Group Picture


Alexander Ecker
Professor of Data Science / Principal Investigator

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: 2.137

Marita Schwahn
Team assistant

+49 551 39 21160
Room: 2.140

Timo Lüddecke
Postdoc / Project Leader

I am interested in teaching computers to understand visual scenes and applying computer vision systems to real- world problems. Particularly, my research focuses on the interpretation and anticipation of actions by learning powerful visual (or multimodal) representations. My goal is to enable robotic systems to act autonomously - so I can watch them while they do my work.
Room: 2.132

Martin Ritzert

Coming from graph learning and learning theory, I am interested in ways to use machine learning techniques to solve actual scientific questions in physics and biology. Furthermore, I like to think about ways in which we can use deep learning to solve or approximate NP hard problems such as graph coloring.

Sharmita Dey

My research interests lie in the field of deep learning, specifically in the areas of deep learning-based multi-object tracking, tracking in the wild, and self-supervised learning. Multi-object tracking (MOT) is a challenging problem in computer vision that involves identifying and tracking multiple objects in a scene over time. The use of deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), has shown great promise in addressing this problem. However, there are still many open research questions, such as how to effectively handle occlusion and how to improve the robustness of tracking in complex, real-world scenarios. Additionally, I am interested in exploring self-supervised learning methods, which can learn from large amounts of unlabeled data, to improve the performance of MOT systems.

Jan Schneider
PhD student

Point clouds of forests are a dense and information rich data structure but difficult to annotate. I am working on methods to apply self supervised learning techniques to point clouds. Self supervised learning could drastically reduce the need for labeled data and enable training for various purposes.

Ayush Paliwal
PhD Student

Coming from theoretical physics, I like to look for symmetries and invariant quantities in systems. If the problem at hand, the system to understand, has too many degrees of freedom, finding such symmetries and invariant quantities can help simplify the problem and give us a better understanding of it. While training deep learning models, I like to look for symmetries and invariant quantities in the dataset, try to have a representation of it on some manifold, and consider corresponding geometry while selecting a neural network to attack the problem. Currently, I am working on the inverse problem of holography reconstruction. The aim is to reconstruct the position, size and shape of cloud particles.
Room: 2.125

Polina Turishcheva
PhD Student

I am interested to understand how brain activity develops with time and how previous sensory input influences the processing of the current stimulus. I work with calcium data from a mouse brain. Also, I believe that deep learning should not only produce good scores but also be interpretable and have biological meaning. I hope to find such interpretable models during my Ph.D.
Room: 2.127

Richard Vogg
PhD Student

I work with computer vision techniques to help researchers who study social relationships of monkeys in the wild. Watching hours of video material can be tiring and a source for mistakes. I want to automate and improve this process by training deep learning algorithms to understand and analyze what is happening in the videos.
Room: 2.126

Laura Hansel
PhD Student

I am interested in finding answers for questions about how we acquire and process visual information, and how we use this information to learn. My goal is to understand how visual perception, information processing and learning works and to use this knowledge to build intelligent systems. I am working at the intersection of computer science and neuroscience by developing methods for analyzing the early visual system of the brain.
Room: 2.127

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: 2.127

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.

Michaela Vystrčilová
PhD Student

The visual system does not act as a mere camera. As early as in the retina, the brain starts to interpret the information it receives in order to build the perception of visual reality we experience. My goal is to shed more light on this process. Taking advantage of recent deep learning methods, I work on building retinal models that enable us to gain more insight into processing in the earliest stages of the visual system.
Room: 2.126

Maxim Titov
Master Student

Artificial neural networks were originally inspired by biology. I find the reverse way of using this technology to gain a better understanding of the brain, a fascinating idea. Here in the Neural Data Science group, I am working on incorporating the correlation of neurons into a model that predicts neural responses in the visual cortex.

Arne Barth
Master student

It seems like computer vision will be integrated into more and more areas in the future. This also applies to the manufacturing industry for the automation of many different processes. That's why I'm writing my master thesis in the field of visual quality control of industrial products in cooperation with the company Maddox AI. In particular, I focus on the knowledge transfer from multiple data sources.

Lisa Joosten
Student Assistant

I am working with image and video labeling software to create annotations in monkey videos. These are used for supervised training.

Sebastian Troue
Master student

In cooperation with the Max Planck Institute for Dynamics and Self-Organization I am working on my physics master thesis. The aim of my work is to utilize deep learning approaches to evaluate measurements on physical systems. More precisely, the focus lies on the reconstruction of particle positions in clouds from recorded digital holograms. In the end, a significant reduction in the computational cost might be achieved, making digital holography a more affordable technique.


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.

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.

Ali Doosthosseini
Master student

I'm working on a graph-based neural network approach to learn and predict tree characteristics from graph representations of their skeletons. A similar method has been used to classify brain cells in mice, and trees look the same as far as computers are concerned.

Anton Winderlich
Master student

A major argument for self-supervised learning is that it does not require reliance on expensive labeled data. I investigate contrastive representation learning by generating a synthetic dataset with Blender and using a new data augmentation, namely perspective change, to gain deeper insight into the underlying functionalities.

Dustin Theobald
Bachelor's student

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

Fabio Seel
Master student

Computer Vision is a fascinating topic for research, as it is at the same time a tool for other researchers to perform and analyze their experiments. My project deals with tracking droplets within a cloud, in order to better understand the emergence of raindrops. Analyzing videos to estimate those trajectories is highly resource demanding, which is the main challenge I want to tackle. I'm particularly interested in finding efficient ways to track a high number of objects.

Jonathan Henrich
Master student

Advances in laser scanning technology make it possible to produce detailed point clouds of forests. For various research goals, it is important to identify individual trees in such point clouds. In my work, I tackle this task by studying supervised deep learning techniques for instance segmentation.

Kai Cohrs
Master's student

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
Bachelor's student

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
Bachelor's student

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