Evidence-Quality Telemetry for Cloud Incident Response
How should incident analysis account for missing, delayed, drifting, or contradictory telemetry?
Research
Production problems, made testable.
I am interested in questions that appear repeatedly in real systems: silent corruption, incomplete evidence, and observability under uncertainty.
A Cloud-Native Approach to Statistical Anomaly Detection and Automated Data Quality Validation
Can anomaly detection and automated data-quality controls be designed as one operational workflow?
Silent Data Corruption in Distributed GenAI-Augmented Data Pipelines
Why can a pipeline remain structurally valid while its business meaning becomes wrong?
SemCorBench: Measuring Semantic Corruption in LLM-Derived Data Artifacts
How can semantic corruption in LLM-derived data artifacts be measured reproducibly?
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.