Research
Research
Exploring intelligent systems that connect humans, machines, data and artificial intelligence.
Academic Focus
Research Focus
Artificial Intelligence & Machine Learning
Deep learning, neural networks, CNN, LSTM, Transformers, reinforcement learning and computer vision, applied to robotics and signal decoding.
Brain-Computer Interfaces
EEG acquisition and decoding, signal processing and adaptive, learning-driven BCI systems for robotic and assistive control.
Intelligent Robotics
AI-driven industrial and collaborative robots, with perception-driven, adaptive behaviour in unstructured environments.
Autonomous Systems
Motion planning, navigation, SLAM and robot perception underpinning autonomous mobile robots and industrial robotic arms.
Human-Robot Interaction
Interfaces between humans, AI systems and robots — including shared control and safety-aware collaboration.
Applied Engineering
Engineering Capabilities
Applied engineering skills built over 8+ years in industry — supporting and extending the research above.
Robotics Engineering
Programming industrial robotic arms, collaborative robots and autonomous mobile robots on ROS 2 — commissioning, testing and production handover, not just simulation.
AI/ML Systems
Integrating trained models into robotic decision-making pipelines — perception-driven task selection and adaptive behaviour in production environments.
Industrial Automation
PLC (Siemens SIMATIC, Allen-Bradley), SCADA, HMI, instrumentation and control, predictive maintenance and IIoT across safety-critical industrial environments.
AI Automation
LLMs, AI agents, prompt engineering and retrieval-augmented generation — with n8n as one automation and orchestration tool among others.
Data Engineering
Python-based data processing with Pandas and NumPy, and API integration supporting AI and robotics systems — a growing technical direction.
Data → Processing → Pipeline → Intelligence → Application
Industrial Systems → AI
Featured Research
Hybrid-Adaptive Brain-Computer Interface
Hybrid Adaptive Brain-Computer Interface Using Artificial Intelligence for Controlling Industrial Robots and Medical Assistive Devices
M.Sc. thesis (JAMK University of Applied Sciences) engineering a hybrid adaptive Brain-Computer Interface that decodes real-time EEG with deep learning to control industrial robotic manipulators and medical assistive devices.