Matthias

Matthias Bethge

Publications, Email, Homepage, Phone: +49 7071 29 70862
Group Leader
Matthias Bethge is Professor for Computational Neuroscience and Machine Learning at the University of Tübingen and director of the Tübingen AI Center, a joint center between Tübingen University and MPI for Intelligent Systems that is part of the German AI strategy. He is also an Amazon scholar and co-founder of Deepart UG, and Layer7 AI GmbH, and co-initiator of the European ELLIS initiative. His main research focus is on robust vision and neural decision making with the goal to advance internal model learning with neural networks. He received the first Bernstein Prize for Computational Neuroscience in 2006 and later became director of the Bernstein Center Tübingen and vice chair of the German Bernstein network. His work on neural style transfer was among the top-ten most popular publications in 2015 among all disciplines (altmetric). He has been serving as area chair for various conferences such as NeurIPS, ICLR, Cosyne and as general chair for the Bernstein conference, and initiated the “BWKI”, a German-wide school competition for AI.
Heike

Heike König

Email, Phone: +49 7071 29 70865
Assistant
Melanie

Melanie Ertle-Palm

Email, Phone: +49 7071 29 70864
Secretary
Judith

Judith Lam

Email, Homepage, Phone: +49 7071 29 70866
Coordinator SFB1233 & BCCN Tübingen
At the Bernstein Center Tübingen, scientists from various disciplines, including theoretical and experimental neurobiology, machine learning, and medicine, collaborate in order to analyze the basis of inference processes in the brain. In particular, a main research goal is to understand the coordinated interaction of neurons during information processing.
Georg

Georg Hafner

Email
Coordinator Tübingen AI Center & ELLIS
Executive coordinator of the Tübingen AI Center (Competence Center for Machine Learning) and the European Laboratory for Learning and Intelligent Systems (ELLIS)
Caroline

Caroline Schmidt

Email, Phone: +49 7071 29 70880
Outreach Coordinator
Coordinator for outreach activities including the National Competiton for Artificial Intelligence (Bundeswettbewerb Künstliche Intelligenz) and the IT training activity at primary schools (It4Kids).
Caroline

Caroline Seidel

Publications, Email, Homepage
Training Coordinator for AI
"AI? But I have no programming skills!" Although children use artificial intelligence every day, they often think that AI is difficult and opaque. Instead, I want to show the kids how much fun AI can be! That's why I create course content that guides children step by step to acquire knowledge about AI, train their own algorithms and develop project ideas for the National Artificial Intelligence Competition (BW-KI).
Wieland

Wieland Brendel

Publications, Email, Phone: +49 7071 29 70 863
Postdoc
My research goal is to close the gap between the visual information processing in humans and machines. One of the most striking differences is the susceptibility of Deep Neural Networks (DNNs) to almost imperceptible perturbations of their inputs. Getting machines closer to humans will require fundamentally new concepts to learn causal models of the world. My work aims to quantify the robustness of DNNs, to identify the causes for their susceptibility and to devise solutions by drawing inspiration from Neuroscience and Computer Vision.
Matthias

Matthias Kümmerer

Publications, Email, Phone: +49 7071 29 70585
Postdoc
Learning what properties of an image are associated with human gaze placement is important both for understanding how biological systems explore the environment and for computer vision applications. Recent advances in deep learning for the first time enable us to explain a significant portion of the information expressed in the spatial fixation structure. My interest is twofold: I want to create better models for predicting human fixations in different tasks and on the other hand make use of these models to increase our understanding of how humans perform this task from a neuroscientific and psychophysical standpoint.
Judy

Judy Borowski

Publications, Email, Phone: +49 7071 29 70582
Graduate Student
I aim to improve understanding visual perception by connecting state-of-the-art object recognition algorithms and the human visual system. Currently, I focus on how to adequately compare these two fundamentally different systems and on how much more understandable humans find convolutional neural networks thanks to interpretability methods. Besides research, I am enthusiastic about outreach and teaching programming (for example with the project IT4Kids).
Max

Max Burg

Publications, Email, Phone: +49 7071 29 70874
Graduate 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

Santiago Cadena

Publications, Email, Homepage, Phone: +49 7071 29 70876
Graduate 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.
Sebastian

Sebastian Dziadzio

Publications, Email, Homepage
Graduate Student
Christina

Christina Funke

Publications, Email, Phone: +49 7071 29 70874
Graduate Student
I analyse and compare biological and artificial neural networks to learn about the mechanisms allowing for robust and efficient visual processing.
Andreas

Andreas Hochlehnert

Publications, Email
Graduate Student
Claudio

Claudio Michaelis

Publications, Email, Phone: +49 7071 29 70877
Graduate 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.
Ori

Ori Press

Publications, Email, Homepage
Graduate Student
I’m interested in unsupervised, elegant models that can represent the world in a meaningful way. In addition, I try to take existing models and make them work using less parameters, data, and supervision.
Patrik

Patrik Reizinger

Publications, Email
Graduate Student
Evgenia

Evgenia Rusak

Publications, Email, Phone: +49 7071 29 70873
Graduate Student
To enable a future where autonomous cars can replace human drivers, we have to ensure that the autonomous agents make the right decisions at all times. In particular, bad weather scenarios currently pose a big problem for important tasks such as object detection and scene understanding. In my PhD, I work on improving the robustness of Deep Neural Nets to natural distortions such as rain or snow.
Steffen

Steffen Schneider

Publications, Email, Homepage
Graduate Student
My goal is to build machine learning models capable of approaching the performance of biological brains in terms of data-efficiency and robustness to perturbations and changes in their environment. Drawing inspiration from adaptation behavior of biological systems, I study methods for domain adaptation, transfer learning and semi-supervised learning.
Yash

Yash Sharma

Publications, Email, Homepage, Phone: +49 7071 29 70878
Graduate Student
I aim to reduce the reliance upon data and compute by enabling agents to capture the causal factors of complex input through the induction of stronger priors.
Matthias

Matthias Tangemann

Publications, Email, Phone: +49 7071 29 70585
Graduate Student
I am interested in temporal aspects of vision and unsupervised learning. The capabilities of the human visual system originate from a dynamic environment and are largely learned without supervision. I would like to explore how temporal information affects computer vision tasks as gaze prediction, segmentation or scene understanding and how it can be used to learn useful representations of our world without supervision.
Ivan

Ivan Ustyuzhaninov

Publications, Email, Phone: +49 7071 29 70873
Graduate Student
Marissa

Marissa Weis

Publications, Email, Phone: +49 7071 29 70877
Graduate Student
Roland

Roland Zimmermann

Publications, Email, Homepage, Phone: +49 7071 29 70875
Graduate Student
Rebecca

Rebecca Wanner

Publications, Email
Master Student
Elif

Elif Akata

Email
Research Assistant
Alice

Alice Rasp

Email
Research Assistant
University of Tuebingen BCCN CIN MPI