Qin Jim Chen
· ProfessorNortheastern University · Environmental Engineering
Active 1995–2024
About
Qin Jim Chen is a professor at Northeastern University College of Engineering, with joint appointments in the Department of Civil and Environmental Engineering and the Department of Marine and Environmental Sciences. His research focuses on adaptive coastal intelligence and nature-based solutions for climate resilience, including integrated numerical modeling and field observations of hurricane impacts on natural and hybrid infrastructure. He has contributed to understanding morphodynamic simulations of coastal storms, the co-evolution of nearshore-beach-dune systems, and the development of models to improve coastal decision-making for climate change adaptation. Professor Chen has been recognized with numerous honors, including the 2022 Søren Buus Outstanding Research Award, the James M. Todd Technological Accomplishment Medal, and the National Science Foundation CAREER Award in 2006. His work involves collaboration with industry partners such as DHI Water and Environment Inc., and he is actively involved in research centers related to coastal sustainability and environmental engineering. He has also been acknowledged as one of the top scientists worldwide by Stanford University for his impactful research contributions.
Research topics
- Machine Learning
- Computer Science
- Oceanography
- Physics
- Geology
- Meteorology
- Mathematics
- Engineering
- Chemistry
- Organic chemistry
- Chromatography
- Statistics
- Algorithm
- Acoustics
- Mechanics
- Environmental chemistry
- Mathematical analysis
- Structural engineering
- Marine engineering
Selected publications
Reconstruction of nearshore wave fields based on physics-informed neural networks
Coastal Engineering · 2022 · 56 citations
- Computer Science
- Machine Learning
- Computer Science
Integration of data-driven and physics-based modeling of wind waves in a shallow estuary
Ocean Modelling · 2022 · 32 citations
- Machine Learning
- Computer Science
- Meteorology
Numerical models solving the wave action balance equation have been widely used to simulate wind waves. In-situ measurements, albeit sparse, are crucial to the calibration and validation of numerical wave models. In this study, a novel hybrid approach was developed by integrating a physics-based Simulating WAves Nearshore (SWAN) model with machine learning algorithms to predict wind waves in a shallow estuary. Two machine learning methods, bagged regression tree (BRT) and artificial neural network (ANN), were employed. It was found that the hybrid approach (BRT–SWAN) could be an efficient tool for modelers to identify sources of error and calibrate parameters in physics-based models. In this study, the wind direction and bottom friction coefficient were determined as the main factors causing errors in SWAN-simulated significant wave height and peak wave period, respectively. Furthermore, it turned out that BRT–SWAN-ANN (ANN trained with BRT–SWAN results) could achieve a similar level of accuracy to OBS-ANN (ANN trained with field observations of wind waves). Thus, the hybrid approach can be applied to estimate wave parameters, removing the limitation of using scarce observations in developing a predictive ANN model.
Water Research · 2021 · 53 citations
- Chemistry
- Chromatography
- Environmental chemistry
Numerical modeling of the interaction between submerged floating tunnel and surface waves
Ocean Engineering · 2020 · 50 citations
- Mechanics
- Marine engineering
- Engineering
Field Observations of Wind Waves in Upper Delaware Bay with Living Shorelines
Estuaries and Coasts · 2020 · 41 citations
- Oceanography
- Geology
Recent grants
NSF · $1.2M · 2015–2019
NSF · $866k · 2018–2021
NIH · $428k · 2014
CAREER: Simulations of Nonlinear Water Waves and Air-to-Sea Momentum Fluxes in the Coastal Ocean
NSF · $400k · 2006–2012
NSF · $154k · 2011–2015
Frequent coauthors
- 34 shared
Ling Zhu
Wuhan University of Technology
- 28 shared
Hongqing Wang
U.S. Geological Survey, Wetland and Aquatic Research Center
- 25 shared
Navid H. Jafari
Louisiana State University
- 23 shared
Kelin Hu
- 20 shared
Per A. Madsen
Technical University of Denmark
- 20 shared
David R. Basco
Old Dominion University
- 16 shared
Patrick Lynett
University of Southern California
- 16 shared
Chih‐Chieh Young
Labs
Qin Jim Chen LabPI
Awards & honors
- 2022 Søren Buus Outstanding Research Award
- James M. Todd Technological Accomplishment Medal
- LES-BTR Best Paper Award
- LSU Rainmakers Award for Innovative Research
- National Science Foundation CAREER Award (2006)
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