My main research interest is in fundamental* *** fluid dynamics**. I use computational fluid dynamics (e.g. RANS) and

**(e.g. machine learning) techniques to better understand and model fluid flows in engineering systems. I am currently working under Dr. Heng Xiao at Virginia Tech getting my Ph.D. on**

*data science**.*

**Data Assimilation in Turbulence Modeling**My other research interest is in ** renewable energy**. Prior to coming to Virginia Tech, I worked on marine renewable energy at Sandia National Laboratories for 3.5 years. There I was part of the team that developed and released the widely-used open-source code WEC-Sim. I also worked on a project for predicting extreme loads on wave energy converters using a variety of statistical tools.

## Publications

**[1]** Wu, Jinlong, et al.* **“Physics-Informed Covariance Kernel for Model-Form Uncertainty Quantification with Application to Turbulent Flows**“*. Computers and Fluids 193 (2019) 104292

**[2]** Coe, Ryan G., et al. * “Full Long-Term Design Response Analysis of a Wave Energy Converter”*.

**[3]** Michelén Ströfer, Carlos, et al. ** “Data-Driven, Physics-Based Feature Extraction from Fluid Flow Fields using Convolutional Neural Networks“.** Communications in Computational Physics, Vol. 25, No. 3, pp. 625-650.

**[4] **Ratanak So et al. ** “Statistical Analysis of a 1:7 Scale Field Test Wave Energy Converter Using WEC-Sim”**. IEEE Transactions on Sustainable Energy 8.3 (Jan. 2017), pp. 1118–1126.