Recent Work
Extended Google's NeurIPS 2025 Nested Learning paper with bidirectional knowledge bridges—improving continual learning performance by +89% at high regularization settings. Implemented the full system in a single day, demonstrating the feasibility of rapid research-to-production when combining deep domain knowledge with production engineering discipline.
The work addresses a critical challenge in production ML: how to enable models to learn new information without forgetting established knowledge. This matters particularly in safety-critical domains like healthcare, where preserving baseline capabilities while adapting to new protocols is essential.
Research Implementation at Production Scale
I specialize in translating frontier research into production-ready systems. This capability comes from 20 years building integration layers across domains—from language instruction in Paris to voice-first field applications for beekeeping to cross-platform gaming APIs. When I encounter a new ML architecture, I recognize patterns I've implemented before in other contexts.
This pattern recognition enables rapid implementation without sacrificing production quality: comprehensive testing, proper error handling, and systems designed for reliability in safety-critical environments.
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