Delft Tensor AI Lab (DeTAIL)

Themes: Biomedical signal processing

Real-life biomedical data is often high-dimensional. Current signal processing solutions artificially segment such high-dimensional data into shorter one- or two-dimensional arrays, causing information loss by destroying correlations between these data. At the same time, advances in biomedical sensor and imaging technology – such as substantially larger recording durations of wearable sensor technology and the unprecedented increase in spatial and temporal resolution of the latest neuroimaging techniques – have led to ever increasing data sets. Tensors (multi-dimensional arrays) are the data structure of choice in artificial intelligence research to exploit the full potential of these data in a timely manner.

Within the DeTAIL Lab, we focus on both the development and application of novel low-rank tensor methods for biomedical signal processing, thereby enabling a much faster training of AI models from large datasets without any loss of accuracy.

We will exploit an as of yet unused property of real-life data; the fact that different modes of data may be correlated. Using tensor decompositions, we can find these correlations as well as compress the data, speeding up computations significantly.

Our findings will, for example, be applied to detect events, such as epileptic seizures, through the classification of multichannel time series data based on labelled training data. We also aim to reveal hidden structure, such as functional networks, in neuroimaging data.

As biomedical innovation is a defining characteristic of the TU Delft, we will develop an interfaculty elective course on AI tensor methods to satisfy the expected continual increase in demand for such knowledge.

DeTAIL is one of 8 Delft AI Labs funded in Spring 2020. In total, 24 labs will start under the AIDU programme of the TU Delft AI Initiative (TU Delft's AI, Data and Digitalisation research and education programme).

For more information, see the project homepage.