Scientific computing researcher • CTO • building high-performance mathematical systems

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:

• ABCRE 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.

A minimal relational calculus for computational physics. Stability through structure, not control. Invariants emerge from composition, not correction.

High-performance fluid dynamics toolkit using Metron Dynamics mathematics.

👉 Repo: qrr-marine-python

• 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, fluid dynamics

• CTO, Relational Relativity Inc. (2025–Present)
• 25+ years software engineering and technical leadership
• Six-time CTO (Symantec, FiServ, multiple startups)
• Former CISSP (#319668)
• Physics degree, Reed College (1991)

Email: energyscholar@gmail.com LinkedIn: linkedin.com/in/stephensonbruce

Building tools for tabletop RPGs — real-time multiplayer VTTs, procedural generation, rule engines.

(Updated March 2026)