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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

#BecauseWhy let gender limit our children’s potential? #HeForShe #YouthDay @OurWatchAus
From: https://t.co/rdIflWD0ym

Correlation ≠ Causation!! And anyway, it's not even like there's not a logical reason why if there WERE a causal relationship it couldn't go the other way (feeling worse > less likely to exercise), so frustrating! https://t.co/O5ScBvIcJg

Darwin on confirmation bias, circa 1876: pay special attention to facts which challenge your biases since they are far more likely to slip through. The level of both honesty and modesty which he displays in his autobiography is quite remarkable.

New preprint up on PsyArXiv: Real-world size is automatically encoded in preschoolers’ object representations. Evidence from kids doing a size-stroop task on ipads. With @brialong https://t.co/j4RBFBocST

A really nice blog post by @agrinh about recent progress in GANs and variational autoencoders. Gives a short overview about GANs and their problems and then dives deep into the newest methods from ICML2018. https://t.co/tc9AGiESFx

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