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_seedingTwitter Feed
Teachers! Parents!
It is legal and perfectly welcome to email scientists asking for a PDF of their paper.
Until all papers are free, please RT to make this more widely known. And fyi, scientists don't get the $$ from sales of papers so all it costs us is 1 minute of time! https://t.co/UJkasG33Od
Deep CNNs use texture more for classification of animals, but shape for objects. Presumably because objects are shown on more plain backgrounds in the training set? https://t.co/9CETXPUyTZ
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Human visual cortex is organized along two genetically opposed hierarchical gradients with unique developmental and evolutionary origins https://t.co/6Lddlvtu76 #biorxiv_neursci
My newest paper with Zonglei Zhen and @neuroKevin. We make the fun observation that the visual processing hierarchy in human cortex is organized along two opposed genetic expression gradients. These gradients develop differently in the womb and distinguish us from monkeys! https://t.co/9l2OPss4dY
