One research program with a single through-line: making machine learning verifiable and trustworthy: proof-of-learning protocols, feature-based model watermarking, adversarial defences, and the design space where distributed ledgers meet learning systems. Three first-author IEEE Access papers (2023, 2024, 2025), a Ph.D. dissertation, and earlier work on NLP-driven cyber-security event detection. Live record on Google Scholar; full academic CV (PDF).
IEEE Access, 2025
Dual-layer framework coupling immutable Proof-of-Learning logs with three watermarking strategies, so verification succeeds only when both the training trajectory and the watermark are consistent.
IEEE Access, 2024
Feature-based model watermarking couples an ownership check to a Proof-of-Learning training record to study defences against spoofing.
IEEE Access, 2023
Comprehensive survey of blockchain-enhanced machine learning: consensus-driven data provenance, federated learning on-chain, and incentive mechanisms across 120+ papers.
Proceedings of the 7th International Conference on Information Systems Security and Privacy (ICISSP), 2021
A workflow for turning Turkish posts and news into alerts for analyst review: select relevant terms, normalise the language and track unusual activity around named targets.
Turkish Autonomous Robots Conference (TORK 2014), 2014
Undergraduate capstone project demonstrating an autonomous cargo and mail delivery robot, presented at the Turkish Autonomous Robots Conference.
Doctoral Dissertation, 2025
Doctoral research developing SecurePoL, a dual-layer framework coupling Proof-of-Learning trajectory logs with three orthogonal watermarking strategies.
Master's Thesis, 2019
Master’s thesis introducing NLP methods for detecting cyber-security events in Turkish social media and news streams, advised by Prof. Cengiz Acartürk and the basis of the later ICISSP 2021 paper.
Technical Report (ResearchGate), 2016
Technical report on a secure proxy architecture for cloud-based services.
