Currently a visiting PhD researcher at ETH Zürich, Integrated Systems Laboratory (D-ITET), Mar.–Jun. 2026. Expected thesis defense in mid-2026.
About Me
I am a Ph.D. candidate at the University of Oulu, working under the supervision of Dr. Panos Kostakos and Dr. Lauri Lovén. My research focuses on the intersection of cybersecurity and artificial intelligence, with specific expertise in Federated Learning, Intrusion Detection Systems, Generative AI, LLM-powered security automation, and Threat Intelligence.
I have 5+ years of hands-on experience building and deploying production-grade AI and Generative AI systems, including LLM APIs, RAG pipelines, and multi-agent architectures applied to complex, real-world security domains. My work contributes to securing critical infrastructure and modern networks (5G/6G) by developing decentralized, privacy-preserving security mechanisms.
Since March 2026, I am conducting a funded research visit at ETH Zürich, Integrated Systems Laboratory (D-ITET), focusing on efficient and secure AI systems.
Education
Doctor of Science (Tech) in Computer Science & Engineering
Focus: Cybersecurity, Federated Learning, GenAI, and Intrusion Detection Systems. Expected completion: mid-2026.
Master of Science in Software Engineering
Bachelor of Science in Software Engineering
Publications
30+ peer-reviewedChain of Simulation: A Dual-Mode Reasoning Framework for Large Language Models with Dynamic Problem Routing
Hybrid Reputation Aggregation: A Robust Defense Mechanism for Adversarial Federated Learning in 5G and Edge Network Environments
SLMFORGE: Small Language Models for Federated Feature Selection via Union Aggregation in Cybersecurity
FedDTKG: Federated Temporal Graph Learning with Adaptive Loss for Robust 5G Attack Detection under Extreme Class Imbalance
Federated Variational Autoencoders for Unsupervised Anomaly Detection in Distributed 5G Networks
A Feature-Aware Federated Learning Framework for Unsupervised Anomaly Detection in 5G Networks
Optimized Contrastive Transformer Models for Self-Supervised 5G Network Intrusion Detection
Large Language Models in the 6G-Enabled Computing Continuum: a White Paper
ChainAdversary: A Retrieval-Augmented LLM Framework for Generating Realistic Attack Scenarios and Incident Response Playbooks
Enhancing Security of Connected Medical Devices in 5G Networks using an Unsupervised Federated Learning Model
Effective Anomaly Detection in 5G Networks via Transformer-Based Models and Contrastive Learning
Advancing Security in 5G Core Networks Through Federated Time Series Modeling
Safeguarding Cyberspace: Enhancing Malicious Website Detection with PSO-Optimized XGBoost and Firefly-Based Feature Selection
White Paper: Ensuring a Secure Future — Comprehensive Insights into 6G IoT Security and Privacy
Cyber Threat Hunting Using Unsupervised Federated Learning and Adversary Emulation
DDoS Attack Detection Using Unsupervised Federated Learning for 5G Networks and Beyond
Edge Intelligence (Book Chapter)
Autonomous Federated Learning for Distributed Intrusion Detection Systems in Public Networks
A Novel Anomaly-Based Intrusion Detection Model Using PSOGWO-Optimized BP Neural Network and GA-Based Feature Selection
An Efficient Rotation Forest-Based Ensemble Approach for Predicting Severity of Parkinson's Disease
An Effective Fake News Detection Method Using WOA-xgbTree Algorithm and Content-Based Features
A Novel Scheme for Improving Accuracy of KNN Classification Algorithm Based on the New Weighting Technique and Stepwise Feature Selection
An Efficient Method for Detection of Fake Accounts on the Instagram Platform
An Effective Model for SMS Spam Detection Using Content-Based Features and Averaged Neural Network
Forecasting Shear Stress Parameters in Rectangular Channels Using New Soft Computing Methods
Method for Replica Selection in the Internet of Things Using a Hybrid Optimisation Algorithm
Research Projects
H2020 IDUNN: A Cognitive Detection System for Cybersecure Operational Technologies
EU H2020End-to-end GenAI security platform integrating OpenAI GPT models and RAG pipelines for interpretable, evidence-backed threat intelligence. Cloud-native, microservices-based anomaly detection system with Explainable AI (SHAP, LIME) deployed on Kubernetes in production. Full MLOps stack (MLflow, Kubeflow, Seldon Core). Demonstrated live to Portuguese National Cybersecurity Center, Portuguese Military Academy, and Bittium.
H2020 NESTOR: An Enhanced Pre-Frontier Intelligence Picture to Safeguard European Borders
EU H2020Developed an augmented reality mapping application for HoloLens 2, integrating Kafka technology for seamless communication platform integration. Conducted final pilot demonstrations and trained EU national authorities in AR applications for border management.
Professional Experience
Visiting PhD Researcher
- Funded research visit on efficient and secure AI systems
- Supported by 6GESS & DigiHealth Visitor Program research mobility grant (EUR 7,000)
Doctoral Researcher
- Designed and delivered production-grade AI-powered applications within EU H2020 projects (IDUNN, NESTOR)
- Built and deployed LLM-powered automation tools integrating OpenAI GPT, RAG pipelines, and multi-agent architectures
- Architected scalable cloud-native platforms (Kubernetes, Docker, Kafka, microservices)
- Coordinated development across international partner organizations in EU H2020 consortia
- Mentored 5 Master's and 6 Bachelor's students (2022–2025)
- Published 15+ peer-reviewed papers including IEEE BigData 2025
Senior Software Engineer & Web Developer
- Designed and developed web applications and internal tools, including CRM systems
- Built desktop and mobile applications tailored to business requirements
- Collaborated directly with stakeholders to define requirements and iterate on usability