Research without systems is incomplete. Systems without research are fragile.
This section documents my work at the intersection of connected surgical intelligence, multimodal perception, and high-stakes AI systems—examining not only models, but the infrastructure, reliability, and economic constraints required to deploy them responsibly.
Explore by Domain
Show all domainsEach domain reflects a layer of research and system architecture. Click to explore.
Connected Surgical Intelligence
Designing measurable, verifiable surgical systems that reduce preventable error.
AI Architecture & Infrastructure
Production-grade machine learning systems, deployment patterns, and infrastructure economics.
Governance & Reliability
Risk management, compliance, and building accountable AI systems for healthcare.
Vision & Perception Systems
Multimodal sensing, calibration, and structured scene understanding in high-stakes environments.
Foundation Models & Learning Dynamics
Representation learning, structured adaptation, and the practical limits of large models.
Connected Surgical Intelligence
Verification, Not Classification
Our first attempt at automated validation used classification. It achieved 97% accuracy in the lab. In production, it failed catastrophically on the first day. Why embedding-based anomaly detection outperforms traditional classification for industrial validation.
The Architecture of Trust
Why Hybrid Intelligence Will Define Clinical AI