Agentic Self-Correction
Reframing model reasoning traces as first-class, verifiable computational artifacts rather than post-hoc rationalizations — “The Dynamic Reasoning Trace.”
Recognition & distinction
A convergence of independent recognition — from the profession's senior engineering bodies, from global analyst indices, and from the peer-review community — alongside quantified enterprise impact and original, published research in agentic AI.
01Global acclaim
AI150 Global AI Influencer 2025–2026 & 2026–2027
Global AI Leader 2025
Top 50 AI Researcher (Stanford recognition)
Senior grades conferred only on members with demonstrated outstanding achievement, adjudged by expert peer panels.
02Impact at scale
As Principal AI Architect at Rivian and technical architect for the Rivian–Volkswagen JV AI transformation, delivering measurable enterprise outcomes at scale.
17+
Years of global experience
100+
Google Scholar citations
25+
Peer-reviewed publications (2023–2026)
100+
Peer reviews (verified reviewer)
30+
Technical certifications (AI, cloud, architecture)
7
Continents delivered (Asia, N. America, Europe, Australia)
$100M+
Measured annual operational impact from AI systems
$200M+
Annual operational savings driven (Samsung SDS)
$50M
Annual cost savings (Workspace migration)
50+
Agentic AI systems architected
20+
Design Thinking sessions facilitated
1,000+
Applications on Microsoft + Google Cloud
1,000+
Petabytes migrated to cloud
5M
Associates served globally
3M
Users on platforms architected
03Pioneering research
Reframing model reasoning traces as first-class, verifiable computational artifacts rather than post-hoc rationalizations — “The Dynamic Reasoning Trace.”
Closing the LLM faithfulness gap by shifting from free-text generation to modular, executable program synthesis (World Journal of AI & Robotics Research).
TurboVec — codebook-oblivious quantization for enterprise RAG: higher recall and far lower latency than trained baselines, with membership-inference risk reduced to near-random (arXiv, 2026).
Security and governance frameworks for quantized edge LLMs in 6G IoT, and a next-generation predictive-maintenance framework for software-defined vehicles (IEEE, 2026).