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X-WR-CALDESC:Events for Northeastern University College of Engineering
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DTSTART;TZID=America/New_York:20230618T080000
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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
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DTSTART;VALUE=DATE:20230620
DTEND;VALUE=DATE:20230624
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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
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DTSTART;TZID=America/New_York:20230620T080000
DTEND;TZID=America/New_York:20230620T170000
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UID:37261-1687248000-1687280400@coe.northeastern.edu
SUMMARY:Alfred P. Navato's PhD Dissertation Defense
DESCRIPTION:Title:\nEnabling Anomaly Detection in Complex Chemical Mixtures Through Multimodal Data Fusion \nDate:\n6/26/2023 \nTime:\n10:00:00 AM \nLocation:\nSH 210\, \nCommittee Members:\nProf. Mueller (Advisor)\nProf. Erdogmus\nProf. Ioannidis\nProf. Onnis-Hayden \nAbstract:\nRecently innovations in machine learning and data processing are increasingly tied to ensuring useability and interpretability when these methods are applied within end-user domains.  One societally important example of such a domain is management and operations of water infrastructure in cities\, where data collection is currently costly and limited\, enabling analytics have the potential to generate real impact for urban communities\, and correctness of results is critical to protect human and environmental health.  This dissertation holistically considers issues of generalizability\, transferability\, and applicability of a range of data fusion and machine learning approaches across end-user domains within the context of solution building for improved real-time management of wastewater infrastructure.  The first chapter provides an overview of the challenges associated with anomaly detection within the wastewater field and reviews the performance of various anomaly detection techniques implemented in other disciplines.  The second chapter discusses the barriers and opportunities in cross-disciplinary pollination of data fusion techniques.  The third chapter presents development of an unsupervised approach facilitating quantitative characterization of the complex background which is wastewater\, necessary to be able to implement any automated operational interventions.  The fourth chapter develops an approach for cost-minimization/information-maximization design of a sensor to facilitate specifically detection of chemical anomalies (defined as inflow events that might compromise wastewater treatment facilities) by using machine learning and feature selection techniques to minimize the number of input signals needed to achieve reasonable accuracies.  Together the third and fourth chapters provide a clear\, explainable\, actionable pathway forward in envisioning next generation wastewater infrastructure\, demonstrating novel and impactful use of data fusion and machine learning techniques in a real-world context.
URL:https://coe.northeastern.edu/event/alfred-p-navatos-phd-dissertation-defense/
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DTSTART;TZID=America/New_York:20230620T130000
DTEND;TZID=America/New_York:20230620T140000
DTSTAMP:20260507T090624
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UID:37070-1687266000-1687269600@coe.northeastern.edu
SUMMARY:Chang Liu's PhD Dissertation Defense
DESCRIPTION:“Unleashing the Potential of Transfer Learning for Visual Applications” \nCommittee Members:\nProf. Raymond Fu (Advisor)\nProf. Sarah Ostadabbas\nProf. Zhiqiang Tao \nAbstract:\nThe recent flourish of deep learning in various tasks is largely accredited to the rich and accessible labeled data. Nonetheless\, massive supervision remains a luxury for many real-world applications. Further\, the domain shift problem has also seriously impeded large-scale deployments of deep-learning models. As a remedy\, Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way\, the dependence on a large number of target domain data can be reduced for constructing target learners. \nIn this dissertation research\, I investigate two major problems in transfer learning\, domain adaptation (DG) and domain adaptation (DA)\, on various visual applications. (1) The challenge of DG lies in an over-simplified assumption\, that is\, the source and target data are independent and identically distributed (i.i.d.) while ignoring out-of-distribution (OOD) scenarios commonly encountered in practice. This issue is common in visual applications such as object recognition\, hyperparameter optimization\, and face recognition. We propose algorithms that are specifically designed for each task\, such as metric learning\, adversarial regularization\, feature disentanglement\, and meta-learning. (2) DA can be considered a special case of DG with unlabeled target data available. The major challenge is how to align the labeled source and unlabeled target data. We delve into the applications of image recognition and video recognition and propose algorithms to ensure domain-wise discriminativeness and class-wise closeness across domains. Experiments show that the proposed algorithms outperform the state-of-the-art methods on the commonly-used benchmark datasets.
URL:https://coe.northeastern.edu/event/chang-lius-phd-dissertation-defense/
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