About

Ruo-Qian (Roger) Wang is an Associate Professor at Rutgers University, affiliated with both the Department of Civil and Environmental Engineering and the Department of Electrical and Computer Engineering. He earned his PhD from the Massachusetts Institute of Technology in 2014, following an MSc from the Singapore Stanford Partnership in 2008 and a BEng from Beihang University in 2007. Professor Wang leads the WHIRLab (Wang Research Lab) at Rutgers, where he supervises a diverse group of graduate and undergraduate students engaged in research projects related to environmental engineering and computational modeling. His research interests include environmental impacts of hydrokinetic power, large eddy simulation of boundary layer dynamics for ice-water interfaces, infrastructure aging under climate change, and modeling of greenhouse gas emissions from ice-covered lakes. Professor Wang's interdisciplinary expertise bridges civil, environmental, and electrical engineering, contributing to advancements in understanding and mitigating environmental challenges through innovative engineering solutions.

Research topics

• Computer Science
• Engineering
• Electrical engineering
• Psychology
• Psychotherapist
• Psychiatry
• Clinical psychology
• Electronic engineering
• Social psychology
• Mechanical engineering
• Waste management
• Chemistry
• World Wide Web
• Data science
• Internet privacy
• Developmental psychology
• Environmental science
• Computer hardware

Selected publications

• Projected Degradation of Weather Satellite Observations and Forecasting under 5G Expansion

  2026-03-02

  article

  Passive microwave observations near 23.8 GHz are central to numerical weather prediction but lie adjacent to frequencies increasingly used for fifth-generation (5G) telecommunications. Here we present the first end-to-end quantification of how terrestrial 5G interference affects weather forecasts, particularly for extreme weather events, using an operational forecasting system. By assimilating synthetically contaminated microwave radiances into a data assimilation framework, we show that aggregate out-of-band emissions introduce structured biases that propagate dynamically through the forecasting system. In simulations of Hurricane Ida’s extratropical transition, these biases trigger the rejection of moisture-sensitive observations and distort atmospheric states, producing precipitation errors of up to 15% and near-surface temperature deviations exceeding 2 °C, even far from interference sources. Our results demonstrate that anthropogenic spectral interference can systematically degrade forecasts of extreme weather, revealing a previously unquantified vulnerability in global early warning systems.

  PublisherDOI

• An integrative framework for AI-supported coastal hydrodynamics monitoring and analysis

  Scientific Reports · 2025-04-11 · 2 citations

  articleOpen access1st authorCorresponding

  The intensification of climate change poses significant threats to coastal regions worldwide, manifesting in increased storm frequency, sea level rise, and consequent flooding risks. This study addresses the urgent need for innovative monitoring strategies by introducing an advanced coastal hazard monitoring system specifically designed for areas with underdeveloped monitoring infrastructure. Employing a blend of traditional methods and cutting-edge technologies, including the Segment Anything Model (SAM) for high-resolution image segmentation and Dynamic Mode Decomposition (DMD) for pattern recognition, we provide a comprehensive assessment of coastal water dynamics. The study highlights the application of SAM in identifying water-land boundary despite challenges such as image distortion and variable lighting conditions. Additionally, the innovative use of monoplotting with DEM provides a robust framework for accurate mapping in complex coastal terrains. This research advances our understanding of coastal dynamics under the impact of climatic changes and sets a new benchmark for environmental monitoring, offering substantial improvements over traditional methodologies by integrating technological advancements with practical fieldwork. The findings demonstrate significant implications for disaster preparedness and the sustainable management of coastal regions, emphasizing the necessity of adopting advanced technologies to enhance the resilience of vulnerable coastal communities against the escalating threats posed by climate change.

  PublisherOA PDFDOI

• Towards Democratized Flood Risk Management: An Advanced Ai Assistant Enabled by Gpt-4 for Enhanced Interpretability and Public Engagement

  SSRN Electronic Journal · 2025-01-01 · 9 citations

  preprintOpen accessSenior author
  PublisherDOI

• Toward Adaptive and Coordinated Transportation Systems: A Multi-Personality Multi-Agent Meta-Reinforcement Learning Framework

  IEEE Transactions on Intelligent Transportation Systems · 2025-04-22 · 31 citations

  article

  Advancements in Intelligent Transportation Systems (ITS) have led to innovative solutions for planning optimization, efficiency enhancement, and resource allocation in transportation networks, which are demonstrated in applications such as smart parking lot management and electric vehicle (EV) charging station allocation, where improved decision-making and system-wide optimization have been achieved. However, as these systems evolve, the demand for better adaptability and coordination continues to grow to maximize their overall effectiveness and efficiency. To achieve this, we propose the Multi-Personality Multi-Agent Meta-Reinforcement Learning (MPMA-MRL) framework. This approach incorporates multiple meta-trained, meta-tested explainable personality policies, which are deployed to each agent. A personality selector is trained and deployed on each agent to optimize the overall performance. MPMA-MRL is superior than traditional methods in terms of the adaptability and coordination in ITS by leveraging improved information from the environment, more practical coordination among agents, faster adaptation speed to intermediate tasks, and more appropriate allocation and planning. The proposed framework is evaluated in the applications of parking lot optimization and EV charging station allocation. Its broader impact on multi-agent smart systems is analyzed to demonstrate its generalizability. The results demonstrate that in parking lot optimization, MPMA-MRL significantly reduces the time required to direct all vehicles to available parking spots. In EV charging station allocation, MPMA-MRL effectively minimizes waiting times at charging stations. Moreover, in both applications, MPMA-MRL exhibits enhanced adaptability to previously unseen scenarios, improving its applicability.

