Alexander Ecker
Group Leader

I want to understand how neural systems perform visual perception. At the interface of computer vision and neuroscience, I try to understand both how biological vision works and how to teach computers to make sense of images. I use an interdisciplinary approach that combines methods from machine learning and computer vision with behavioral studies and neuronal population recordings in the brain.
+49 551 39 21272
Room: 2.123

Marita Schwahn

+49 551 39 21160

Timo Lüddecke

39 26927
Room: 2.127

Claudio Michaelis
Phd Student

Humans do not only excel at acquiring novel concepts from a single demonstration but can also readily identify or reproduce them. When shown a new object humans have no problem pointing at similar objects or drawing their outlines. My goal is to bring similar capabilities to computer vision systems.

Max Burg
Phd Student

I want to understand visual perception in the brain by leveraging novel machine learning techniques to build predictive models of neural population responses to natural images. Especially, I think it is important to combine a model’s predictive accuracy with interpretability to gain insights into mechanisms of biological computation. I am excited by the idea to implement the biologically inspired building blocks we develop into artificial neural networks.

Santiago Cadena
Phd Student

I study visual processing in the brain by building predictive models of population responses from the macaque and rodent brains to image and video sequences. I leverage on advances in machine learning and computer vision to both improve predictive power and to gain insights into the nonlinear computations of visual neurons. My goal is to be able to use these insights to enhance current computer vision methods.

Marissa Weis
Phd Student

My research goal is to understand how visual perception in the brain works and how to apply this knowledge to improve perception in artificial neural networks. I work at the intersection of computational neuroscience and machine learning. I’m especially interested in unsupervised representation learning and to understand how the notion of objects arises in visual perception in biological systems and how we can use this knowledge to improve scene understanding of artificial neural networks.
Room: 2.126

Laura Pede
Phd Student

Room: 2.135

Dustin Theobald

Broad interest in data science topics. Currently writing my bachelor thesis on self-supervised learning.

Kai Cohrs

At the Neural Data Science group, I am working on models that predict neural responses from different areas of a macaque's visual system simultaneously. My goal is to learn something about the signal flow between those areas. Beyond that, my main interest lies in probabilistic and Bayesian machine learning which I would like to use to make ML more robust. I am fascinated by how these approaches allow us to quantify the uncertainty of our models helping us to make their usage safer.


Linus Wagner

During my time at the Neural Data Science Group I focused on extending a convolutional neural network for modelling neural data in mouse primary visual cortex to take into account noise correlations.

Vitus Benson

I study how we as a society can leverage both physical understanding and data-driven models for tackling complex challenges. At the Neural Data Science group I build robust deep learning models for trustworthy automated building damage assessment after natural disasters.
Neural Data Science Group
Institute of Computer Science
University of Goettingen