[BIC-announce] FW: Open ACES lab presentation, de Grand Pre, 1pm Monday 9th August. Carlton Chu: Machine Learning for fMRI connectivity & disease prediction

[Jennifer Chew, Ms.]

Open ACES lab presentation, de Grand Pre, 1pm Monday 9th August.

Title:

Machine Learning for fMRI connectivity & disease prediction

Abstract:

In recent years, applying pattern recognition and machine learning methods to neuroimaging data has gained great popularity. In the first half of this presentation, which draws on my doctoral work, I will give brief introduction to the decoding and classification models, specifically the "kernel method", and how they are different from the conventional mass univariate statistical analysis. In the second half, I will talk about the following applications, 1. Classification of Alzheimer's diseases (AD). 2.
Prediction of clinical scores for MCI and AD patients 2. Pittsburgh fMRI decoding competition.

Biography:
Carlton Chu(Chia-Yueh CHU) was borned in Taiwan, and later immigrated to New Zealand at age 16. He graduated with a first class honours degree in Computer Systems Engineering from University of Auckland, New Zealand. He then received a Masters in Biomedical Engineering from the University of New South Wales, Australia. After working as a part time research assistance for about a year at the Neuropsychiatric centre, Prince of Wales hospital, Sydney, he obtained a PhD in Neuroimaging method from University College London, working in the statistical methods group at the prestigious Wellcome Trust Centre for Neuroimaging, creators of the famous 'SPM' program, the de-facto standard analysis tool for neuroimaging used around the world.
There he developed innovative new pattern recognition methods to automatically detect the early stages of neurodegenerative diseases such as Alzheimer's and Huntingdon's just from structural brain images. Carlton was supervised Dr. John Ashburner and Prof. Karl Friston, who are the authors of SPM.

In 2007, Carlton won the first prize in the 2nd Pittsburgh Brain Activity Interpretation Competition (PBAIC), a prestigious international competition involving the application of machine learning to the problem of classification of brain activity. He led a small research team to victory, acclaim from peers in the field, and the $10K first prize, by beating out teams of experts from many top research facilities including MIT, NIH, Berkeley, Stanford and Caltech. He is currently working as a Postdoc fellow at the section of functional imaging method, National Institute of Mental Health, NIH, and is supervised by Dr. Peter Bandettini, a pioneer of fMRI research. His current research involves applying pattern recognition methods to study functional connectivities.