SmartTensors: Unsupervised Machine Learning
2021-07-29, 12:30–13:00 (UTC), Green

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.


The world’s most valuable resource is no longer oil. It is data. SmartTensors (http://tensors.lanl.gov; https://github.com/SmartTensors) is 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. The workshop will demonstrate how SmartTensors can be easily applied to these and other application areas. The workshop will include hands-on real-time demonstrations of already existing case studies. The workshop will be designed to be suitable and useful for anyone regardless of their machine learning experience by providing materials at introduction, intermediate and expert levels.

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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