The Agile Research Group advances software engineering for machine learning and cybersecurity by building reliable, explainable and secure AI systems. Its research spans requirements engineering for ML (elicitation, verification/validation and traceability), formal methods for models, synthetic datasets, ontology/knowledge graph-driven data engineering, quantitative explainability for vision and speech, and LLM/RAG safety (attack surfaces, defenses and provenance). Applications include aerospace and safety-critical domains, with outputs that range from benchmarked datasets and tools to peer-reviewed publications and industry-transferable methods.
The goals of this lab include:
- Advance trustworthy AI engineering by developing methods for requirements engineering, verification/validation, dataset provenance and secure deployment of ML systems.
- Bridge AI and cybersecurity by investigating vulnerabilities in modern systems, creating defenses and applying formal methods to critical domains such as aerospace.
- Empower students at all levels, from undergraduate through Ph.D., to contribute to cutting-edge research, gain publication experience and build skills in software engineering for machine learning.
Equipment
- High-performance GPU Workstations (4 total): Each equipped with modern NVIDIA GPUs for deep learning model training, dataset generation and large-scale experiments in machine learning and cybersecurity.
- Quadcopter Drones with Cameras (2 total): Each drone operates with GPS, video and sounds capture to support experiments in cybersecurity and machine learning for uncrewed aerial systems.
- HackRF: Hardware for software-defined radio to support cybersecurity experiments on the drones.
Capacity
Researchers execute end-to-end ML engineering studies — from requirements and dataset design to formal verification, secure LLM/RAG evaluation and deployment-grade tooling — supporting multiple concurrent projects and delivering publishable results, reproducible pipelines and validated datasets/models.
Lab Director
Associate Professor and Program Coordinator for M.S. in Computer Science and M.S. in Software Engineering
- Electrical Engineering and Computer Science Dept
- College of Engineering
Related Resources
Contact Us
Dr. Omar OchoaEngineering Special Projects and Labs (M Building), Rm. 115
Daytona Beach, FL 32114