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AI Governance Architecture for High-Stakes Decisions

I help banks, insurers, and public-sector institutions design AI systems with assurance built in: verifiable, governable, and accountable from the first requirement.

Executive education: MIT Professional Education · Cornell · Cambridge

Doctoral research: Purdue University

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AI Assurance Design Checklist

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About

I'm Federico J. Quijada, an AI governance architect and executive educator based in Chicago.

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For more than twenty years I led technology programs at AIG, Blue Cross Blue Shield, Wells Fargo, and United Airlines, including a $6 million AI/ML program at United. That work taught me that what holds enterprise AI back is rarely capability. It is architecture.

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My doctoral research at Purdue University focuses on memory-centric AI architecture for executive decision-making: systems where verification, accountability, and governance are designed in from the start rather than added after deployment. The architecture, SLMA (Structured Language Model Architecture), is patent-pending, with nine invention families filed through Purdue Research Foundation. I have presented this work at MIT (ICAD 2026) and at the AI Assurance & Governance Summit at Stanford, and I teach AI leadership in executive programs at MIT, Cambridge, and Cornell.

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My legal training (JD, Villanova) means I treat governance and compliance as design requirements, not afterthoughts.

 

Education: Doctor of Technology (AI) candidate, Purdue University; Master of Liberal Arts, University of Chicago; ScM in Technology Leadership, Brown University; JD, Villanova University; BA, Saint Anselm College. Read the full background on the Research & Teaching page.

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Executive Education

  • MIT Professional Education

  • University of Cambridge, ASCEND Program

  • Cornell University, Chief AI Officer Program and AI Blueprint

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How I Help

I work with leaders responsible for AI in regulated, high-stakes environments.

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AI Architecture & Assurance Advisory: design reviews and verification strategy for AI systems that inform consequential decisions.

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AI Governance for Regulated Industries: governance frameworks for banking, insurance, and government advisory that hold up under scrutiny.

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AI Contracting & Risk: aligning AI vendor contracts with how the technology actually works (data flows, subprocessors, traceability, and exit).

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Executive AI Education: programs and workshops for C-suite and Chief AI Officer audiences.

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​Speaking: keynotes and panels on AI assurance, governance, and architecture.

Selected Research and Speaking

Stanford AI Assurance & Governance Summit, October 2026: finalist paper and presentation, "Verification-by-Design: Embedding Assurance into AI-Enabled Decision Architectures."

 

ICAD 2026 at MIT, June 2026: presentation on a memory-centric AI architecture for managerial judgment.

 

BIG.AI@MIT 2026, April 2026: poster on compliance-by-design generative AI at the edge.

 

See all research, teaching and talks on the Research & Teaching and Speaking pages.

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