Contact me if interested in Postdoc or Student Research positions in shape-based computer vision for scientific image analysis or uncertainty quantification for robust machine learning.

Excited to announce the Workshop on Drawings and abstract Imagery: Representation and Analysis (DIRA) at ECCV 2022! More info

Research Interests

I am a machine learning researcher in the Information Sciences group at the Los Alamos National Laboratory. I develop machine learning methods to help scientists analyze complex data. My research areas include trustworthy and reliable AI, probabilistic graphical models, unsupervised machine learning, interactive machine learning, transfer learning and lifelong machine learning. Applications include image analysis and remote sensing.

  • Project Leader / Principal Investigator:
    • Uncertainty quantification for robust machine learning (UQ4ML)
    • Visual data analytics tools (VDAT)
    • Image analysis using graphs (GoFigure)
  • Applications of machine learning projects include learning image analysis, ChemCam spectroscopy data analysis, and other scientific data analysis problems.


See longer publications list.


Open source code available for

  • GoFigure collection for retrieval and analysis of technical images: GoFigure
    • Visual Hash for matching copies of visually similar images: VisHash Example of VisHash to compare 3 drawings
  • Bayesian discovery of multiple Bayesian networks: beandisco_multi
  • Bayesian discovery of Bayesian networks with informative priors: beandisco_prior