Scientific computing researcher • computational physics • critical transitions
I work at the intersection of early warning signals, critical transitions, and computational physics. Current focus: validating novel early warning signal methods for detecting critical phenomena in complex systems.
Developing and validating computational methods for detecting critical transitions in complex systems. Recent work:
- Validation suite: 10 modules, 44 tests validating a novel nonlinear bandpass filter on experimental thermoacoustic data
- ewstools contribution: Implementing DFA (detrended fluctuation analysis) for the standard Python EWS toolkit
- arXiv paper: "Convergent Discovery of Critical Phenomena Mathematics Across Disciplines" (arXiv:2601.22389)
Applications: climate tipping points, ecosystem transitions, financial crises, epidemiological regime shifts.
- Scientific computing: Python (NumPy, SciPy, matplotlib), Rust
- Methods: FFT/spectral analysis, Monte Carlo, power-law fitting, time series analysis, statistical hypothesis testing
- Infrastructure: TypeScript/Node, WebSockets, real-time state sync, AWS, Docker
- Domains: Early warning signals, statistical physics, dynamical systems
- 25+ years software engineering and technical leadership
- Former CISSP (#319668)
- Physics degree, Reed College (1991)
Email: energyscholar@gmail.com ORCID: 0009-0005-6842-6686 LinkedIn: linkedin.com/in/stephensonbruce
Building tools for tabletop RPGs — real-time multiplayer VTTs, procedural generation, rule engines.
(Updated November 2026)