Projects

use large language models for aiding Fortran simulation code translation to C++

use mixture of LLMs with prompting (zero/few shot learning) and fine tuning

create workflows and GUIs, uncertainty analysis and validation.

create predictive machine learning models for in-depth analysis and visualization of ensemble wildfire data

create explainable models for such large-scale ensembles

create in situ ML algorithms for modeling extreme events and perform sophisticated statistical inference,

use neural networks for Gaussian Process parameter estimation, unsupervised clustering for feature tracking/identification

couple Julia runtime with Fortran simulation codes, develop new streaming and parallel distributed inference techniques, scalability analysis and handle extreme-scale simulation data

deliver to open-source repo PRISM .

under Exascale computing project, develop automatic feature-based sampling method for compression of extreme-scale spatio-temporal datasets,

contribute HistSampling filter to VTK-m/VTK-h code base

use Summit for scaling and testing

automation of LabVIEW-based tools via machine learning and feature detection algorithms,

acceleration of existing ML regression code on GPU and multi-core CPU for faster computation,

UQ for high dimensional data and PCA-based reduction, developed interactive open-source tools for data exploration,

sensitivity study for determining input parameter saliency.