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.
- August 2020: Our proposal titled “Collaborative Research: Framework: Improving the Understanding and Representation of Atmospheric Gravity Waves using High-Resolution Observations and Machine Learning” has been funded by the NSF Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program. This 5-year project involves close collaboration between our group at Rice and three groups at Courant Institute (Dr. Ed Gerber), NWRA (Dr. Joan Alexander), and Stanford University (Dr. Aditi Sheshadri). We have a number of PhD and postdoc positions through this project. See the details here.
- April 2020: Prof. Hassanzadeh has received the Young Investigator Award from the Office of Naval Research (ONR) for a proposal entitled “Using Artificial Intelligence and Inexact Computing to Improve Modeling of Multi-scale, Multi-physics, Chaotic Dynamical Systems with Applications to Weather Predictions”. Check out the Rice press release.
- March 2020: AGU’s EOS has a news story “Combining AI and Analog Forecasting to Predict Extreme Weather” covering our recent paper in JAMES on this topic.
- February 2020: Two papers on using deep learning for pattern recognition-based weather prediction, both led by PhD student Ashesh Chattopadhyay are out. Analog forecasting of extreme‐causing weather patterns using deep learning is published in AGU’s Journal of Advances in Modeling Earth System and Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spatio-temporal climate data is published in Scientific Reports. Check out the Rice press release.
- December 2019: Our recent paper on the projected increase in the size of blocking events under climate change is highlighted in NSF Research News and in a short video clip by pattern.com.
- November 2019: Our paper led by Ph.D. student Ebrahim Nabizadeh and in collaboration with Prof. Da Yang (UC Davis/LBNL) and Prof. Libby Barnes (CSU) is published in Geophysical Research Letters. In this paper, we show that jet streams’ blocking events, which cause weather extremes such as heat waves and cold spells, are becoming larger under climate change. We also use the Buckingham-pi theorem to derive a scaling law for the size of the blocking events in a hierarchy of climate models. The findings have implications for the size and magnitude of future extreme weather events. See the Rice press release, NSF Research News, phys.org & ScienceDaily