BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Northeastern University College of Engineering - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Northeastern University College of Engineering
X-ORIGINAL-URL:https://coe.northeastern.edu
X-WR-CALDESC:Events for Northeastern University College of Engineering
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230618T080000
DTEND;TZID=America/New_York:20230623T170000
DTSTAMP:20260413T013318
CREATED:20230127T165522Z
LAST-MODIFIED:20230127T165522Z
UID:35294-1687075200-1687539600@coe.northeastern.edu
SUMMARY:Reconnect Workshop 2023: Risk Assessment
DESCRIPTION:Where: The Omni Hotel\, Providence\, Rhode Island   \nThis year’s workshop is focused on Risk Assessment. Risk assessment is an overall process of identifying\, analyzing\, and preparing for potential risks (e.g.\, natural or man-made disasters)\, which is extremely important to ensure the continuity of organization operations and the well-being of the people involved. Risk assessment goals include (a) a better understanding of the vulnerability and the potential impact on the organization and people involved\, and (b) prescribing control measures and contingency plans to minimize the potential impacts. Reconnect 2023 will review classic methods and tools for risk assessment for various risks and hazards through real-world case studies. Several researchers affiliated with the newly established DHS Center of Excellence\, SENTRY\, will provide an overview of SENTRY’s research mission and offer examples of how they will use risk assessment methods in the center’s research. These include allocating resources for disaster management facing adaptive adversaries\, deploying “virtual sentries” to protect civilian spaces—so-called “soft targets”—around the country\, and several others. Registration fees\, lodging\, meals\, and travel: Accepted participants from US academic institutions: registration\, lodging in a single room\, and meals will be provided at no charge. Limited funds may be available to provide partial support for travel.   \nThe application deadline is March 15\, 2023\, or until all slots are filled. Click here to apply. \nOrganizers: Margaret (Midge) Cozzens\, DIMACS\, Rutgers University; Tamra Carpenter\, Rutgers University Jun Zhuang\, University at Buffalo Primary Speakers: Jun Zhuang\, University at Buffalo; Allison Coffey Reilly\, University of Maryland\, Seth Guikema\, University of Michigan; and Auroop Ganguly\, Northeastern University  
URL:https://coe.northeastern.edu/event/reconnect-workshop-2023-risk-assessment/
ORGANIZER;CN="ALERT":MAILTO:alert-info@coe.neu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230620
DTEND;VALUE=DATE:20230624
DTSTAMP:20260413T013318
CREATED:20230518T135356Z
LAST-MODIFIED:20230518T135448Z
UID:37050-1687219200-1687564799@coe.northeastern.edu
SUMMARY:2023 AEESP Conference Hosted at Northeastern University
DESCRIPTION:Northeastern University is proud to present the 2023 AEESP Research and Education Conference\, organized in collaboration with a large team from universities in New England including Massachusetts Institute of Technology\, Tufts University\, University of Connecticut\, University of Maine\, University of Massachusetts-Amherst\, University of New Hampshire\, and University of Rhode Island.  This conference will be welcoming participants from across the country and internationally\, as well as representatives from industry\, for 4 days of sharing\, learning\, and planning. The conference activities will commence on Tuesday\, June 20 and continue through Friday\, June 23\, under the theme “Responding Together to Global Challenges”. The conference and its attendees will reflect the many contributions environmental scientists and engineers are making to address concerning global\, historical\, and emerging challenges to living in a healthy\, safe\, and just society such as a changing climate\, emerging environmental quality and human health threats\, aging infrastructure and networks\, marginalization of communities\, and evolving education demands. \nThe Association of Environmental Engineering and Science Professors (AEESP) is a global organization of professors in academic programs who strive to provide education in the sciences and technologies of environmental protection. Founded in 1963\, the Association has grown to universities around the world\, facilitating improving education and research programs and encouraging graduate education. “This conference is an important meeting to exchange novel ideas in environmental research and education. We are proud to host academics\, students\, and friends from far and wide who will be sharing their recent advances in their fields of study\,” said Philip Larese-Casanova\, Associate Professor at Northeastern University\, who also serves as the Conference Chair. In addition\, the conference also serves the profession by providing information to government agencies and the public and provides direct benefits to its members. \nUnique elements of this year’s conference include a career fair and outreach activities on Conference Day 1\, a gala dinner on Day 2 at the New England Aquarium\, and a series of field trips in and around Boston on Friday June 23 to round out the week! The conference schedule includes the conventional and highly sought-after workshops and technical presentations. Students\, post-docs\, and faculty will find ample organized opportunities for networking\, career development\, and community outreach at the conference. \nRegistration is open through June 5 for the 2023 AEESP Conference!
