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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If you are, like me, often ponder over the question of why DNN with so many parameters can generalize without severe overfitting, check this out: https://t.co/7uVIerFboE

Humans can decipher adversarial images! Our new work (out TODAY in @NatureComms) shows that people can do "theory of mind" on machines—predicting how machines will see the bizarre images that "fool" them.

Paper: https://t.co/G7KqkK0QSW

Full data & code: https://t.co/3qYTvxp7kE

Our latest work (@WinawerLab @dora_hermes). ECoG broadband and fMRI BOLD responses are remarkably consistent, and can be fit using the same image-computable model. ECoG gamma, however, is quite different, and is elicited by only specific stimulus types. https://t.co/Es6NH6S3I1


Want to train your own BigGAN on just 4-8 GPUs? Today we're proud to release BigGAN-PyTorch, a full @PyTorch reimplementation that uses gradient accumulation to get the benefits of big batches even on small hardware.


Repo joint work with @_alexandonian

A docker container, but for the mental state I was in when I thought this project was a great idea

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