I'm Gayatri — a Software Engineer and AI Practitioner with 4+ years of experience building enterprise-scale data pipelines, ML systems, and multi-agent AI architectures.
I believe the most elegant solutions often mirror patterns found in nature: adaptive, resilient, and beautifully interconnected. Whether I'm designing a federated learning system or a self-improving AI agent, I'm always drawn to architectures that feel alive.
When I'm not training models or debugging pipelines, you'll find me on a hiking trail with my camera, lost in a book, experimenting in the kitchen, or chasing the next flight somewhere new. I travel to collect perspectives — and I bring every one of them back to my work.
Currently pursuing my MS in Applied Data Science at SJSU, I'm focused on the frontiers of Explainable AI, multi-agent systems, and GenAI. I'm open to collaborations that push boundaries and make everyday systems genuinely smarter.
PrismGraph AI — Researchers waste hours drowning in disconnected PDFs. Standard AI chatbots hallucinate and lose context across papers. PrismGraph entirely deconstructs research papers and builds them into a living, interactive knowledge graph — automatically extracting authors, methodologies, claims, datasets, and citation relationships. Ask a question, get an evidence-backed, fully verifiable answer drawn across a web of interconnected knowledge.
Setu — A voice agent for chemotherapy patients that only answers from a verified oncology/pharma dataset, refuses to guess outside it, speaks empathetically, and shows the exact source used for every answer.
HydraSwarm — Simulates a 7-agent software engineering company where every agent queries HydraDB before acting and stores lessons back after. Run a task once, score 7/10. Run a similar task again — agents recall what went wrong. Score goes up. Uses 7 distinct HydraDB capabilities: knowledge ingestion, sub-tenants per agent, shared org memory, hybrid recall, graph relations, and inference. 325 unit tests across 21 suites.
Recognized internally at Michelin for exceptional initiative, consistent high-quality delivery, and cross-team impact in analytics.
Thoughts on AI, data, and the world we're building.
Data centers consumed 1.65 billion gigajoules of electricity in 2022. With AI demand projected to grow 35–128% by 2026, the energy cost of intelligence is a problem we can no longer ignore.
How I designed a 7-stage multi-agent system that learns from its own mistakes and improves decision quality over time using persistent organizational memory.
From logit lens to gradient attribution — a practical guide to understanding what large language models are actually doing when they generate text.
Whether it's a collaboration, an opportunity, or just a hello — my inbox is always open.