{"id":468,"date":"2020-02-07T22:01:17","date_gmt":"2020-02-07T22:01:17","guid":{"rendered":"http:\/\/sites.rutgers.edu\/daniel-balouek-thomert\/?p=468"},"modified":"2020-02-25T19:30:17","modified_gmt":"2020-02-25T19:30:17","slug":"outstanding-paper-award-in-artificial-intelligence-for-social-impact-aaai-20","status":"publish","type":"post","link":"https:\/\/sites.rutgers.edu\/daniel-balouek-thomert\/outstanding-paper-award-in-artificial-intelligence-for-social-impact-aaai-20\/","title":{"rendered":"Outstanding Paper Award in Artificial Intelligence for Social Impact (AAAI-20)"},"content":{"rendered":"

Our paper entitled \u201cA Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning\u201d was selected in the Artificial Intelligence for Social Impact track at AAAI-20. This work addresses the challenges of characterizing earthquake and issuing broadcast alerts in a matter of seconds by relying on an effective use of scientific instruments and large-scale detection.<\/p>\n

\"\"<\/a>
Overview of the Distributed Multi-Sensor Earthquake Early Warning (DMSEEW) system. The architecture relies on sensors, edge and fog resources to detect earthquakes and broadcast alerts.<\/figcaption><\/figure>\n

As categorizing medium earthquakes is not done well by GPS, and categorizing large earthquakes is not done well by seismometers, the paper proposes a predictive model that combines these measurements using machine learning techniques to improve the accuracy of Earthquake Early Warning systems. Specifically, the authors proposed a Distributed Multi-Sensor system that combines data from both types of sensors (GPS stations and seismometers) to detect medium and large earthquakes at scale.<\/p>\n

Evaluations on real-world earthquake datasets have shown to be more accurate than their baseline while being robust to infrastructure failures.<\/p>\n

The project is based on an interdisciplinary research collaboration between RDI2, the INRIA Large-Scale Collaborative Data Mining (LACODAM) team in France, and Univ. of Oregon.<\/p>\n

Preprint: https:\/\/hal.archives-ouvertes.fr\/hal-02373429\/document<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Our paper entitled \u201cA Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning\u201d was selected in the Artificial Intelligence for Social Impact track at AAAI-20. This work addresses the challenges … Read More<\/a><\/p>\n","protected":false},"author":682,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[7],"tags":[],"class_list":["post-468","post","type-post","status-publish","format-standard","hentry","category-publications"],"acf":[],"yoast_head":"\nOutstanding Paper Award in Artificial Intelligence for Social Impact (AAAI-20) - Daniel Balouek-Thomert<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sites.rutgers.edu\/daniel-balouek-thomert\/outstanding-paper-award-in-artificial-intelligence-for-social-impact-aaai-20\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Outstanding Paper Award in Artificial Intelligence for Social Impact (AAAI-20) - Daniel Balouek-Thomert\" \/>\n<meta property=\"og:description\" content=\"Our paper entitled \u201cA Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning\u201d was selected in the Artificial Intelligence for Social Impact track at AAAI-20. 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