Our goal is to enable scientists to concentrate on science by minimizing the effort required to explore and to understand the data generated by their simulations and experiments. This goal drives research efforts in large-scale visualization, data compression and streaming, data management and workflows, data analysis, and machine learning. To learn more about what we do, we invite you to look at some of the projects to which our group members contribute in the areas of precision medicine, climate modeling, cancer research, simulation workflows, data compression, and materials science. Our funding comes from a variety of sources including ASC, ECP, Laboratory Directed Research and Development, and the National Cancer Institute.

Group Lead

Brian Gallagher: scientific machine learning, applied machine learning, network science

Research Staff

Indrasis Chakraborty: applied machine learning, control theory, time series forecasting, dimensionality reduction

Vuthea Chheang

Ya Ju Fan: optimization models and algorithms, data classification and clustering, dimensionality reduction

Ming Jiang: scientific machine learning, data-intensive computing, multiresolution analysis, flow visualization

Hyojin Kim: computer vision, machine learning, image analysis, CT and inverse problems, GPU computing

Dan Laney: scientific visualization, data compression, multi-resolution algorithms, simulated radiographic diagnostics for verification and validation, computational science workflows, data management

Peter Lindstrom: data compression, scientific visualization, scientific computing

Yang Liu

Haichao Miao

Aditya Ranganath

Brian Weston: compressible fluid dynamics, fully-implicit solvers, high-explosive cookoff modeling