I build agentic data foundations and privacy infrastructure for large-scale AI systems — semantic data layers, data governance, and compliance built directly into the data pipeline.
Building agentic data foundations and privacy infrastructure at Meta Superintelligence Labs.
The data layer that lets AI agents reason over enterprise-scale data safely. Semantic models translate tribal knowledge into machine-readable, agent-queryable representations — so autonomous systems can find, trust, and act on the right data with minimal human overhead.
Privacy-first systems that keep AI development compliant by design. Frameworks ensure data used across pre-training and fine-tuning honors commitments under GDPR, DMA, and CCPA — protecting billions of data points without slowing research velocity.
Curated, governed data foundations powering frontier AI research at Meta Superintelligence Labs. Trusted warehouse tables across diverse sources, with privacy and compliance mitigations engineered directly into the pipeline.
Scalable systems that prioritize trust and compliance — from financial fraud detection to privacy infrastructure for frontier AI.
Building agentic data foundations and privacy-first infrastructure for AI model development. Frameworks ensuring data across pre-training and fine-tuning aligns with GDPR, DMA, and CCPA while protecting billions of data points.
Led data infrastructure initiatives for real-time fraud detection and data security, architecting patented compliance automation at scale and earning the company's internal Genius Medal.
Two issued patents in graph analytics and network classification, cited by IBM and Uphold among others.
Foundational techniques for analyzing and linking large-scale entity graphs — the basis for automated, distributed violation detection — developed during my tenure at American Express. Independently cited by USPTO examiners as prior art in six subsequent applications, including filings from IBM, Zoom, and Panasonic.
A system that classifies and quantifies network effects across online platforms, using machine learning to model the evolution of users, nodes, and interconnections for more accurate behavior and growth predictions.
Articles and features on privacy-first frameworks, AI compliance, and building trust in a data-driven world.
I'm a Staff Data Engineer at Meta Superintelligence Labs, working at the intersection of agentic AI, data infrastructure, and privacy. My focus: building scalable, privacy-first systems that let data power AI responsibly — aligned with GDPR, DMA, and CCPA.
Earlier, at American Express, I led advancements in fraud detection and data security, earning two patents for scalable solutions. I hold an approved U.S. National Interest Waiver (NIW).
I hold a Master's in Management Information Systems from the University of Arizona and a B.Tech in Electrical & Electronics Engineering from Pondicherry University.
To be at the forefront of privacy-first infrastructure for the agentic era — building systems where powerful AI and rigorous data ethics are not a trade-off, but the same design decision.