Connected Labs · AI Governance · Biopharma R&D
Automation and AI executive in biopharmaceutical R&D, and a data-science PhD. I lead the programs that connect laboratory instruments, govern the data they produce, and let AI operate where the rules are strictest — systems that pass audit, earn trust, and actually ship.
What I do
Giving every laboratory asset an identity, then connecting it — so instrument data arrives governed, traceable, and ready to use.
Qualifying the workflows scientists actually run, and making experiment context, samples, and instrument output resolve to one model.
Designing AI that does real work end to end — with humans kept firmly in the loop where it counts.
Turning GxP, 21 CFR Part 11, and emerging AI regulation into controls teams can actually operate.
Combining automation, ML, and LLMs to remove manual effort without removing accountability.
Setting the architecture, economics, and governance that let AI move past the pilot and last.
Agentic AI is an escalation pattern, not a default. Exhaust the simpler approach first — reach for the complex one only when the value genuinely requires it.
— Operating principle
Programs
The projects further down are things I built myself. These are the programs I lead or have led — multi-year, multi-site, and answerable to Quality, Compliance, and a steering committee. Described by capability; specifics stay with the people entitled to them.
Leading the enterprise lab asset management program for a global biopharmaceutical R&D organization — unique asset identity, governed equipment metadata, and the lifecycle processes that keep the registry true from capital purchase through relocation, ownership transfer, and decommissioning. On that foundation, a multi-wave programme bringing critical laboratory instruments onto the enterprise scientific data platform, sequenced by scientific workflow rather than by site so each wave delivers a complete data flow for a real experiment.
An integration between the lab asset registry and the scientific data platform that applies governed equipment metadata as data labels at the moment of ingestion. Raw instrument output leaves local drives, lands in a governed location carrying machine-readable provenance, and arrives FAIR, traceable to its source asset, and correctly segregated by compliance status — without a scientist retyping anything.
Helping lead use-case qualification for ELN and LIMS scope across Research and Pharmaceutical Sciences — turning requirements gathered across R&D functions and user personas into scored use cases, integration scope, and a defensible MVP, and supporting commercial platform evaluation. The through-line: experiment context, sample lineage, and instrument output resolving to one governed data model, which is the actual prerequisite for closed-loop experimentation.
An enterprise agentic AI governance framework synthesizing NIST AI RMF, ISO/IEC 42001, and Microsoft Responsible AI into a single GxP-aligned model — built around a risk-tiered autonomy classification that maps how independently a system acts to the controls it has to carry: human approval gates, bounded agency, mandatory observability, and audit mechanisms aligned to 21 CFR Part 11, GAMP 5, and ALCOA+. Adopted at VP/SVP level, with the finding that governance throughput, not tooling, is what limits scaling.
Owned the digital transformation portfolio for an 800-person life-sciences division — product strategy, vision, and multi-year roadmap — and built the automation Center of Excellence that governed standards, reusable assets, and ROI tracking across RPA, intelligent document processing, and AI workflows. Institutionalized use-case intake and prioritization reviewed quarterly with senior leadership.
Led a multimillion-dollar data platform modernization for a top-10 pharmaceutical firm, migrating legacy data platforms to cloud architecture across business units and standing up a program management framework across the delivery teams. Separately, established and steered a statewide vaccine administration program roll-out from stakeholder engagement through development and delivery.
Portfolio
Interactive tools and explainers on agentic AI decision-making, unit economics, and AI-first architecture — all live on GitHub Pages, one click to open.
“Should we build this?” — a six-step decision walk-through for agentic AI initiatives. Answers the questions cost models don't ask, and returns a structured view of whether to build, configure, buy, or defer — and which governance bodies belong in the loop first.
An interactive companion for understanding agentic AI cost levers: six business-friendly sliders, three preset scenarios, three pricing surfaces. Moves the conversation from “AI is expensive” to “here are the levers.”
A demonstration of disciplined AI architecture decision-making: choose the simplest pattern that solves the problem, and price the true cost of an agentic system before building it.
An interactive, client-side explainer that turns a static target-architecture slide into an explorable experience — documenting the future state of an AI-first life-sciences platform and making the semantic AI layer (ontologies → knowledge representation → models → inference) explicit.
Live Applications
Full-stack applications deployed on Render, built for regulated-industry workflows — open one and use it right now. Not screenshots: the real thing.
A production-grade LLM evaluation and human-in-the-loop review application for regulated industries — GxP-aligned quality monitoring across pharma R&D use cases, packaged for one-click deployment on Render.
An AI-powered proof of concept that analyzes clinical-trial Informed Consent Forms to classify participant consent for future and secondary data use. Returns structured, audit-ready results with confidence scoring and human-review flags — decision support, not legal advice.
Upload a cancer metabolite panel as CSV — the app auto-resolves each compound to SMILES via PubChem, then predicts co-folded 3D protein–ligand structures with the NVIDIA NIM OpenFold3 API.
A production-grade Flask application for pharmaceutical supplier onboarding — replacing a manual, nine-system procurement workflow with an intelligent orchestration layer powered by Claude.
Thought Leadership
The latest writing, pulled automatically from my Substack — where AI, automation, RPA, and BPM meet biopharma, from agentic systems and governance to M&A and valuations.
Domain
I work where science has to be both ambitious and accountable — the laboratory floor, pharmaceutical R&D, pharmacovigilance, and other settings governed by GxP and 21 CFR Part 11. That means data with provenance, controls over shortcuts, evidence over hype, and architectures that hold up when the instrument fleet is global and the audit is real.
Lab & data — Lab asset management · Instrument connectivity · ELN / LIMS / SDMS · Scientific data platforms · Data contextualization & ontologies · FAIR & ALCOA+
Compliance — GxP · 21 CFR Part 11 · GAMP 5 · Computer system validation · Pharmacovigilance
AI — AI governance · NIST AI RMF · ISO/IEC 42001 · Agentic architecture · LLM evaluation & HITL · FinOps for AI · CoE design
Published research
Identification of a Novel Biomarker Panel for Breast Cancer Screening — International Journal of Molecular Sciences, 25(21), 11835.
Clinical research credentials
GCP for Clinical Trials with Investigational Drugs & Biologics (ICH) · Biomedical Data & Specimens Research · Public Health Research · Research Study Design — CITI Program
Domain & leadership
Advanced International Pharmacovigilance & Argus Safety (APVASC) · Harvard Business School, Leading with Finance · Duke University, Healthcare Leadership · IQVIA Emerging Leaders Program
Technical & delivery
IBM Data Science · Certified Scrum Master (CSM) · Certified Scrum Product Owner (CSPO) · Deloitte Strategic Growth Transformation · SAFe delivery
Recognition
CEO Award for Operational Excellence, IQVIA · Applause Award, Deloitte Consulting · CHART Award for Excellence in Delivering Client Programs, J. Knipper
Languages
English · Hindi · Telugu
Connect
Open to conversations on lab digitalization, scientific data platforms, AI strategy and governance in regulated industries — and the occasional good problem. Pick a slot below, read my writing, or reach out on LinkedIn.