Work
Research, publications, and professional experience.
Research
IEEE ICBC 2026
LPOR: A Layered Proof of Reserves Framework for Usable and Publicly Auditable Solvency Verification
Traditional Proof of Reserve systems are technically difficult for everyday users. LPOR introduces layered verification that separates lightweight public verification from heavyweight cryptographic verification, making exchange solvency verification significantly more accessible.
Future Research
Proof of Transaction (PoT)
Proof of Reserves verifies whether a centralized exchange holds sufficient assets, but it does not provide evidence that transactions are executed faithfully. Proof of Transaction (PoT) extends cryptographic transparency to the transaction layer by requiring exchanges to leave publicly verifiable commitments to transaction execution.
These commitments are intended to make practices such as dishonest order matching, price manipulation, front-running, or phantom liquidity more difficult to conceal. Rather than requiring every user to perform verification directly, PoT explores how transaction evidence could be audited by affected users, regulators, independent auditors, or technically capable public verifiers.
The research investigates commitment schemes, verifiable computation, and privacy-preserving proofs to provide meaningful auditability while protecting sensitive user data and proprietary exchange information.
Research Roadmap
Publications
Book • Amazon
Bitcoin Minibook
400+ copies distributed worldwide. An educational resource making Bitcoin approachable for beginners.
Talks & Presentations
IEEE ICBC 2026
Presented LPOR research in Brisbane, Australia.
Boeing Engineering Presentations
Internal engineering conferences (2024 & 2025).
Changwon CEO Academy
Public Bitcoin education session.
Professional Experience
Boeing
Software Engineer
Airplane Configuration Manager
- Proposed and led the development of a cross-site Engineering Agent Proof-of-Concept across teams in Seoul, Istanbul, and Seattle, integrating AI capabilities into the Airplane Configuration Manager by orchestrating multidisciplinary engineering analysis workflows over a version-controlled knowledge graph.
- Led development of Airplane Configuration Manager, a tool for managing the Airplane Object Model (AOM) data on Harmony-Fabric, Boeing's Ontology Platform, enabling faster multidisciplinary trade studies supporting Airplane Configuration & Integration and MDAO workflows.
- Developed a Python-based API for data ingestion into the ontology platform. Achieved a 40% reduction in processing time by replacing file-based uploads with direct updates to target knowledge graph sections.
- Engineered a merge conflict resolver by leveraging node handling within the knowledge graph structure, enhancing granular version control and design iteration efficiency.
- Automated engineering workflows by building pipelines connecting GitLab, Jira, and Flight Testing Platform using Node.js Express, MySQL, and Kafka. Configured webhooks to trigger server-side processing for target events. Designed an authorization system to enhance security and access control. Established CI/CD pipeline on Azure DevOps for Build/Test/Deploy stages.