  PublisherDOI

• Environmental Co-Design: Fish-Blade Collision Model for Hydrokinetic Turbines

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Environmental Co-Design: Fish-Blade Collision Model for Hydrokinetic Turbines

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen accessSenior author
  PublisherDOI

• Environmental Co-design: Fish-Blade Collision Model for Hydrokinetic Turbines

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Advancing Earth science applications through a semi-automatic monoplotting framework for efficient 3D geo-referencing of monocular oblique visual Data

  Computers & Geosciences · 2025-03-21

  articleOpen accessSenior authorCorresponding

  The widespread availability of high-quality images from smartphones, drones, and digital cameras presents an unprecedented opportunity for global geospatial data collection. However, these images are often captured at oblique angles, making geo-referencing challenging and limiting their usability. Monoplotting, a technique that requires only a single image and a Digital Elevation Model (DEM), addresses these challenges by establishing pixel-level correspondence between imagery and real-world coordinates. However, traditional monoplotting methods are labor-intensive, requiring manual identification of control points in both the image and DEM, as well as manual tuning of camera parameters, which restricts scalability for large-scale databases and near-real-time applications. This paper proposes a novel semi-automatic monoplotting framework that minimizes human intervention. The framework integrates key point detection, geo-referenced 3D point retrieval, regularized gradient-based optimization, pose estimation, back-projection, and pixel mapping to enable efficient geo-referencing. To the best of the authors’ knowledge, this is the first study to incorporate key point detection into monoplotting, and apply regularized gradient-based optimization for camera position and parameter determination, even with unequal numbers of key points from the image and DEM. Numerical experiments with a historical image and a corresponding real-world DEM demonstrate the framework’s effectiveness. The robustness of the method is further evaluated on distorted images, where the distortion strength coefficient is treated as an unknown and estimated through projection optimization. The results confirm the framework’s ability to establish accurate correspondence between the image pixel domain and real-world 3D coordinates. Additionally, integrating machine learning models, such as semantic segmentation, highlights the framework’s advantages in Earth science applications, including snow and glacier characterization. • A new semi-automatic monoplotting framework boosts spatial data analysis. • Key point matching enhances pixel-world mapping. • Pose estimate is improved via gradient-based optimization. • ML integration advances Earth science applications • ML-monoplotting is a new method toward spatial intelligence.

  PublisherDOI

• Poster: Turbulence Meets Ice: Decoding the Equilibrium of Scalloped Geometries

  2025-11-23

  articleOpen accessSenior author
  PublisherDOI

• Towards democratized flood risk management: An advanced AI assistant enabled by GPT-4 for enhanced interpretability and public engagement

  Environmental Modelling & Software · 2025-12-12

  articleOpen accessSenior authorCorresponding

  Traditional flood risk communication fails to bridge the gap between complex technical data and the needs of the public, hindering effective response. This research addresses this gap by developing and validating a novel AI-powered assistant that uses GPT-4 to democratize flood risk information. Our core methodology includes a Retrieval-Augmented Generation (RAG) framework that synthesizes real-time flood warnings, geospatial data, and social vulnerability indices into clear, conversational responses. To validate its effectiveness, we conducted a mixed-methods evaluation, including a comparison across different GPT models. Key quantitative findings reveal that the assistant achieved high performance scores in general flood knowledge (5/5) and handling flash flood alerts (4.3/5). Response times averaged a rapid 12 s for non-function-calling queries, though more complex data retrieval tasks averaged 36 s, highlighting areas for optimization. Our comparison identified GPT-4o as the optimal model for balancing accuracy with response time. The broader implications of this work demonstrate that large language models can serve as powerful tools to translate complex environmental data for non-experts, paving the way for more equitable, engaging, and effective public participation in disaster risk management. • An AI Assistant was developed using GPT-4 to enhance flood risk interpretability and engagement. • Real-time flood warnings, maps, and social vulnerability data were integrated into a framework. • Complex flood information was translated into actionable advice for public and decision-makers. • The Assistant's performance was evaluated on relevance, error resilience, and context accuracy. • Flood risk management was advanced by democratizing access to social and environmental data.

  PublisherDOI

Frequent coauthors

• E. Eric Adams

  Massachusetts Institute of Technology

  37 shared

• Henry G. Artman

  National Institute of Diabetes and Digestive and Kidney Diseases

  36 shared

• R. John Looney

  University of Rochester

  36 shared

• Mary Gail Mercurio

  36 shared

• Adrian Wing‐Keung Law

  33 shared

• Oliver B. Fringer

  Stanford University

  28 shared

• Anthony Rosario

  27 shared

• Herbert Levine

  Northeastern University

  24 shared

Labs

• WHIRLabPI

  Ruo-Qian Wang's research lab at Rutgers University

Education

• PhD, Civil and Environmental Engineering

  Massachusetts Institute of Technology

  2014

• M.Sc., Civil and Environmental Engineering

  Nanyang Technological University

  2008

• BEng, Aeronautics Science and Engineering

  Beihang University

  2007