معرفی
Serveh Kamrava is an Assistant Professor at the Colorado School of Mines, Department of Chemical and Petroleum Engineering. Her research bridges Chemical, Environmental, and Petroleum Engineering with advanced computational methods, focusing on energy storage, fluid dynamics, and machine learning applications in complex systems.
Research Interests: Her work centers on developing physics-guided machine learning models to predict flow and transport in heterogeneous and porous materials. She aims to improve energy storage systems by enhancing efficiency and longevity through data-driven and multiscale modeling techniques. Key areas include deep learning for material reconstruction, fluid flow simulation, and dispersion prediction in geophysical and environmental contexts.
Publication Trends: Her recent publications (2019–2021) demonstrate a strong focus on integrating deep learning with physical laws in porous media, particularly in geological and membrane systems. The work spans journals in geophysics, computational materials, environmental science, and chemical engineering, showing interdisciplinary impact.
- Scientific Awards:
- 2022 NSF Award
- 2022 ACS-PRF DNI Award
Advising and Grants: Dr. Kamrava is actively recruiting PhD students, indicating ongoing research funding and mentorship activities. She likely holds active grants, including the NSF award, supporting her work in machine learning applications for energy and environmental systems. She advises prospective students with backgrounds in machine learning and computational fluid dynamics.
Labs and Teams: She leads a research group focused on the interface of engineering and data science, utilizing state-of-the-art machine learning to solve multiscale problems in energy and environmental engineering. The group emphasizes Python-based computational modeling and interdisciplinary collaboration.
