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• R&D Scientist/Engineer in Computer Science (AI Credibility) at Sandia National Laboratories, following a Ph.D. in Statistics at Iowa State University. More: About.
• I design Bayesian models for complex scientific computing challenges: surrogates for large-scale simulators like E3SM and fusion reactor design, and expressive hidden Markov models for detection. Full list of publications, talks, and posters: Research.
• R is for data munging, data visualization, and more stats-y modeling, Python for ML/DL modeling and deployment, C++ for performance, LaTeX for typesetting, GNU Make for pipelining, Unix Shell for system automation, Org-mode for note taking and planning, GNU Emacs for the ultimate text editing experience, Arch Linux for gluing it all together.
• Tackling a new problem? keep it simple and straightforward, light weight is the right weight, premature optimization is the root of all evil, don't repeat yourself (too often).
• Huge fan of note taking and list keeping, see it by yourself.
• I’ll trade off measure theory for linear algebra.