Machine learning research.
Software architecture.

Selected research & architecture

• 2025 · IEEE Access

  SecurePoL: Watermarking + Proof-of-Learning

  Defends proof-of-learning against spoofing by making verification depend on a feature-based model watermark as well.

• 2026 · Avion · Architecture

  Enterprise AI strategy and reference architecture

  The adoption roadmap and reference architecture I wrote for Avion, spanning on-premises inference, governed cloud APIs and edge workloads. The design distinguishes prototypes from proposed capabilities.

• 2024 · IEEE Access

  Model watermarking for Proof-of-Learning

  Studies how feature-based model watermarking can strengthen proof-of-learning against spoofing attacks.

• 2023 · IEEE Access · Survey

  Survey on Blockchain-Enhanced Machine Learning

  Reviews the design space where distributed ledgers meet learning systems.

Research agenda

Training evidence, adversarial robustness and the conditions for using learned systems in practice. Published work and open directions are identified below.

1. Published · IEEE Access 2023–2025 · Ph.D. 2025

   Proving how a model was trained, and who owns it

   Models are bought, fine-tuned and shipped by people who did not train them. Proof-of-learning lets a trainer show the training was really done; a watermark lets an owner show a model is theirs. Each can be forged on its own.

   My dissertation and SecurePoL study combine the two, so verification succeeds only when the training record and the watermark agree, and evaluate the result against specified spoofing attacks. Publications

2. Published · IEEE Access 2024–2025

   Testing security claims against an adversary

   A verifier can accept a plausible training record that was never honestly produced. A watermark can also be attacked. The useful question is which attacks a defence withstands, under which assumptions and at what cost.

   My research studies spoofing attacks on proof-of-learning and the use of model watermarking to strengthen verification. Watermarking study · SecurePoL

3. Open research direction of the author, not yet published

   From model quality to system-level evidence

   Accuracy alone does not establish that a learned component is useful inside a larger system. Timing, the freshness of its inputs and the behaviour when it is unavailable also need to be evaluated.

   I am exploring these questions through an executable architecture experiment with asynchronous inference, expiry checks and fallback. AI Meets the Deadline states the model's assumptions and separates synthetic traces from measured results.

Technical leadership & research practice

• Engineering Leadership

  Direction, standards, teams

  I lead Avion's enterprise AI strategy: the adoption roadmap and the reference architecture behind it. Before that I built mission-critical UAV software at STM and led the engineering team that shipped Havelsan's data-leakage-prevention product.

• Research Engineering

  From a question to an experiment

  Hands-on research in proof-of-learning and model watermarking, backed by twelve years building software in security, distributed systems and simulation. I use that engineering experience to turn research questions into implementations that can be inspected and tested.

• Security, then ML Security

  Thinking like the attacker

  Seven years building security products (Comodo's secure web gateway and patch manager, Havelsan's DLP) before turning to ML security. A model earns trust by surviving contact with an adversary, not by scoring well on a clean test set.

• Research into Practice

  Evidence a product team can use

  I turn a research question into something a team can judge: reproducible experiments, working prototypes, and explainers for non-specialists, like the films in the Research Lab.

Experience

1. 2023–now Senior Software Engineer, Avion Full Flight Simulators. Real-time simulation platforms and cloud-native infrastructure for Level D flight simulators. Since 2026 also the company's AI adoption roadmap and enterprise reference architecture, the platform presenting it, and engineering-knowledge retrieval prototypes.
2. 2021–2025 Ph.D. in Electrical Engineering & Computer Science, Embry-Riddle Aeronautical University (Graduate Research Assistant). Dissertation: Enhancing Proof-of-Learning Security Against Spoofing Attacks Using Model Watermarking, guided by Dr. Kenji Yoshigoe (Committee Chair) and IEEE Fellow Dr. Houbing Song. Three first-author IEEE Access papers, including SecurePoL (December 2025) with Dr. Yoshigoe after graduation. Dissertation · Verify diploma.
3. 2020–2021 Software Team Lead, Havelsan. Led a 14-engineer team delivering the Havelsan DLP data leakage prevention product for defence and government clients.
4. 2019–2020 Expert Software Engineer, STM Defence Technologies. Mission-control and ground-control systems for Kargu, STM's rotary-wing loitering munition in service with the Turkish Armed Forces, and the Togan reconnaissance UAV. Hard-real-time and safety-critical, where a missed deadline is a mission failure.
5. 2014–2019 Expert Software Engineer, Comodo Cybersecurity. The Comodo Dome Secure Web Gateway, the enterprise Patch Manager and the Chromium-based Dragon browser, leading the design and development of Windows services and applications in C++.
6. 2010–2014 B.Sc. in Computer Engineering, Middle East Technical University (METU). Turkey's most selective computer engineering program (English-medium), entered among roughly the top 1,000 of more than 2 million national candidates. Graduated with 1st prize at TÜBİTAK's national project competition and 2nd prize at the METU CENG graduation Demo Day.