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).
Newsfeed 
I am incredibly excited about the new eLife feature #ScientistAndParent to which my family and I contributed.
https://elifesciences.org/interviews/24427a8f/tim-kietzmannChristmas is coming early this year: the @nvidia GPU grant program has gifted us with a brand new titan xp!
https://developer.nvidia.com/academic_gpu_seeding7 Questions about academic publishing
7 Questions about academic publishing - my take on the publication process, targeted at MSc students (printed ...Looking forward to joining Darwin College as a Postdoctoral Research Associate!
https://www.darwin.cam.ac.uk/Deep Neural Networks In Computational Neuroscience – preprint published
http://biorxiv.org/content/early/2017/05/04/133504Twitter Feed
Amazing. Can't wait for the Android version in my phone and tablet...
An incredibly intuitive way to view MRI slices, using the VIVE Tracker. Awesome work from @Vice_Packet39
https://t.co/atVI12vuiu
Nature just published a major feature on researchers working on bias in machine learning. Features many of us, including @geomblog @mikarv @s010n @achould @b_mittelstadt @rayidghani @dgrobinson @bowlinearl - and the @AINowInstitute work on due process https://t.co/XIbRaOU8yC
#cloudme @tweetcloudbot
Oh wow, these deep learning libraries are so accurate these days
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