1. (active) PI: Inertial neural surrogates for stable dynamical prediction, Data intensive scientific machine learning, DOE-ASCR FOA2493.
  2. (active) Co-PI: AI emulator assisted data assimilation, Future computing, LDRD-Prime, Argonne National Laboratory, U.S. Department of Energy. (PI - Rao Kotamarthi)
  3. (active) Senior personnel: RAPIDS2:A SciDAC Institute for Computer Science, Data, and Artificial Intelligence, U.S. Department of Energy. (PI - Rob Ross)
  4. (active) External collaborator: Prediction-to-Mitigation with Digital Twins of the Earth's Weather, MOE Tier-2 Grant, Singapore. (PI - Gianmarco Mengaldo)
  5. (Finished) Co-PI: A Scalable, Energy Efficient HPC Environment for AI-Enabled Science, Collaborative PPoSS funding, National Science Foundation. (PI - Zhiling Lan)
  6. PI: Margaret-Butler Fellowship project: Scalable machine learning for turbulence closure and reduced-order modeling. (2 yr postdoctoral fellowship).
  7. 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.


  1. Reviewer - AIAA Journal, Applied Mathematical Modeling, Chaos, Computer Methods in Applied Mathematics and Engineering, Communications in Computational Physics, Computers and Fluids, Computer Physics Communications, International Journal of Computational Fluid Dynamics, IEEE Transactions on Plasma Science, Journal of Fluid Mechanics, Journal of Scientific Computing, Physics of Fluids, Physica D, International Journal of Numerical Methods in Fluids, Journal of Nonlinear Science, Nature Communications, Nature Machine Intelligence, Nature Scientific Reports, Theoretical and Computational Fluid Dynamics, Atmospheric Science Letters, New Journal of Physics, Transactions of Machine Learning Research.
  1. Organizer - AI, Statistics and Machine Learning Journal Club, Argonne National Laboratory.
  2. National Lab Day Speaker (Outreach program for middle school children), Computational Fluid Dynamics Laboratory, Oklahoma State University 2017, 2018
  3. NAACP ACT-SO Mentor, Chicagoland High Schools, 2020-Present.
  4. Hour of code volunteer, Downers Grove Junior High School, 2022.
Workshop participation
  1. Invited participant, Vistas in the Applied Mathematical Sciences, Institute for Mathematical Statistical Innovation (IMSI), The University of Chicago, IL, 2020.
  2. Invited participant, NSF workshop on Machine Learning for Transport Phenomena, Southern Methodist University, TX, 2020.
  3. Invited participant, Mathematics of Reduced Order Models, ICERM, Brown University, RI, 2020.
  4. Invited participant, Algorithms for Dimension and Complexity Reduction, ICERM, Brown University, RI, 2020.
  5. Invited participant, IPAM Workshop III: Validation and Guarantees in Learning Physical Models: from Patterns to Governing Equations to Laws of Nature, UC Los Angeles, October 2019.
  6. Invited participant, Department of Energy - AI for Science Townhall, Argonne National Laboratory, June 2019.
  7. Invited participant, Advances in PDEs: Theory, Computation and Application to CFD, ICERM, Brown University, RI, 2018.
  8. Invited participant, SDSC Summer program in HPC and Data Science, UC San Diego, 2017.
Conference participation
  1. MS Organizer - Recent Advances in Data-Intensive Physics-Informed Machine Learning for Accelerating Computational Science, USNCCM, 2023, New Mexico.
  2. MS Organizer - Non-intrusive reduced-order modeling via deep learning strategies, ECCOMAS Congress, 2022, Oslo Norway
  3. MS Organizer - Acceleration and Enhancement of High-fidelity PDE Solvers through Machine Learning, 16th U.S. National Congress on Computational Mechanics, IL, 2021
  4. MS Organizer - Domain-Aware, Interpretable and Robust Machine Learning for Computational Science, SIAM Virtual Conference on Computational Science and Engineering, 2021
  5. MS Organizer - Domain-Aware, Interpretable and Robust Scientific Machine Learning Methods Applied to Computational Mechanics, AIAA Aviation Forum, Reno, NV, 2020
  6. MS Organizer - Data-driven methods for computational fluid dynamics, SIAM Conference on Computational Science and Engineering, 2019
  7. Session chair - MAE Graduate Research Symposium, Oklahoma State University, 2018
  8. Organizer - 2-day TensorFlow workshop, Mechanical and Aerospace Engineering, Oklahoma State University, 2018
Plain Academic