Welcome to my page!

I am a postdoctoral fellow at the Department of Mechanical and Production Engineering at Aarhus University, Denmark,  specializing in the intersection of machine learning and wind-energy science. My passion lies in developing cutting-edge models that offer both speed and precision, effectively bridging the gap between high-fidelity simulations and low-order models. By leveraging machine-learning techniques, I attempt to create predictive solutions that optimize the performance of wind farms, making them more efficient and reliable.

During my Ph.D. studies, I had the opportunity to immerse myself in a multidisciplinary research environment, exploring diverse subjects related to water research and the enhancement of thermo-fluidic phenomena using active methods. Specifically, I delved into corona-discharge generated electrohydrodynamics (EHD) flow, employing both experimental and numerical investigations. To further enhance my expertise, I spent six months at the Applied Electrostatics Research Centre (AERC) at Western University, Canada.

My research interests lie in the areas of Fluid Mechanics, Wind Energy, Machine Learning, EHD, and Transport Phenomena.

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