My expertise is in applied mathematics, computer science and engineering. My research is in the general area of data analytics, model diagnostics and machine learning. I am the inventor and lead developer of a series of novel theoretical methods and computational related to machine learning, data analytics, model diagnostics, and data inference tools. I am also a co-inventor of LANL-patented machine-leaning methodology. Over the years, I have been the principal investigator of several projects for machine learning, model development, model analyses, uncertainty quantification and decision support
Demonstrate SmartTensors (http://tensors.lanl.gov; https://github.com/SmartTensors): a toolbox for unsupervised machine learning based on matrix/tensor factorization constrained by penalties enforcing robustness and interpretability (e.g., nonnegativity; physics and mathematical constraints; etc.). SmartTensors has been applied to analyze diverse datasets related to a wide range of problems: from COVID-19 to wildfires and climate.
GeoThermalCloud is an open-source machine-learning framework for geothermal exploration. It has been applied for the discovery, exploration, and development of Hidden Geothermal Resources.
ML4Geo is an open-source machine-learning framework designed for various types of analyses of geoscience data. ML4Geo has been already applied to investigate diverse datasets related to Earth sciences, including climatic, geologic, geophysics, geothermal, carbon storage, oil/gas, and wildfire applications