URL:https://coe.northeastern.edu/event/2023-aeesp-conference-hosted-at-northeastern-university/
ORGANIZER;CN="Civil & Environmental Engineering":MAILTO:civilinfo@coe.neu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230623T100000
DTEND;TZID=America/New_York:20230623T110000
DTSTAMP:20260413T013318
CREATED:20230606T153237Z
LAST-MODIFIED:20230606T153237Z
UID:37194-1687514400-1687518000@coe.northeastern.edu
SUMMARY:Cooper Loughlin's PhD Dissertation Defense
DESCRIPTION:“Deep Generative Models for High Dimensional Spatial and Temporal Data Analysis” \nCommittee Members:\nProf. Vinay Ingle (Advisor)\nDr. Dimitris Manolakis\nProf. Purnima Ratilal-Makris \nAbstract:\nData analysis and exploitation in practical applications is challenging when observations are the result of many interacting natural and man-made phenomena. We address two important problems for which traditional methods of analysis are insufficient. One problem of practical interest is the identification of particular materials from remotely sensed hyperspectral imagery. This is traditionally accomplished by comparing image pixel spectra to those from a known material library. Such techniques are limited by spectral variability\, background interference\, and imperfect compensation of atmospheric components. Established methods address these limitations with statistical techniques. Simple probability models result in tractable methods; however\, analyses are limited by errors due\, in particular\, due to false alarms. \nAnalysis of complex time series is another challenging problem\, particularly when data are high dimensional. This arises in air quality monitoring\, where atmospheric concentration measurements of multiple pollutants are taken over time. Two analysis goals in this context are forecasting and anomaly detection. Both tasks are enabled by an accurate model for the temporal dynamics and interaction between pollutants. Air quality data are complex due to long term temporal dependencies\, non-linear dependence between pollutants\, and missing observations. Traditional multivariate time series analysis approaches\, such as the vector autoregression and linear dynamical system models\, fail to capture those characteristics necessary for a sufficient probabilistic model. \nWe use deep generative models to develop practical solutions that address these problems. This is made possible through the application of deep latent variable models. The modeling approach follows the philosophy that complex data can typically be explained by simpler underlying factors of variation. Variational autoencoders (VAEs) are deep latent variable models that emulate data generation by transforming simple\, low dimensional\, latent random vectors through a deep neural network. VAEs are trained to produce samples that resemble the training data\, thus capturing a manifold on which complex data are distributed. This philosophy is extended to time series data\, where we consider sequences of latent vectors. \nWe utilize VAEs develop a flexible generative model for hyperspectral imagery. Based on that model\, we develop a novel material identification framework which localizes target material spectra along the manifold. Through experiments on real data\, we show that the \ac{VAE} approach is better able to reject false alarms from materials with similar spectra when compared to established methods alone. We additionally develop a novel dynamical \ac{VAE} model for time series of air quality data. Using that model\, we develop practical methods for computing forecast distributions using Monte Carlo integration. We evaluate forecast distributions against real air quality data and demonstrate the ability to predict temporal dynamics and forecast uncertainty. The primary contribution of this work is to develop practical solutions to challenging data analysis problems through the use of deep generative models.
URL:https://coe.northeastern.edu/event/cooper-loughlins-phd-dissertation-defense/
END:VEVENT
END:VCALENDAR