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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!

https://developer.nvidia.com/academic_gpu_seeding

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!

https://www.darwin.cam.ac.uk/

Deep Neural Networks In Computational Neuroscience – preprint published

http://biorxiv.org/content/early/2017/05/04/133504

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

http://www.nature.com/articles/sdata2016126
Twitter Feed

And the finally, less advice and more 'strategy', which I learned from @NKriegeskorte: Read your whole paper out loud, sentence by sentence, and see what can and should be improved. Things that seem ok while reading can be atrocious when read aloud https://t.co/n9ykojIfgR

talk videos from the amazing recent @ipam_ucla workshop on new deep learning techniques... https://t.co/iHmTsLTU00

One-shot immitation learning from noisy labeled data!

Can we meaningfully interpret the dissimilarity between fMRI activity patterns? We don’t know. But here one piece of good news: the representational geometry stays stable even when the overall activity increases fivefold. @NKriegeskorte @andpru: https://t.co/2aNcl5xjZf

Radia Perlman told the perfect story about how engineers come up with solutions before understanding the problem:

"When my son was 3, he came to me crying 'my hand! my hand!'

I started kissing it. *mwah* *mwah* *mwah* 'Tell me what happened...'

'I got pee on it.'"

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