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We study fluid dynamics and heat transfer in complex natural phenomena and engineering systems using numerical, mathematical, and statistical models, 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, reduced-order modeling, extreme weather events, atmospheric turbulence, climate modeling, flow control in energy systems, and numerical and mathematical modeling of thermo-fluid processes. Our research is currently supported by NASA, NSF, National Academy of Sciences, Microsoft AI, Mitsubishi Electric Research Lab, Rice University Creative Ventures, and Rice Houston Engagement and Recovery Effort.

See available positions for a new postdoc opening in our group (updated June 2019).

Recent News:

  • June 2019: Second year PhD students Ashesh and Charles presented our recent work on using a hierarchy of deep learning techniques for data-driven prediction of a chaotic dynamical system at the ICML workshop “Climate change: How can AI help?“.
  • May 2019: Our paper led by Ph.D. student Packard Chan and Prof. Zhiming Kuang (Harvard University) is published in Geophysical Research Letters. In this paper, we have evaluated/compared indices for atmospheric blocking events in terms of how well they capture heat wave-causing weather patterns.
  • April 2019: Prof. Igor Mezic from UCSB visited our group and gave a talk titled “Koopman Operator Theory for Dynamical Systems, Control and Data Analytics” at the MECH seminar series.
  • March 2019: Our paper in collaboration with Prof. Zhiming Kuang (Harvard University) is published in the Journal of the Atmospheric Sciences. In this paper, we used a novel framework based on accurate linear response functions to quantify the dynamics of the annular mode, the leading mode of variability of the extratropical circulation.
  • February 2019: Our paper led by postdoc Dr. Amin Khodkar and with collaborators from MERL is published in Physical Review Fluids and selected for Editors’ Suggestion. In this paper, we applied the Green’s function method to compute an accurate reduced-order model and an eddy parameterization scheme for turbulent Rayleigh-Benard convection.
  • January 2019: Dr. Hassan Arbabi from MIT visited our group and gave a talk titled “Data-driven Analysis and Control of Dynamical Systems: a Koopman-Operator Perspective” at the MECH seminar series.