The department investigates a broad range of theoretical and practical aspects of modern algorithmics. We design new algorithms and algorithmic techniques, analyze their efficiency and the quality of their solutions, develop provably efficient and correct software, and package our programs in software libraries. The strength of our approach lies in the fact that we consider these aspects in unity and not in isolation.
Perceptual Computing in general and Computer Vision in particular have great potentials to change the way we interact with computers and how machines such as robots perceive the world. Over the last three decades significant progress has been made in computer vision. Today it is possible to use image information for quality control and domain specific problems such as face recognition, recovery of CAD models for well-defined objects and basic visual surveillance. Robustness of perception and vision algorithms however is a notorious problem and one of the major bottlenecks for industrial applications. At the same time there is little doubt that in the next decades small and inexpensive sensors will be developed and embedded in many devices. Our hypothesis is that the integration of multiple features and sensors facilitates robustness in environments of realistic complexity.
The Internet is a hugely successful human made artifact that has changed the society fundamentally. In becoming such a hugely successful infrastructure the usage of the Internet and, thus, the Internet has and continues to change as my research has highlighted.
During the last few decades computer graphics ﬁrmly established itself as a core discipline within computer science. New and emerging technologies such as digital media, social networks, digital television, digital photography and the rapid development of new sensing devices, telecommunication and telepresence, virtual and augmented reality further indicate its potential and pose new challenges in the years to come.
We are witnessing an explosion of digital information. The Internet provides a seemingly endless amount of data that is constantly growing. From a technical viewpoint, this poses tremendous challenges regarding the intelligent organization, semantic search, and deep analysis of the data. This concerns not just the data and knowledge on the Web, but also in databases, social media, digital libraries, and scientific data repositories.
The Visual Computing and Artificial Intelligence Department investigates foundational research problems at the intersection of Computer Graphics, Computer Vision and Artificial Intelligence. It is our long term vision to develop entirely new ways to capture, represent, synthesize and simulate models of the real world at highest detail, robustness, and efficiency. To achieve this long term goal, we develop new concepts that rethink and unite established approaches from Computer Graphics and Computer Vision with concepts from Artificial Intelligence, in particular Machine Learning.
|Molecular Networks in Medical Bioinformatics (former group)
|Dr. Mario Albrecht
|Computational Systems Biology (former group)
|Dr. Jan Baumbach
|Dr. Christoph Bock and Prof. Dr. Thomas Lengauer
|Structural Bioinformatics of Protein Interactions
|Dr. Olga Kalinina
|Statistical Learning in Computational Biology
|Dr. Nico Pfeifer
|High-Throughput Genomics and Systems Biology
(Excellence Cluster on Multimodal Computing and Interaction)
|Dr. Marcel Schulz
We cover both method development and applications, the latter notably in pharmaceutics and medicine. On the methodical side we perform research on the analysis of biological sequences (including recombination, viral evolution and computational epigenetics), analysis and prediction of protein structure and function, analysis of intermolecular interactions and interaction networks, gene and protein expression patterns, computational drug screening and drug design.On the application side, we focus on the diseases HIV/AIDS, where we analyse viral drug resistance patterns as well as variants of viral entry into the host cell, HCV/Hepatitis C, where we contribute to uncovering the molecular basis of host-pathogen interactions and neurodegenerative and autoimmune diseases, where we study underlying protein interaction networks.
The research of this group concentrates on automated deduction in (subsets of) first-order logic. On the theoretical side, the work is focused on the development, analysis, and combination of logical calculi. Practically, the group is concerned with the implementation of powerful automated theorem provers and other deductive systems and their application. One central application area is computer-aided verification of hardware and software.
Computer networks have become the “new electricity” to facilitate rich cloud services. The network infrastructure is crucial to the performance of cloud computing. The network and cloud systems group was founded in 2020. The research focus is on building high-performance and cost-efficient network and systems for cloud applications. We follow a cross-layer approach and cover broad topics for optimizing the cloud stack, including novel hardware, network protocols, software systems, and cloud applications.
Multimodal Language Processing
Language is fundamental to how we humans communicate with one another and exchange knowledge. We have seen immense progress in automatic language comprehension and language generation in the last few years. A central goal of the Research Group on Multimodal Language Processing lies in complementing language processing with other modalities for better grounding, deeper understanding and more naturalistic interaction.