Welcome to the page of Dr. Tim Christian Kietzmann. I am a Researcher and Graduate Supervisor at the MRC Cognition and Brain Science Unit of the University of Cambridge (line manager Prof. Niko Kriegeskorte). I investigate principles of neural information processing using tools from machine learning and deep learning, applied to neuroimaging data recorded at high temporal (EEG/MEG) and spatial (fMRI) resolution. Feel free to contact me with any questions or paper requests, and follow me on twitter (@TimKietzmann) for latest updates.

Research Interests

Cognitive Neuroscience meets Machine Learning. My main research aim is to understand dynamic information processing in the brain. Focusing mainly on vision, I am particularly interested in understanding the cortical mechanisms that allow us to robustly extract information from noisy sensory information. I ask how the brain learns robust representations from the statistical regularities in the world. What are the underlying computational mechanisms and representational transformations? What are the computational objectives that the visual system optimises for, and how do they shape neural representations? What temporal dynamics govern information processing and how does experience affect them?

I approach these questions by combining human neuroimaging with machine learning techniques (pattern recognition, and deep neural network models).

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Twitter Feed

ICLabel: An automated electroencephalographic independent component classifier, dataset, and website https://t.co/mgyGZbVQh4

"If you only want tenure maybe you don't deserve tenure" -@NKriegeskorte at the #VSS2019 event 'How to Spend Your Time Well as a Young Researcher'


How might our brains approximate back-propagation?
Rafal Bogacz enlightens us on the many ways via his review w/ @jcrwhittington.
Plus more free energy principle talk, why he's so infatuated with FEP, how diff brain areas do diff things (novel!) & more.


Are you interested in deep learning for NLP but also concerned about the CO2 footprint of training? You should be! Excited to share our work "Energy and Policy Considerations for Deep Learning in NLP" at @ACL2019_Italy! With @ananya__g and @andrewmccallum. Preprint coming soon.

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