Jackson Zane

I'm Jackson, a CS and Math student at Stanford building agentic AI systems with persistent memory and interpretable architecture. My work spans both ends of applied ML: at Stanford Medicine I built a multiscale-entropy EEG model that hit 0.811 AUC while bypassing a confound that fooled standard pipelines, and at a law firm I shipped LLM-powered document processing for real case workflows. I also have a peer-reviewed publication in Statistics in Biosciences from DNA methylation research on breast cancer. Ask me about agentic AI systems, ML research in biomedical settings, and building LLM pipelines for real-world applications.