expert-validated historical cases
AI-assisted log analysis
& root-cause diagnosis
NVIDIA internship
Evaluating AI-assisted diagnostics
with reproducible evidence.
Built a Python evaluation harness that scaled coverage from dozens to 200+ expert-validated historical cases and enabled blinded comparisons across agent backends.
- Scoring & reliability
- Developed deterministic and ensemble-based semantic scoring for diagnostic quality, evidence grounding, abstention, reliability, and latency. Added stage-level timing and error traces.
- Making results useful
- Created a live dashboard and CI-ready reports with per-case drilldowns and release comparisons. Extended a Kubernetes-native framework with secure container and API adapters.
