Dr. Ozgur Ural
I design secure ML architectures and build distributed systems that cannot fail.
I'm Dr. Ozgur Ural. With over a decade of engineering experience and a Ph.D. in Machine Learning (conferred at ERAU, 2025), I bridge the gap between rigorous academic research and production reality. As a Senior Software Engineer, I focus on architecting fault-tolerant distributed systems, driving technical standards, and delivering enterprise platforms where failure is not an option. My doctoral work and published research on proof-of-learning, model watermarking, adversarial robustness, and blockchain-enhanced ML (four IEEE Access papers, 2023–2025) ground my approach to securing AI infrastructures.
Currently in Leiden, Netherlands. Building, architecting, publishing.
- 11+Years engineering
- Ph.D.ERAU · 2025
- SystemsDistributed & Cloud
- FocusMission-critical code
Systems Architecture & Core Domains
My work focuses on the intersection of deep technical expertise and organizational scale: driving architectural decisions, setting technical standards, and ensuring that complex systems—from mission-critical C++ to cloud-native microservices—remain robust under extreme conditions.
- Systems Architecture
Technical Excellence
Designing real-time, mission-critical systems. Bringing rigorous engineering discipline to ML pipelines and establishing architectural patterns that elevate the entire engineering organization through scalable code and resilient design.
- Fault-Tolerant Distributed Systems
Enterprise infrastructure
Architecting scalable cloud-native services (Scala, TypeScript, gRPC) and designing robust distributed protocols whose outputs and state can be audited end-to-end without compromising latency.
- Trustworthy ML & Security
Securing AI pipelines
Deep domain expertise in defending ML training integrity against spoofing attacks. My Ph.D. and IEEE-published research focus on feature-based model watermarking and proof-of-learning verification.
- Adversarial Robustness
When models can be trusted
Bridging research and production by ensuring models survive contact with adversaries. Evaluating provenance verification, adversarial examples, and the limits of claimed model identities.
Recent
- Dec 2025 Joined the Program Committee for the 2nd Workshop on NLP Applied to Information and Cyber Security (NLPAICS 2026), University of Alicante. Conference.
- Dec 2025 Published SecurePoL: Integration of Watermarking with Proof-of-Learning to Enhance Security Against Spoofing Attacks in IEEE Access. Read paper.
- Aug 2025 Conferred Ph.D. in Electrical Engineering & Computer Science (ERAU). Dissertation: Enhancing Proof-of-Learning Security Against Spoofing Attacks Using Model Watermarking. Advisor: Dr. Kenji Yoshigoe. Dissertation · Verify diploma.
- Jul 2025 Successfully defended Ph.D. dissertation.
- Nov 2024 First-author paper Feature-Based Model Watermarking for PoL in IEEE Access. Read paper.
- Dec 2023 Published the survey Blockchain-Enhanced Machine Learning in IEEE Access. Read paper.
- May 2021 Published Automatic Detection of Cyber Security Events from Turkish Twitter Stream and Newspaper Data at ICISSP. Read paper.
Selected Work
SecurePoL: Watermarking + Proof-of-Learning
Defends proof-of-learning protocols against spoofing by integrating feature-based model watermarking with verification.
Enhancing Proof-of-Learning Security Against Spoofing Attacks
Doctoral thesis formalizing watermark-augmented PoL, threat models, and empirical robustness analysis.
Feature-Based Model Watermarking for PoL
First-author paper introducing the watermarking-based defense layer for proof-of-learning systems.
Survey on Blockchain-Enhanced Machine Learning
Comprehensive survey of the design space at the intersection of distributed ledgers and learning systems.
Get in touch
For research collaboration, technical advisory, or speaking engagements, reach out by email, or browse my publications, projects, and technical writing.