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.
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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.
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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.
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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.
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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.

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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.

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

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.