Engineering × research
AI engineer. Security researcher. Inventor.
I build AI systems—and research how to make their actions traceable and accountable.
PhD Researcher at the University of Greenwich
01 / Bio
My background in mathematics and statistics led me into infrastructure, cybersecurity, and machine learning. I work across model building, AI solutions, and the systems they depend on.
Today, I’m a PhD Researcher at the University of Greenwich alongside my industry work in AI engineering. My research investigates behavior, evidence, and accountability in agentic AI.
I created Chagu and SARAD to explore these questions through protocol development, anomaly detection, and open-source code.
I bring an inventor’s mindset and practical engineering experience to research—and research depth to the systems I build.
02 / Selected work
Security through connected evidence.
Creator and inventor of Chagu, bringing together secure data transformation, anomaly detection, and blockchain-based accountability.
My independent protocol development and AI security research explores anomaly detection, provenance graphs, and blockchain accountability in distributed and agentic systems.
Finding anomalies in satellite imagery.
An open-source anomaly detection pipeline for synthetic aperture radar images, combining statistical RX detection, autoencoder reconstruction, and anomaly score fusion.
03 / Research direction
At Greenwich, my doctoral research investigates security and trust in agentic AI. Chagu provides a continuing space for prototypes, questions, and experiments.
Current research directions
How can we trace the sources, actions, and dependencies behind an agent’s behavior across system boundaries?
How can statistical and learned signals help identify unusual behavior in complex, interacting systems?
How can evidence support a proportionate response—and help us assess whether a system has recovered?
04 / Engineering practice
Research depth meets practical delivery.
Model development, evaluation, feature engineering, and anomaly detection.
Python · PyTorch · TensorFlow · scikit-learn · Polars · pandas · MLflow
LLM agents, tool integration, orchestration, evaluation, and observable workflows.
TypeScript · Temporal · LiteLLM · Langfuse · LangChain · Vertex AI
Cloud infrastructure, distributed systems, MLOps, observability, and intelligent automation.
Kubernetes · AWS · GCP · Terraform · Prometheus · Grafana · Neo4j
Selected research & engineering experience
Creator, AI Data Scientist & Researcher
Independent protocol development and AI security research involving anomaly detection, provenance graphs, and blockchain accountability.
Staff AI Engineer
AI engineering, AIOps, anomaly detection, and intelligent automation.
Senior DevOps & MLOps Engineer
Infrastructure, ML workflows, observability, and security.
Senior DevOps & MLOps Engineer
ML infrastructure, algorithm execution, and engineering automation.
Principal DevOps Software Engineer
ML integration, data pipelines, and distributed infrastructure.
05 / Ideas in public
06 / Foundations
Mathematics, statistics, business, and engineering inform how I approach research and the systems it connects to.
PhD Researcher, Computer Science
University of Greenwich · Registered researcher
AI Performance Engineering Fellowship
Nebius Academy
Nebius Agentic AI Builder
Nebius Academy · 2026
Advanced Data Science
Professional certification · Y-Data
Big Data Engineering
Professional certification · NAYA College
Specialist Diploma
Mathematics & statistics · Quantitative finance focus
MBA
Bologna
GenAI Pioneer Program
One of 200 selected participants · 2024
“There are two types of sacrifices:
Mikhail Tal · widely attributed
correct ones, and mine.”
07 / The next move
I bring an inventor’s mindset, research depth, and AI engineering expertise to ambitious international teams tackling complex challenges in AI and cybersecurity.