The human brain is one of the most complicated biological systems in the world. The brain activities measured by various signals such as electroencephalogram (EEG), electrocorticogram (ECoG), and functional magnetic resonance imaging (fMRI) provide avenues that can help understand the underlying mechanisms of the brain as well as diagnosis brain disorders and the related diseases. However, without the proper techniques to analyze the brain signals, they are of limited value. In this talk, I will discuss the challenges in brain signal analysis and emphasize the role of machine learning techniques in feature extraction and classification of EEG/ECoG signals. From an algorithmic perspective, I will present multitask learning techniques that aim to discover the common structure that is shared across the brain signals from different subjects to improve the learning performances. In addition, I will also discuss some theoretical aspects of multitask learning, and address two fundamental questions: First, compared with single-task learning, why multitask learning can succeed? Second, under what conditions multitask learning can succeed?
Boyu Wang is a postdoctoral fellow at the Department of Computer and Information Science, University of Pennsylvania, working with Eric Eaton. Previously, he was a postdoctoral fellow at Princeton Neuroscience Institute, Princeton University, working with Kenneth Norman. He has obtained a PhD in Computer Science from McGill University in 2015, advised by Joelle Pineau. His research is at the intersection of machine learning and brain signal analysis, with a focus on transfer and multitask learning. His current research also includes the theoretical aspects of transfer and multitask learning. Boyu is the recipient of Lorne Trottier Science Accelerator Fellowship from McGill University, and Scientific and Technological R&D Award from Macau Government.