Research
Research
Exploring intelligent systems that connect humans, machines, data and artificial intelligence.
Academic Focus
Research Focus
Artificial Intelligence and 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.