Researcher @ NIST - Rachael T. Sexton, Systems Integration Division
Human-Systems Integration | Complex Systems Analysis | Bayesian ML | Network Analysis

• I’m a researcher at NIST, focusing on human-systems-integration and the analysis of complex sociotechnical systems.
• My work combines Python (Bayesian machine learning, network analysis), NLP for maintenance & reliability engineering, and systems thinking.
• I’m passionate about developing tools that make data annotation and knowledge capture easier and more reliable.

• Python for scientific computing, Bayesian ML, and advanced data analysis
• Complex systems analysis, especially structure recovery and metric spaces
• Kernel methods and covariance estimation
• Network analysis and graph theory
• Natural Language Processing (NLP) for maintenance management and reliability engineering
• Data annotation tool design, workflow automation

Quantifying tacit knowledge for investigatory analysis
Nestor helps users annotate their data with tags much more efficiently than traditional approaches. It’s designed to empower researchers and practitioners in reliability and maintenance domains.

• GitHub: rtbs-dev
• ORCID: 0000-0001-5904-2887
• Google Scholar: My Profile
• Dissertation: Measuring Network Dependencies from Node Activations
• Bluesky: @rtbs.bsky.social

• Board game nerd, baking enthusiast, and music lover
• Gardening keeps me grounded 🌱
• Metroidvania explorer 🦗
• Proudly carrying the title of my lab’s “graph theory propaganda vector” 🕸️

“A desire path is no more than the trace of a decision—less than that, an impulse—to find a new way to join what we know with what we have yet to discover.”
_{—David Farrier}