AI Safety for the Real World

Mission-Critical Technical and Operational Leadership

More than 20 years leading technical and operational teams across government, defense, academia, nonprofit, and industry. Senior leadership on Department of Defense and Veterans Affairs programs within funding environments exceeding $100 billion ⓘ, spanning enterprise architecture, decision-support systems, security and compliance, electronic health records, cloud systems, and data strategy. Led programs with multimillion-dollar budgets and worked directly with senior leaders across defense, government, and institutional settings.

International Defense and Security Systems

At the Naval Postgraduate School, served as a DoD civilian program leader and Principal Investigator responsible for programs in defense technology, modeling and simulation, collaboration platforms, and decision-support systems, with more than $9.8 million ⓘ in fully funded federal proposals across six programs. Every proposal was funded at the amount requested. Work included counterterrorism, counterinsurgency, peacekeeping operations, and international collaboration across more than 100 countries. Recognized directly by the Undersecretary of Defense for Intelligence:

Research in Complex Adaptive Systems

The research foundation of this work is CASS, the Complex Adaptive System Simulation framework, a federally validated simulation platform under the US Department of Defense Verification, Validation, and Accreditation (VV&A) process, built and published over nearly two decades. Originally developed at the Naval Postgraduate School for counterterrorism, peacekeeping, and international conflict environments, CASS has since extended to organizational behavior, social policy, and human-machine interaction. The framework models the emergence of beliefs, values, and interests across whole societies at scale, applying network science, discrete event simulation, and behavioral survey data to forecast group-level dynamics in high-consequence environments.

Published work spans agent-based modeling methodology, social simulation validation, cognitive modeling from behavioral data, and violent extremist network dynamics. More than 20 peer-reviewed publications, book chapters, and conference papers, with an H-index of 7, more than 100 citations, and 8 highly influential papers indexed on Semantic Scholar.

Applied Complexity in Live Equity Derivatives Markets

From 2015 to 2022, the CASS framework was extended into live US equity index derivatives markets as a rigorous, high-stakes validation of the methodology. Over seven years, agent-based simulation of coupled autonomous execution systems and human traders was applied to identify and operationalize the emergent cascading failure patterns those coupled systems produce under volatility stress. Significant in-silico testing preceded any live market participation, and simulation and live operation then ran together, applying the same pre-deployment validation standard that now grounds the AI safety governance work.

The operation was formally structured, with institutional-grade exchange access, multi-platform infrastructure across futures and options on ES and NQ contracts, and legal and tax architecture appropriate to the complexity of the instruments. The results validated the core thesis: that complex adaptive systems producing emergent behavior under stress are modelable in advance, governable through disciplined simulation, and consequentially different from what static evaluation frameworks can detect. During this period, formal participation in sessions convened by the CFTC, SEC, CME, and CBOT on the development of regulatory frameworks for cryptocurrency futures and derivatives contributed practitioner perspective at the formative stage of that policy development.

The intellectual through-line to AI safety governance is direct. Emergent behavior in live financial markets and emergent misalignment in AI systems at scale share the same underlying structure: nonlinear dynamics in human-automated systems that static testing cannot anticipate, that manifest only at scale and under stress, and that carry asymmetric consequences when governance fails. This research program is ongoing. Partnership with organizations seeking to extend this work into AI-relevant domains is actively welcomed.

AI Governance at the Intersection of Technology and Human Systems

The most significant gaps in real-world AI safety governance are not purely technical. They are organizational, institutional, and deeply human. Doctoral research at USC's Suzanne Dworak-Peck School of Social Work centers on governance architecture for AI systems deployed in professional and high-stakes institutional contexts. This builds on a research lineage running from graduate work on network-theoretic organizational resilience at the University of Connecticut, through doctoral training in computational modeling and simulation at the Naval Postgraduate School, to current work on the governance conditions that determine whether AI safety frameworks hold under real institutional pressure.

The capstone examines LLM governance in professional practice environments, arguing that the decisive barrier to safe AI integration is a governance architecture problem rather than a technical one, grounded in antifragile systems theory, community-based participatory design, and the organizational conditions that determine whether safety holds under real institutional pressure.

Approach draws on sociotechnical systems theory, organizational behavior, industrial psychology, human-centered design, and macro social work: disciplines that illuminate how people actually act inside institutions and how to intervene at the level of structural conditions rather than surface behaviors. That is precisely the level at which real-world AI governance must operate.