I work on decision systems where ambiguity has consequences.
My focus is not model performance or feature optimization. It is how authority, accountability, and meaning behave once automation enters environments governed by oversight, escalation, and post-incident review.
Most failures I see are not technical.
They are architectural.
They occur when:
- decision authority becomes diffuse
- responsibility is implied instead of assigned
- human involvement exists in form, not function
- systems optimize locally while intent erodes globally
I study how these failures emerge—and how to design systems that resist them.
My background spans AI and machine learning, systems analysis, and technical and strategic writing in regulated, high-accountability contexts. I operate across engineering, governance, and decision-support layers, focusing on how choices are framed, constrained, reviewed, and defended.
I don’t build optimism into systems.
I build explainability, stoppability, and ownership.
If a decision cannot be clearly traced, justified, and interrupted, it does not meet the standard—regardless of performance metrics.
I work selectively, on problems where decisions must hold up not just at deployment, but afterward.