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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

Academics who want your papers read by policy folks, please dont put papers on your website on dropbox as most govts & firms block access to dropbox as part of their security procedures.

We also cant access academic journals so we rely on your pre publication website versions.

Cool! @russpoldrack just published an online version of his new statistics book: "Statistical Thinking for the 21st Century" https://t.co/EwYtdIawnV

#OSD2018 Here's the
Good Enough Practices in Scientific Computing

by @gvwilson
https://t.co/Td5bKdyHas

The ‘bus factor’ - how many people need to be hit by a bus for software to become unusable? Even for free tools used by (hundreds) of millions, as few as 5 #OSD2018 @esdalmaijer

If an eye doctor looked at a retinal photo, the chance of getting gender correct would be 50-50.
But deep learning training led to an AUC of 0.97
@pearsekeane pointed out how striking this is @JeffDean at a recent #AI @GoogleAI meeting; data:
https://t.co/3nbFj35myt @NatBME #OA

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