【Lecture】12/6(五)_Using Artificial Intelligence to Better Understand the Brain and Behavior
Graduate Seminar Course, Electrical Engineering/Computer Science Special Topic Lecture
Lecture Information:
Title: Using Artificial Intelligence to Better Understand the Brain and Behavior
Speaker: 鍾子平教授(Tzyy-Ping Jung), UC San Diego, National Yang Ming Chiao Tung University, and National Tsing Hua University
Time: Friday, December 6, 2024, 14:00–16:00
Location: Lecture Hall 4 (A307), 3rd Floor, Engineering Building
Speaker’s bio:
Tzyy-Ping Jung is the Co-Director of the Center for Advanced Neurological Engineering, Associate Director of the Swartz Center for Computational Neuroscience, and an Adjunct Professor in the Department of Bioengineering at the University of California, San Diego. He also holds adjunct professorships at National Tsing Hua University, National Yang Ming Chiao Tung University in Taiwan, Tianjin University in China, and the University of Science and Technology Beijing in China.
Dr. Jung pioneered transformative techniques for applying blind source separation to decompose multichannel EEG, MEG, ERP, and fMRI data. In recognition of his contributions to blind source separation for biomedical applications, he was elevated to IEEE Fellow in 2015. He is also a Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA). Dr. Jung’s research emphasizes the integration of cognitive science, computer science and engineering, neuroscience, bioengineering, and electrical engineering. His interdisciplinary work is highly regarded and well-cited by peers, with over 46,000 citations and an h-index of 92, according to Google Scholar.
Lecture Abstract:
Machine learning (ML) and artificial intelligence (AI) have revolutionized how we address challenges involving complex, unstructured, and unlabeled data. Historically inspired by the human brain, a compelling question remains: can AI now propel advancements in understanding human brain function and behavior? This presentation will explore (1) the AI tools necessary for scaling the collection and analysis of naturalistic neural and behavioral data and (2) practical applications of ML/AI in analyzing EEG and physiological data during real-world activities such as reading, learning, and gaming. Early findings suggest that ML and deep learning (DL) methodologies provide unprecedented insights into the intricate relationships between neural signals and behavior. These innovations deepen our understanding of the mind's capabilities and limitations while offering transformative applications in medicine, education, and cognitive monitoring.
The seminar counts as a 2-hour interdisciplinary activity. Once the interdisciplinary event registration opens, you can sign up to participate.
Lecture Information:
Title: Using Artificial Intelligence to Better Understand the Brain and Behavior
Speaker: 鍾子平教授(Tzyy-Ping Jung), UC San Diego, National Yang Ming Chiao Tung University, and National Tsing Hua University
Time: Friday, December 6, 2024, 14:00–16:00
Location: Lecture Hall 4 (A307), 3rd Floor, Engineering Building
Speaker’s bio:
Tzyy-Ping Jung is the Co-Director of the Center for Advanced Neurological Engineering, Associate Director of the Swartz Center for Computational Neuroscience, and an Adjunct Professor in the Department of Bioengineering at the University of California, San Diego. He also holds adjunct professorships at National Tsing Hua University, National Yang Ming Chiao Tung University in Taiwan, Tianjin University in China, and the University of Science and Technology Beijing in China.
Dr. Jung pioneered transformative techniques for applying blind source separation to decompose multichannel EEG, MEG, ERP, and fMRI data. In recognition of his contributions to blind source separation for biomedical applications, he was elevated to IEEE Fellow in 2015. He is also a Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA). Dr. Jung’s research emphasizes the integration of cognitive science, computer science and engineering, neuroscience, bioengineering, and electrical engineering. His interdisciplinary work is highly regarded and well-cited by peers, with over 46,000 citations and an h-index of 92, according to Google Scholar.
Lecture Abstract:
Machine learning (ML) and artificial intelligence (AI) have revolutionized how we address challenges involving complex, unstructured, and unlabeled data. Historically inspired by the human brain, a compelling question remains: can AI now propel advancements in understanding human brain function and behavior? This presentation will explore (1) the AI tools necessary for scaling the collection and analysis of naturalistic neural and behavioral data and (2) practical applications of ML/AI in analyzing EEG and physiological data during real-world activities such as reading, learning, and gaming. Early findings suggest that ML and deep learning (DL) methodologies provide unprecedented insights into the intricate relationships between neural signals and behavior. These innovations deepen our understanding of the mind's capabilities and limitations while offering transformative applications in medicine, education, and cognitive monitoring.
The seminar counts as a 2-hour interdisciplinary activity. Once the interdisciplinary event registration opens, you can sign up to participate.