About

I began my work in a neuroscience research lab, building analysis pipelines for MEG and EEG data with a focus on source localization and spectral signal analysis. Working with high-density neural recordings shaped my approach to brain data as both a signal processing and systems engineering challenge. That experience continues to influence how I think about extracting reliable structure from noisy biological signals.

Professionally, I moved into large-scale data infrastructure, designing real-time streaming systems and cloud-native pipelines that operate in production environments. My work centers on building resilient, low-latency architectures that transform complex data into reliable, actionable outputs. This engineering background provides the operational discipline required to bring advanced computational methods out of research settings and into practical applications.

Today, I operate as an independent researcher and builder focused on bridging neuroscience, data engineering, and AI. My work applies modern streaming architectures, high-performance C++ signal processing, and foundation model adaptation to consumer-grade EEG. I prioritize open tooling and reproducible systems to accelerate progress toward accessible, real-time neurotechnology. The full bio is in my CV.

john.garcia at esca.la