I am PhD student working on a wide range of machine learning and neuroscience topics including brain connectivity, Gaussian process regression, variational Bayesian inference and deep learning.
I am an Assistant Professor of Artificial Intelligence working on combining deep learning and neural coding to systematically investigate the cognitive algorithms in vivo with neuroimaging and implement them in silico with artificial neural networks. Find out more about me at guc.lu.
My research is part of the Dutch project (NESTOR) for restoring sight to blind people. I am developing computer vision models that transform camera input into meaningful visual patterns.
My research as a PhD student is focused on applying machine learning to brain data. I am also involved in development of a Brain-Computer Interface system, which allows patients with a locked-in syndrome to communicate.
I am working on a PhD thesis about the similarities between visual representations in the human brain and in deep neural networks. I am also working on machine learning methods for decoding visual perceptions from brain data by exploiting these similarities. My academic homepage is at seeliger.space.
My research focuses on Bayesian inference, (Gaussian) graphical models, network analyses, causal inference, applied to both neuroscience (brain connectivity) as well as psychology (network psychometrics). More detail and a publication list can be found at my website.
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