Nathan Baune
Platform & Systems Engineer
I build production tools, ML systems, and complex software platforms — from real-time EEG classification and neuroimaging pipelines to a GPU-accelerated simulation engine with its own visual design environment, multi-language CLI, and LLM-powered natural language interface.
The thread connecting my work: studying how information processing architectures produce emergent behavior—from neural circuits to ML models to simulated ecosystems.
For the past decade I've built production systems in neuroscience and health tech:
- Research tools and systems shipped to 9 labs across 3 institutions.
- 2 startups: co-founder (PlatformSTL LLC), founder & principal engineer (Gothic Grandma LLC).
- MR.Flow: GUI orchestrating neuroimaging pipelines with parallel execution optimized to user hardware. Setup and deployment reduced from weeks to ~45 minutes.
- Epoche: ML workbench for neurophysiology—feature extraction, grid search, ensemble optimization, and interpretability tools for mechanistic hypothesis generation and model/ensemble deployment.
- Real-time ML classification of brain state for closed-loop experiment control, therapy onset, and neurostimulation. <40ms end-to-end latency.
- Proprio (PlatformSTL, St. Louis): STTR-funded wearable activity classification platform (hardware → cloud → ML pipeline) for monitoring patient care and long-term outcomes.
- Countless rapid-turnaround prototypes and hours of engineering, scientific, and statistical consulting.
Technologies
Current Interests
I'm actively exploring the intersection of AI and complex systems:
- Agentic AI architectures — how multi-model orchestration, tool use, and planning emerge in LLM-based systems
- Interpretability and alignment — making AI behavior legible and predictable, from SHAP/LIME to mechanistic interpretability
- Simulation as testbed — using MUSE's transparent, deterministic systems to study how architectural choices shape emergent behavior
- Human-AI collaboration — designing interfaces where AI augments rather than replaces human judgment (see: BABEL, Epoche)
Read more: How this question connects everything I've built →
Platform & Build Engineering
Build, codegen, packaging, runtime infrastructure. Click a thumbnail to view it below.
—
Research Tools, Lab Infra & Applications
ML / signal-classification apps, real-time lab signal infrastructure, and research-support utilities.
—
Recent Writing
What I Learned from Years Working with "Black Boxes"
Build for inspection. Make state observable. Use AI for what it excels at.
Why ML Interpretability Needs Storytellers
Epoche: making ML legible to domain experts, not just engineers.
The Divide Between Deterministic Simulation & Black-Box AI
Why MUSE uses LLMs for translation, not generation.
From Research Pipelines to Production
Lessons learned from years operating across different engineering worlds.