Data Scientist and Machine Learning Researcher with expertise in Graph Neural Networks (GNNs), Reinforcement Learning (RL), and Brain-Computer Interface (BCI) applications. Ph.D. in Machine Learning with extensive experience in developing AI-driven solutions for EEG signal classification, economic forecasting, and causal inference. Passionate about leveraging machine learning to uncover insights in complex systems and optimise decision-making processes.
Available upon request.
EEG_GLT-Net – Developed an optimised EEG classification model using Graph Neural Networks (GNNs) and reinforcement learning. EEG_RL-Net – Applied reinforcement learning to EEG Motor Imagery classification, improving accuracy and real-time performance. Economic Graph Lottery Ticket (EGLT) – Built a GNN-based model for economic forecasting, outperforming traditional econometric models. Temporal Causal Inference in Economic Indicators – Applied Peter-Clarke algorithms to uncover causal interrelationships in economic data.