University-wide, Talks & Lectures, Open to the Public
9 Nov 2020
Zoom Webcast

Numerical Modeling to Connect Big Data and Decision-Making for Coastal Resilience

Department of Civil, Environmental, and Ocean Engineering

Department of Civil, Environmental, and Ocean Engineering
Roger Wang, Ph.D.

Assistant Professor at the Department of Civil and Environmental Engineering, Rutgers University

To meet one-on-one with the speaker via ZOOM, contact [email protected]

ABSTRACT

In the big data era, it is still difficult to rely on the model-based design to mitigate natural hazards, optimize infrastructure planning, and address urban environmental problems. On the one hand, we lack tools to mine and fuse the growing big data to improve numerical models. On the other hand, we have difficulties to use high-fidelity models to support decision-making. As data science and data-driven algorithms emerge, there is an opportunity to apply and develop a suite of new methods to connect data, especially the unconventional big data, and high-fidelity models to address the model-based decision-making issues of urban and coastal infrastructure design. My talk will focus on my recent studies of monitoring urban floods with AI-enabled social media data and supporting decision making with data-driven analysis for coastal infrastructure.

BIOGRAPHY

Roger Wang, Ph.D., is an Assistant Professor at the Department of Civil and Environmental Engineering, Rutgers University. Prior to joining Rutgers University, he worked as a Lecturer at the University of Dundee, UK. He received his Ph.D. degree in Environmental Fluid Mechanics from Massachusetts Institute of Technology in 2014, his M.S. degree from Nanyang Technological University, and his B.S. degree from Beihang University. He has conducted postdoctoral research at MIT and the University of California Berkeley from 2014 to 2017. Dr. Wang’s research group is aimed at developing numerical models to connect big data and decision-making in civil and environmental engineering systems.

LIVE WEBCAST

Passcode: 243336

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