Silent data corruption in GenAI-augmented pipelines
Why can a pipeline remain structurally valid while its business meaning becomes wrong?
Research
Production problems, made testable.
I am interested in questions that appear repeatedly in real systems: silent corruption, conflicting evidence, observability under uncertainty, and the limits of authority in data integration.
Cloud-native anomaly detection and data-quality validation
Can anomaly detection and pre-model data controls be designed as one operational workflow?
Evidence-quality telemetry for incident response
How should incident analysis account for missing, delayed, or contradictory telemetry?
Authority-free reconciliation
How should a system select among conflicting sources when none deserves permanent authority?
Method
What makes a useful research question.
I start with a failure pattern that cannot be explained well by existing operational checks. The next step is to separate the local incident from the general mechanism: what conditions make the failure possible, what evidence would reveal it, and what intervention changes the outcome?
The aim is not to turn every production lesson into a grand theory. It is to make the reasoning clear enough that someone else can challenge it, reproduce it, or apply it in a different system.