Abstract: Understanding the inner workings of human brains, and their connections to both neurological disorders and normal development, is one of the most intriguing scientific questions. Advances in neuroscience have been greatly facilitated by various neuroimaging technologies, including anatomical magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), electroencephalography (EEG), diffusion tensor imaging, positron emission tomography (PET), among many others. The sheer size and complexity of medical imaging data, however, present significant challenges and call for continual development of new statistical methodologies. In this talk, I will provide an overview of a range of neuroimaging topics our group has been investigating, including imaging tensor analysis, brain connectivity network analysis, multimodality analysis, and imaging causal analysis. I will also illustrate with a number of specific case studies.
About the speaker: Dr. Li received BE in Electrical Engineering from Zhejiang University, China, in 1998, and PhD in Statistics from the University of Minnesota in 2003. He then worked as a Postdoctoral Researcher at School of Medicine, University of California, Davis. He joined the Department of Statistics, North Carolina State University, in 2005, as an Assistant Professor, and was promoted to Associate Professor with tenure in 2011. He was a visiting faculty at the Department of Statistics, Stanford University and Yahoo Research Labs from 2011 to 2013. He joined the Department of Biostatistics and Epidemiology, University of California, Berkeley, as an Associate Professor with tenure in 2014, and was promoted to Full Professor in 2018. He is a Fellow of the American Statistical Association (ASA), a Fellow of the Institute of Mathematical Statistics (IMS), and an Elected Member of the International Statistical Institute (ISI). He is the Editor-in-Chief of the Annals of Applied Statistics for 2025–2027.
.png){kind=logo}
