Reliability Engineering
Designing resilient AI systems with consistent behavior under distribution shifts and operational stress.
Research Area
AI Safety
Advancing AI safety through research in reliability, evaluation, governance, verification, and responsible deployment. We build frameworks and safeguards that help intelligent systems operate predictably, transparently, and securely in real-world environments.
Iris VerifyIris Guard
Core Research Pillars
Evaluation Frameworks
Building rigorous benchmarks, stress tests, and quality gates to measure model performance and risk.
Governance Systems
Establishing policy-aware controls, auditability standards, and lifecycle oversight for AI operations.
Identity & Trust Verification
Developing verification and integrity checks for identity, content authenticity, and system trust.
Responsible Deployment
Applying safety-by-design principles for secure rollout, monitoring, incident response, and remediation.
Current Focus
01
Model risk scoring and failure mode mapping
02
Safety evaluation pipelines for production release
03
Fraud and identity verification intelligence
04
Governance and audit-ready compliance controls
Build Trustworthy AI at Scale
Collaborate with OpenQCore Research to develop robust, verifiable, and responsibly deployed AI systems.