Independently verifiable evidence of patents, publications, institutional affiliation, and professional experience. Every claim on this page can be cross-checked through external systems: USPTO Patent Center, Zenodo, ORCID, Google Scholar, SSRN, and employment records.
This experience reflects progressive work across data governance, infrastructure security, operational technology, and critical infrastructure systems. Each role contributed domain-specific insights that informed the development of the AUTHREX governance framework. Consulting engagements were conducted on a part-time, remote basis during academic breaks.
Formal education across applied intelligence, international business, and computer science engineering. Two STEM-designated programs (Georgetown M.P.S. Applied Intelligence, USF B.Sc. Computer Science Engineering).
Active participation in defense, aerospace, AI, and autonomous systems communities through trade shows, technical conferences, professional society events, and continuing education. Sustained engagement across these communities ensures that AUTHREX governance architectures remain aligned with operational requirements, emerging standards, and the evolving needs of the U.S. defense and critical infrastructure sectors.
Independent third-party citations, academic references, and professional community recognition of AUTHREX governance research, generated without solicitation by university journals, defense communities, and academic platforms across multiple countries.
A public comment by Burak Oktenli (Docket NHTSA-2026-0529-0007) is cited by name in the U.S. Federal Register: NHTSA, Incident Reporting for Automated Driving Systems (ADS) and Level 2 ADAS, 91 FR 30789 (May 26, 2026), among public comments referenced in the notice, which also include submissions from Virginia DOT, the Alliance for Automotive Innovation, and Consumer Reports. Verify on federalregister.gov.
Citing Sources: Baruwal Chhetri, Tariq, Grobler, Thapa et al. (arXiv preprint arXiv:2606.00088, From Frontier to Shadow AI: A Simmering Threat to Assurance and Security in Critical Infrastructure, May 2026, cites the author's SSRN analysis of AI outpacing human clearance models), Joey Hernandez / Pomona College Department of Economics (The Cascade Protocol book, ISBN 979-8195946579, May 2026), University of Toronto LexAI Journal (ERAM framework, Ref #4), Jorge Enrique Rivera Rojas / Colombia (defense logistics, cited alongside DoD & USDA), SSRN Community / Elsevier (submarine signatures), Wargaming Weekly / Bluesky (AI-driven simulation), Haberler.com / Turkey (media feature).
This research addresses a critical technical gap: how authority is assigned, monitored, degraded, revoked, and recovered in autonomous systems where decisions occur faster than human reaction time. The proposed endeavor is evaluated under the Dhanasar national-interest framework across four dimensions: substantial intrinsic merit (the what), national importance (the why), advancing the endeavor (the how), and positioning (the who).
The development of operational authority-lifecycle governance architectures for autonomous systems in U.S. national security, defense, and critical infrastructure. The novel idea is to treat authority as a graded, trust-proportional resource, assigned, monitored, degraded, revoked, and recovered under a formal lifecycle, rather than a binary on/off control. Seven formally specified architectures (SATA, HMAA, CARA, MAIVA, FLAME, ADARA, ERAM), verified with TLA+ model checking (48,751 states, 8 safety properties) and released as open, DOI-registered artifacts.
The endeavor is nationally important across economic, industry, technological, end-consumer, and societal dimensions. It addresses priorities identified by DoD Directive 3000.09, DARPA Assured Autonomy, the NIST AI Risk Management Framework, the NHTSA AV Framework, and the SELF DRIVE Act of 2026 (H.R. 7390), and its cross-domain portability spans ten operational domains (defense, automotive, maritime, critical infrastructure, orbital, counter-UAS, agentic AI, swarm, IT/OT, and financial) on one architectural foundation.
Execution is evidenced by four U.S. provisional patents, 36 published works (20 Zenodo DOIs + 16 SSRN papers), nineteen interactive simulations producing 2,800+ runs with no observed unsafe authority transitions, and twelve hardware research platforms, built on reproducible protocols (seeded PRNG, weighted Dempster-Shafer fusion, Byzantine fault-tolerant consensus, CUSUM anomaly detection) and open-access dissemination under MIT and CC BY 4.0 licenses. Four independent external citations and a U.S. Federal Register reference (NHTSA, 91 FR 30789) mark early third-party recognition.
The applicant combines formal computer-science engineering training (B.Sc., USF), graduate business proficiency (MBA, 99th-percentile standardized assessment), and a Georgetown M.P.S. in Applied Intelligence (STEM), with 140+ professional credentials across IEEE, AIAA, ACM, AAAI, INFORMS, NDIA, and Sigma Beta Delta, and active engagement across defense, aerospace, AI, and manufacturing forums, a rare combination of technical depth, cross-domain breadth, and demonstrated capacity to build and publish.
The work fills gaps identified by Congress (H.R. 7390 cybersecurity requirements), NHTSA (1,429 AV incidents, 2021-2025), and DoD (autonomous weapons governance); supports defense modernization, automotive safety, maritime security, and critical infrastructure resilience; prevents autonomous escalation (FLAME), detects adversarial manipulation (ADARA), and provides cryptographically auditable authority chains. As independent, cross-sector research that no single employer could reasonably sponsor, it is intrinsically cross-employer in scope, supporting a waiver of the job-offer and labor-certification requirements in the national interest.
Includes the full four-part National Interest Waiver analysis, research programs, roadmap, policy impact, and deployment scenarios
Independently Verifiable Documentation: every record below links to an external system operated by an independent third party (USPTO, Zenodo, ORCID, Google Scholar, SSRN, ResearchGate).
