Machine Learning and Deep Learning for Electroencephalography (EEG)-based Brain Signal Analysis

This session explored the rapidly evolving field of machine learning, with a particular focus on deep learning techniques applied to Electroencephalography (EEG)-based brain signal analysis. Professor Xiaodong Qu shared insights from his extensive teaching and research experience, highlighting how advanced models like transformers were utilized to analyze clinical data. With a strong emphasis on real-world applications, Dr. Qu discussed how his machine learning course - an elective in the CS master's program at GW - had led to twelve of his students publishing seven papers in top-tier conferences over the past three years. Attendees also learned about ongoing interdisciplinary collaborations with leading medical institutions like Harvard Medical School and GW Hospital, and how these partnerships were advancing the use of machine learning in healthcare. The session concluded with a Q&A where students could inquire about research opportunities, the admissions process, and the potential for hands-on projects in the master's program.