Welcome to the page of Dr. Tim Christian Kietzmann. I am a Researcher at the MRC Cognition and Brain Science Unit of the University of Cambridge and a member of the Lab of Prof. Niko Kriegeskorte. I investigate principles of neural information processing using tools from machine learning and pattern recognition, 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 for latest updates.

Research Interests

Cognitive Neuroscience meets Machine Learning. My main research focus is on 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 the world around us. Questions I ask include: What are the basic computational properties and representational transformations at the different stages of processing? What temporal dynamics govern visual processing and how does experience affect them? What is the role of overt visual attention in visual perception?

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

Newsfeed rss

Christmas is coming early this year: the @nvidia GPU grant program has gifted us with a brand new titan xp!


7 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!


Deep Neural Networks In Computational Neuroscience – preprint published


We just released a huge free viewing dataset (2.7 million fixations, 949 observers)

Twitter Feed

Today's paper shows that it is possible to implement John Von Neumann's claim: "With 4 parameters I can fit an elephant, and with 5 I can make him wiggle his trunk"

Paper here: https://t.co/SvVrLuRFNy

Tired of tuning parameters of SGD or Adam for #DeepLearning? Our new optimizer (https://t.co/90hi80ghna) works much better than the best constant learning rates. Try it out: #Tensorflow code included, see https://t.co/k4YVzeqJrF

New paper!

Hummingbirds, like insects, seem to use remembered view of landmarks to pinpoint a flower's location.


Congrats! Today, @HannesMehrer (@KriegeskorteLab) presented at #RSBioCompVision. Watch this space for info on a new deep learning training set to better model the human ventral stream.

Looking forward to day 2 of #RSBioCompVision in London starring Kalanit Grill Spector, Jitendra Malik, Raul Vicente, Shimon Ullman, @tserre, @Awfidius, Simon Stringer, and @FurberSteve - what a line-up

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