WHIRLab Research group develops numerical models to connect big data and decision-making
Roger Wang joined the Rutgers University School of Engineering civil and environmental engineering (CEE) faculty as an assistant professor in 2019. Since then, his Wang Hydro-Environment Informatics Research Lab (WHIRLab) research group has focused on developing numerical models to connect big data and decision-making in civil and environmental engineering systems, leveraging the emerging digital infrastructure.
Working with students in his WHIRLab, he creates innovative methods to address this challenge. The group is mining information from social media, smartphone videos, and satellite imaging to reveal and understand trends in sea-level rise and coastal flooding. It is also developing numerical models to assess the impact of cyber-security such as sensor reliability on flooding forecasts.
His lab recently received funding from the NJ DOT to work with NJDOT/NJDEP to develop tools to help transportation planners enhance climate resilience. The WHIRLab has also received funding to assess the impact of cyber-security issues on hydrological forecasting; evaluate and address 5G’s impact on weather forecasting; use satellite imaging to analyze coastal waves and flooding; and optimize the planning and operation of offshore wind farms. While it might be considered somewhat unusual for a CEE professor, Wang has also recently received funds from Colgate-Palmolive to apply his fluid dynamics knowledge to study the reason for and seek a solution to tooth pain.
Four Questions for Prof. Roger Wang
What attracted you to civil and environmental engineering and to models that connect big data and decision-making?
As a kid, I dreamed of studying UFOs and aliens. So I was trained to be an aerospace engineer in college, which was the closest major to my dream that also balanced my engineering interest. Realizing that no serious job is about UFOs, I focused my undergraduate study on fluid mechanics, which has rich and fascinating phenomena to explore. My final year project was about sandstorms, which convinced me that fluid mechanics had a great potential to address environmental problems. Thus, supported by scholarships, I changed my field to environmental fluid mechanics in my master’s degree and Ph.D. studies.
As big data and AI emerged, I witnessed their power and their great potential to revolutionize the field of civil and environmental engineering, where decision-making is a central task. However, the connection between big data and decision-making was difficult to establish because big data, as featured by unconventional data sources such as social media, is unstructured and cannot be used directly in engineering. Leveraging my intensive aerospace engineering and fluid mechanics training in numerical modeling, I found that numerical modeling coupled with AI techniques could fill the gap. My lab’s series of preliminary studies has validated the feasibility of this approach.
Who would most benefit from your research?
Coastal communities and inland residents living with flooding risks – especially from sea-level rise and climate change – will gain fresh insights into mitigating flooding issues. My research will also benefit city and transportation planners and others wishing to design and operate more resilient infrastructure systems. In addition, the civil and environmental industry is eager to transition into the big data and AI age. My research leveraging AI and big data demonstrates the rising power of the new methods to advance this frontier.
What most excites and inspires you about your research?
I’m most fulfilled when I find new data sources that open our eyes to the discovery of new phenomena – or the observation of known phenomena. I’m also driven by a fundamental question: how can we predict the future better? I am excited that my work in data, modeling, and decision-making is contributing to this issue.
How are Rutgers students contributing to your current research projects?
Rutgers undergraduate and graduate students have helped WHIRLab collect new data from satellites, radar, sensors, field studies, social media, and other sources. They help develop AI and numerical models to discover exciting new phenomena – and explain their observations. Through group meetings and conferences, we brainstorm new ideas and design new studies that can overcome obstacles and push our knowledge forward and break down technology boundaries.