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Mechanical and Civil Engineering Seminar

Friday, January 26, 2018
9:00am to 10:00am
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Gates-Thomas 135
Data-driven Infrastructure Informatics in Smart and Resilient Cities
Thomas Matarazzo, Postdoctoral Fellow, Senseable City Lab, MIT ,

The rapid revolution in sensing, computation, telecommunications, and mobile devices over the past two decades has provided exciting opportunities for integrated sensing systems in the urban environment and has created a path towards achieving resilient engineering systems. Embedded and interactive sensory networks can deliver comprehensive feedback about the true condition of structures, and produce information that can assist the operation, maintenance, rehabilitation, and functionality of structures and infrastructure. Simultaneously, we live in a time where ubiquitous smartphones contain dozens of different sensors and empower the public to regularly "crowdsense" infrastructures. The objective of this presentation is to provide a vision for how research in health monitoring techniques, sensor networks, machine learning, and the internet of things, supplemented by laboratory and field implementations, progresses to resilient and cooperative engineering systems in a smart and connected urban environment.

Dr. Matarazzo's research interests include system identification, mobile sensing, machine learning, and automated infrastructure management. In 2010, he graduated Summa Cum Laude with a B.S. in Civil Engineering from Manhattan College. He obtained M.S. (2012) and Ph.D. (2015) degrees in Structural Engineering from Lehigh University. He acquired an NSF EAPSI research fellowship, then a Japan Society for the Promotion of Sciences postdoctoral fellowship, which funded his research on post-earthquake sensing systems at Kyoto University in Japan. He has over twenty publications including six first-author journal articles. Currently, he is a researcher at the MIT Senseable City Lab where he is leading an interdisciplinary research team to better understand how crowdsourced smartphone data from vehicle trips can provide bridge information.