNN baseline. \nDiagnosis with brain imaging is often incomplete with a unimodal technique\, and having multiple sources (multimodal) may be advantageous. Successful multimodal fusion can provide more holistic information\, compared to its constituents. One relevant example is the classification of late post-traumatic seizure (LPTS). Previous works in this space have tackled LPTS classification with either unimodal functional imaging\, or non-machine learning (ML) structural modeling. In this dissertation\, we first undertook the ML classification of binary LPTS: with unimodal\, structural brain imaging\, namely diffusion magnetic resonance imaging (dMRI). By incorporating interpretable domain knowledge (post-traumatic lesion volume compensation)\, we improved 7% in the mean area under the curve (AUC) over the standard technique in literature. Finally\, we classified LPTS for a larger sample of subjects\, utilizing multimodal imaging\, including functional MRI (fMRI) and electroencephalography (EEG). Following unsupervised imputation for any missing modality within the subjects\, we introduced our novel multimodal fusion algorithm\, which attempts to leverage the underlying structure of the multivariate information. We found that our proposed algorithm improved by 7% in AUC performance\, over a naive Bayesian estimator that can handle missing data intrinsically.\nCollectively\, the work presented here demonstrated that incorporating domain knowledge in the modeling pipeline successfully improved inference. Similar improvements were also observed by learning and leveraging the possible underlying latent structure of the given information\, and adapting the models accordingly. \n\n\n\nCommittee:\n\nProf. Deniz Erdogmus (Advisor) \nProf. Mathew Yarossi (Co-advisor)\nProf. Dominique Duncan\nProf. Sarah Ostadabbas
URL:https://coe.northeastern.edu/event/md-navid-akbars-phd-dissertation-defense/
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DTSTART;TZID=America/New_York:20221206T170000
DTEND;TZID=America/New_York:20221206T190000
DTSTAMP:20260604T180632
CREATED:20221128T164621Z
LAST-MODIFIED:20221128T164621Z
UID:34567-1670346000-1670353200@coe.northeastern.edu
SUMMARY:Offshore Wind Tech Week Network Reception
DESCRIPTION:This event will celebrate #OffshoreWindTechWeek – comprised of the National Offshore Wind R&D Symposium December 5 & 6 and the International Offshore Wind Technical Conference (IOWTC) December 7 & 8. \nThis networking reception will close out the 2022 Symposium and introduce the IOWTC\, happening the following days on December 7 & 8 at Northeastern University. All offshore wind industry professionals are welcome to attend\, regardless of whether they are attending Symposium or IOWTC. \n*Note – All in-person NOWRDC Symposium registrants are automatically registered for this networking reception. \nLocation: Alumni Center at Northeastern University\, 716 Columbus Ave\, Boston\, MA 02120 \nRegister
URL:https://coe.northeastern.edu/event/offshore-wind-tech-week-network-reception/
LOCATION:Alumni Center\, 716 Columbus Ave\, 6th Floor\, Boston\, MA\, 02120\, United States
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221206T203000
DTEND;TZID=America/New_York:20221206T220000
DTSTAMP:20260604T180632
CREATED:20221201T145802Z
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UID:34643-1670358600-1670364000@coe.northeastern.edu
SUMMARY:The "Finals Cookie" with Dean Abowd
DESCRIPTION:Please join us for a relaxing evening before finals begin. We’ll provide hot chocolate and LOTS of cookies. \nHosted by the College of Engineering \nTuesday December 6th from 8:30 to 10:00 pm \nThe Tents at Robinson Quad
URL:https://coe.northeastern.edu/event/the-finals-cookie-with-dean-abowd/
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