Mahmoud Alimoradi is a PhD candidate specializing in Reinforcement Learning, Adaptive Learning, Transfer Learning, and Brain-Computer Interface (BCI) technologies. His research focuses on integrating advanced machine learning techniques with brain signal analysis, such as EEG and fNIRS, to develop intelligent systems capable of real-time task detection. By leveraging adaptive and transfer learning frameworks, Mahmoud aims to enhance the efficiency and practicality of BCI systems, reducing calibration time and enabling cross-subject adaptability. His passion lies in pushing the boundaries of Reinforcement Learning to create advanced systems for neurorehabilitation, cognitive enhancement, and brain-controlled devices, striving to make transformative contributions to healthcare and human augmentation.