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We study turbulent flows in complex natural phenomena and engineering systems using numerical, mathematical, statistical, and machine learning methods, guided by observational and experimental data. Our work is often motivated by theoretical and applied problems related to environment and energy. Examples of problems of interest are environmental and geophysical flows, extreme weather events, reduced-order modeling, atmospheric turbulence, and climate modeling. Our research is supported by NASA, NSF, ONR, National Academy of Sciences, Microsoft AI, Mitsubishi Electric Research Lab, Rice University Creative Ventures, and Rice Houston Engagement and Recovery Effort.

Updated September 2020: A number of PhD & Postdoc positions supported by NSF on applications of machine learning to modeling of gravity waves and atmospheric flows are available. See the details here. 

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