Research

Projects
  1. (active) PI: Machine learning to improve cycling and forecasts with GEOS and expedite the evaluation of assimilating observations from new instruments, AIST Earth System Digital Twin Award, NASA.
  2. (active) Co-PI: Black hole tomography with gravitational waves and artificial intelligence, New Initiative Grant, Charles E. Kaufman Foundation.
  3. (active) PI: Early Career Program - RIFLES: Rapid Information Fusion through Learning Effective Closures, Army Research Office (Modeling of Complex Systems).
  4. (active) Co-PI: Using Machine Learning to Uncover Deep Convective Cloud Processes with TRACER Observations, U.S. Department of Energy (Atmospheric System Research).
  5. (active) Institutional PI: Inertial neural surrogates for stable dynamical prediction, Data intensive scientific machine learning, U.S. Department of Energy ASCR.
  6. (active) Institutional PI: DeepFusion Accelerator for Fusion Energy Sciences in Disruption Mitigations, U.S. Department of Energy, FES.
  7. (active) Institutional PI: RAPIDS2:A SciDAC Institute for Computer Science, Data, and Artificial Intelligence, U.S. Department of Energy ASCR.
  8. (active) Institutional PI: Foundation models for surface hydrology, Argonne National Laboratory Subaward.
  9. (Finished) Co-PI: AI emulator assisted data assimilation, Future computing, LDRD-Prime, Argonne National Laboratory.
  10. (Finished) Co-PI: A Scalable, Energy Efficient HPC Environment for AI-Enabled Science, Collaborative PPoSS funding, National Science Foundation. (PI - Zhiling Lan)
  11. (Finished) PI: Margaret-Butler Fellowship project: Scalable machine learning for turbulence closure and reduced-order modeling (2 yr postdoctoral fellowship).
  12. (Finished) PI: SambaWF: Highly resolved surrogate models for weather forecasting, LDRD-Expedition, Argonne National Laboratory, U.S. Department of Energy.
Software development
  1. PythonFOAM, In-situ data analyses with OpenFOAM and Python.
  2. TensorFlowFOAM, A framework that enables the deployment of TensorFlow deep learning models and partial differential equation solutions concurrently in OpenFOAM.
  3. PAR-RL, A framework that leverages the Ray library to deploy scalable deep reinforcement learning for arbitrary scientific environments on leadership class machines.
  4. PyParSVD, A Parallelized, streaming, and randomized implementation of the SVD for Python using mpi4py.
  5. PySPOD, A package to compute the spectral proper orthogonal decomposition in Python.
Plain Academic