HMAA: U.S. Provisional No. 63/999,105 (March 7, 2026)
CARA: U.S. Provisional No. 64/000,170 (March 9, 2026)
SATA: U.S. Provisional No. 64/002,453 (March 11, 2026)
FLAME: U.S. Provisional No. 64/005,607 (March 14, 2026)
All four submitted via USPTO Patent Center. Awaiting review.
HMAA: 10.5281/zenodo.18861653 ↗
CARA: 10.5281/zenodo.18917790 ↗
SATA: 10.5281/zenodo.18936251 ↗
MAIVA: 10.5281/zenodo.19015517 ↗
FLAME: 10.5281/zenodo.19015618 ↗
ADARA: 10.5281/zenodo.19043924 ↗
HMAA-UAV: 10.5281/zenodo.19128769 ↗
Testbed: 10.5281/zenodo.19143190 ↗
BLADE-EDGE: 10.5281/zenodo.19177472 ↗
BLADE-AV: 10.5281/zenodo.19232130 ↗
BLADE-MARITIME: 10.5281/zenodo.19246785 ↗
BLADE-INFRA: 10.5281/zenodo.19277887 ↗
BLADE-SPACE: 10.5281/zenodo.20183269 ↗ (TRL 2-3 Preliminary Design Phase, 15-document engineering package)
BLADE-CUAS: 10.5281/zenodo.20299604 ↗ (counter-UAS, TRL 2-3 hardware / 3-4 simulation)
BLADE-AGENT-HSM: 10.5281/zenodo.20299821 ↗ (agentic-AI hardware root of trust, TRL 2-3 silicon / 3-4 emulator)
BLADE-SWARM: 10.5281/zenodo.20351198 ↗ (attritable swarm autonomy, TRL 3-4 simulator / spec, TRL 2 testbed)
BLADE-INFRA-OT: 10.5281/zenodo.20342067 ↗ BLADE-FINANCE: 10.5281/zenodo.20374692 ↗ (IT/OT bridge governance, TRL 2-3 hardware / 3-4 simulation)
View All on Zenodo ↗Georgetown University listed on Zenodo publication records. M.P.S. Applied Intelligence program (STEM-designated), School of Continuing Studies.
ORCID 0009-0001-8573-1667, verified researcher identity linking publications, patents, and institutional affiliation.
Verify on ORCID ↗Publication index with citation tracking and research metrics.
Verify on Google Scholar ↗Citation 1 (May 2026): SSRN paper AI-Enabled Military Decision-Making and Escalation Risk: Human-Machine Command Authority in Great Power Competition (SSRN ID 6082847) cited in Hernandez, J. (2026), The Cascade Protocol (Pomona College, Department of Economics), ISBN 979-8195946579, DOI 10.5281/zenodo.20113664. Reference #36 of 36, alongside the U.S. Department of War AI Strategy (January 2026), King's College London, US Army War College, Harvard Law School PILAC, Lieber Institute West Point, Carnegie Endowment for International Peace, Atlantic Council, and NDU Press.
Citation 2 (April 2026): SSRN paper ERAM: Escalation Risk in AI-Enabled Command and Control (SSRN ID 6176802) cited as Reference #4 in "The Ethics of AI in U.S. Warfare," LexAI Journal, University of Toronto Law and Ethics of Artificial Intelligence Students' Association.
Citation 3 (May 2026): SSRN paper The strategic convergence: AI has outpaced human clearance models (SSRN 5940814) cited as reference [41] in Baruwal Chhetri, M., Tariq, S., Aamir, T., Grobler, M., Thapa, C., & Singh, R. (2026), From Frontier to Shadow AI: A Simmering Threat to Assurance and Security in Critical Infrastructure, arXiv:2606.00088, an empirical study of 27 Australian critical-infrastructure organisations. The applicant's work is cited twice, on shadow AI at the interaction layer and as an insider-risk condition.
Citation 4 (June 2026): SSRN paper AI-Enabled Military Decision-Making and Escalation Risk: Human-Machine Command Authority in Great Power Competition (SSRN ID 6082847) cited in endnote 39 of Panwar, R.S. (2026), Cognition, Agency, and Authority: A Taxonomy of Advanced AI Systems with Military Implications, an Academia.edu preprint by a retired Indian Army Lieutenant General and computer-science doctorate from the Indian Institute of Technology Bombay.
View Full Citation Detail ↗14 distributed scholarly papers on AI governance, escalation risk, and national security. 8,197 cumulative downloads; SSRN Author Rank 14,599 of 2,727,789 (top 0.54%).
Verify on SSRN ↗Research profile with full-text access and academic networking. Hosts public PDFs of distributed research and policy papers.
View on ResearchGate ↗Authorized to work in the United States; employment at Blue.Cloud (Tampa, FL, 2021-2024). Data governance and cloud infrastructure in regulated environments.
Ten-volume technical reference series across three book series under the Authority & Architecture publisher imprint: The Authority Equation (3 volumes), The Authority Discipline (4 volumes), and Autonomous Authority (3 volumes). All volumes assigned ISBNs registered through Bowker, distributed via Amazon and IngramSpark in print and ebook formats. The Authority Equation Volume I assigned Library of Congress Control Number 2026912260.
View Catalog at Authority & Architecture ↗IEEE Member (#102193505) · AIAA Student Member (#1936005) · ACM Member (#9952787) · AAAI Member (#656504) · INFORMS Member (#2009712) · NDIA Member (#1700222) · Sigma Beta Delta Lifetime Member (#2007930) · DARPAConnect (public DARPA outreach program, no affiliation) · NIST AI RMF Trustworthy AI in Critical Infrastructure Profile Community of Interest (Member, 2026). Plus 140+ additional credentials from 25+ institutions.
View All 140+ Credentials ↗