About me
About me
I am a Code, Standards, and Research Specialist at the Canada Masonry Design Centre (CMDC), where I collaborate with researchers and engineers on the development and revision of masonry design codes and standards (e.g., CSA S304), and help translate research findings into practical guidance for industry.
Prior to joining CMDC, I was a Postdoctoral Fellow in the Structural Modeling and Reliability Analysis (SM&RA) Group at the University of Alberta, where I also completed my Ph.D. in Structural Engineering under the supervision of Dr. Yong Li.
Prior to joining CMDC, I was a Postdoctoral Fellow in the Structural Modeling and Reliability Analysis (SM&RA) Group at the University of Alberta, where I also completed my Ph.D. in Structural Engineering under the supervision of Dr. Yong Li.
I have a broad research interest spanning masonry & concrete structures, computational modeling, data-driven modeling, and uncertainty quantification. My work emphasizes the rigorous application of mechanics-based theories and mathematical methods to better understand and predict the behavior of structural systems. I believe a strong foundation in engineering mechanics is essential for developing reliable solutions to complex structural problems. I also actively explore emerging data-driven surrogate modeling techniques (e.g., physics-informed neural networks, Gaussian process regression, polynomial chaos expansion), as well as probabilistic and statistical methods (e.g., Bayesian inference, variance-reduction techniques such as control variates), to enhance engineering analysis and structural assessment. I also investigate reinforcement learning for intelligent construction.
I have a broad research interest spanning masonry & concrete structures, computational modeling, data-driven modeling, and uncertainty quantification. My work emphasizes the rigorous application of mechanics-based theories and mathematical methods to better understand and predict the behavior of structural systems. I believe a strong foundation in engineering mechanics is essential for developing reliable solutions to complex structural problems. I also actively explore emerging data-driven surrogate modeling techniques (e.g., physics-informed neural networks, Gaussian process regression, polynomial chaos expansion), as well as probabilistic and statistical methods (e.g., Bayesian inference, variance-reduction techniques such as control variates), to enhance engineering analysis and structural assessment. I also investigate reinforcement learning for intelligent construction.