Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.


Big, multidimensional data such as brain/heart MRI is expected to transform healthcare. However, such data poses great challenges, particularly the need for interpretation and very high dimensionality accompanied by a relatively small sample size. Deep learning models are powerful but inadequate to address these problems, due to their opaque and data-hungry nature. This talk will present tensor-based machine learning models for extracting/selecting compact, interpretable features directly from tensor representations of multidimensional data. I will show their applications in prediction and interpretation of brain fMRI for neural decoding and cardiac MRI for disease diagnosis. Finally, I will discuss some ongoing and future research works on interpretable machine learning, transfer learning, and network embedding.