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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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DTSTART;VALUE=DATE:20201015
DTEND;VALUE=DATE:20201231
DTSTAMP:20260422T015519
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:20201112T080000
DTEND;TZID=America/New_York:20201112T090000
DTSTAMP:20260422T015519
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
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DTSTART;TZID=America/New_York:20201112T120000
DTEND;TZID=America/New_York:20201112T130000
DTSTAMP:20260422T015519
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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
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DTSTART;TZID=America/New_York:20201112T130000
DTEND;TZID=America/New_York:20201112T140000
DTSTAMP:20260422T015519
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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/
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