Flight Simulators Are Becoming the AI Proving Ground
What if flight simulators are the AI proving ground nobody is talking about? Three years inside Avion.
Field Notes
Field notes on ML security, distributed systems, and high-reliability software, written for researchers and engineering leaders. For longer-form parables, see the Essays; for interactive demos, visit the Lab.
How to Recognize Advice That Actually Helps
Entrepreneurs, researchers, and engineers live in a torrent of guidance. Podcasts, newsletters, and mentors offer conflicting prescriptions, each delivered w…
The Real Test for Making Something People Want
Scaling conversations dominate startup culture, yet the first question any product must answer is painfully small: will even one person use it when given the…
Why Great Engineering Teams Run on Curiosity
Working on simulator software, defensive cybersecurity platforms, and doctoral research has taught me that durable progress rarely starts with a perfect road…
The Myth of the Quick Fix
Every engineer knows the temptation of the “five-minute hack.” A bug appears, the schedule is tight, and a clever shortcut promises to save the day. Weeks la…
Documentation as the First User Interface
Before a developer ever sees your landing page, they might encounter your README. For many technical products, documentation is the first user interface. It …
Hiring Generalists: The Small Team’s Secret Weapon
Why early-stage engineering teams should hire for breadth and learning rate, and how generalists become the connective tissue that later scales into specialists.
The Invisible Cost of Shiny Tools
Engineering teams love new tools. The promise of faster development, cleaner abstractions, or a more elegant stack is hard to resist. Yet every adoption deci…
Why Early Beta Feedback Matters More Than Launch Day
Product launches often steal the spotlight, but the feedback gathered during early beta testing quietly determines whether launch day is a victory lap or a s…
Proof of Learning: Building Trust in Future Machine Learning
Proof of Learning (PoL) verifies that a model was genuinely trained on claimed data by providing verifiable evidence of the training process. I first felt th…
Model Watermarking and the Future of Trustworthy AI
Model watermarking embeds identifiable patterns into a model’s parameters or outputs so that ownership can be demonstrated without access to the original tra…
ML-Enhanced Blockchain: Toward Intelligent, Adaptive Ledgers
Machine learning (ML) is increasingly used to make blockchain networks more secure, efficient, and user-friendly. When I co-authored our survey on blockchain…
Blockchain-Enhanced Machine Learning: Linking Trust, Data, and Incentives
How blockchain’s immutable ledgers and decentralized governance address auditability, provenance, and incentive-alignment challenges in machine-learning pipelines.
Autonomous Flight Systems: Engineering Mission-Critical Autonomy from Tactical UAVs to Verifiable AI
Architectural lessons from engineering mission-control and ground-control software for Kargu and Togan UAVs: hard real-time constraints, deterministic safety envelopes, and the boundary between learned models and physical actuators.
Adversarial Example Attacks Against Proof-of-Learning: A Reproduced Threat Reference for Every SecurePoL Defense
The runnable reference implementation of four spoofing attacks against plain Proof-of-Learning verification, used as the calibration threat model for every SecurePoL defense published in IEEE Access 2024, 2025, and the 2025 Embry-Riddle doctoral dissertation.
SecurePoL with Watermarking: The Jupyter Notebook Suite Behind Three IEEE Access Papers and a European FFS Deployment
The official repository of Jupyter notebooks implementing the three watermarking strategies compared across the SecurePoL line of work, with the measured spoofing-resistance numbers reported in the IEEE Access 2024 and 2025 papers and the 2025 Embry-Riddle doctoral dissertation.
Clinician-Calibrated EKG Measurement with PyQt5: The Deterministic Baseline for a Clinical-AI Annotation Pipeline
A PyQt5 desktop tool for clinician-validated EKG interval measurement, used to generate 312 signed, verified labels that now form the ground-truth baseline for a 2026 Turkish-hospital AI-assisted landmark-detection pilot. No neural components; purely deterministic measurement logic for regulatory traceability.
Presenting at ICISSP 2021: Why Low-Resource, Agglutinative Languages Break Standard Security Detectors, and What We Measured
The in-person talk at the International Conference on Information Systems Security and Privacy 2021, co-authored with Prof. Cengiz Acartürk, covering the agglutinative-morphology failure mode and the operational false-positive budget that made the Turkish cyber-event detector usable.
Secure Proxy on Cloud: A 2016 Architecture for Cloud-Native Security Policy Enforcement, and What Became of It
The 2016 ICESC conference paper and demonstration video documenting a cloud-native secure proxy architecture, the design patterns that survived five years into the Comodo Secure Web Gateway product line, and the lessons that now shape Avion Level-D simulator policy planes.