BACKGROUND

I'm an independent AI safety researcher with a focus on red teaming, data engineering, and tool development for improving model safety. My approach combines hands-on experimentation with systematic analysis, developing practical solutions while researching fundamental safety issues.

Over the past year, I've immersed myself in AI safety research through competitive red teaming, developing specialized tools for dataset creation and fine-tuning workflows. My work includes building LLM Scribe, a tool for creating hand-typed datasets with multiple output formats optimized for various training frameworks.

My background in 3D visualization and technical art at studios like MPC, Onchain Studios, and Technicolor developed my skills in complex problem-solving, pipeline optimization, and working with large-scale technical systems - skills that translate directly to understanding and improving AI safety mechanisms.

RESEARCH & DEVELOPMENT FOCUS

TECHNICAL SKILLS

DIVERSE TECHNICAL BACKGROUND

My technical experience spans from 3D visualization pipelines to electrical engineering projects (including PCB design and hardware prototyping). This breadth of experience allows me to approach AI safety challenges from multiple angles, identifying vulnerabilities and solutions that might be missed by a more narrow focus.

While my formal education is in Computer Animation (B.F.A., Ringling College), my self-directed learning in AI safety, data science, and engineering demonstrates my ability to rapidly acquire expertise in complex technical domains - a critical skill for the evolving field of AI safety research.