Scientific contact persons: Prof. Gerti Kappel, E-mail: gerti@big.tuwien.ac.at

Webpage of your present research group: https://www.big.tuwien.ac.at, öffnet eine externe URL in einem neuen Fenster

The Business Informatics Group (BIG) is a research unit of the Institute of Information Systems Engineering at TU Wien Informatics. The research unit focuses on business informatics that integrates theory and methods of information systems and computer science. In particular, BIG works on those information technology aspects that have a significant effect on the way organizations do their business. The current research areas of BIG cover model-driven engineering, data engineering, process engineering, enterprise engineering, and industrial engineering.

The new VRG group should complement BIG by means of a domain-specific (e.g., Cyber-physical Systems, Internet of Things), application-specific (e.g., Digital Twin, Knowledge Graphs), or technology-specific (e.g., Artificial Intelligence, Semantic Technologies) focus of one of the core BIG research areas. Particularly, we are welcoming applications that combine model-driven engineering with advanced processing (e.g., simulations, transformations, generators) and/or advanced visualization techniques (e.g., multi-paradigm modeling, hybrid modeling, collaborative modeling).

Scientific contact persons: Prof. Thomas Gärtner, E-mail: thomas.gaertner@tuwien.ac.at

Webpage of your present research group: https://ml-tuw.github.io/, öffnet eine externe URL in einem neuen Fenster

Online social networks have become ubiquitous parts of modern societies.

While they enable us to interact with people from around the world, they are also being misused to foster polarization and disagreement. At the core of online social networks are timeline algorithms, based on data mining and machine learning, which influence who we interact with and which information we obtain. However, the impact of these algorithms on the real-world is still not well-understood, especially from a foundational, mathematical point of view.

In this research group, we build a foundational understanding of online social networks and their underlying algorithms. We aim to understand how timeline algorithms influence the polarization in online social networks, how malicous actors can sow disagreement in societies and how such attacks can be mitigated, as well as how communities and echo chambers can be detected. To study polarization and disagreement, we combine analysis tools from theoretical computer science with opinion formation models from sociology. In community detection, our goal will be to generalize classic results which only consider simple friendship relationships, by taking into account more complex phenomena, such as conflicts between different groups and temporal information.

The goal of this group is to have highest international visibility and to publish at the finest venues in computer science. In the medium to long term, we also wish to contribute to the effective regulation of timeline algorithms and their impact on modern societies.

*Research Unit Machine Learning* (194-06):

Our main research interests are efficient and effective machine learning and data mining algorithms. Machine learning considers the problem of extracting useful functional or probabilistic dependencies from a sample of data. Such dependencies can then, for instance, be used to predict properties of partially observed data. Data mining is often used in a broader sense and includes several different computational problems, for instance, finding regularities or patterns in data. By efficiency we mean on the one hand the classical computational complexity of decision, enumeration, etc. problems but on the other hand also a satisfactory response time that allows for effectiveness. By effectiveness we mean how well an algorithm helps to solve a real world problem.

Our research aims at narrowing the gap between theoretically well-understood and practically relevant machine learning.

Research questions concern for instance:

  -   learning with non-conventional data, i.e., data that has no inherent representation in a table or Euclidean space

  -   incorporation of invariances as well as expert domain knowledge in learning algorithms

  -   computational, sample, query, and communication complexity of learning algorithms

  -   constructive machine learning scenarios such as structured output prediction

  -   learning with small labelled data sets and large unlabelled data sets

  -   adversarial learning with mistake and/or regret bounds

  -   parallelisation/distribution of learning algorithms

  -   approximation of learning algorithms

  -   scalability of learning algorithms

  -   reliability of learning algorithms

  -   extreme learning

  -   ...

To demonstrate the practical effectiveness of novel learning algorithms, we apply them in Chemistry, Material Science, Electrical Engineering, Computer Games, Humanities, etc.

Scientific contact persons: Prof. Eduard Gröller, E-mail: eduard.groeller@tuwien.ac.at

Webpage of your present research group: https://www.cg.tuwien.ac.at/, öffnet eine externe URL in einem neuen Fenster

Our group performs extensive fundamental and applied research in computer graphics. Our areas of expertise are modelling and rendering for computer graphics, visualization, visual computing, virtual environments, and color. Besides our research projects, we specialize in consulting and technology transfer as well as computer graphics related education on both undergraduate and graduate levels. The VRG leader would complement our existing expertise by covering an important visual computing area like medical or illustrative visualization.

 

Scientific contact persons: Prof. Holger Arthaber, Email: holger.arthaber@tuwien.ac.at

Webpage of your present research group: https://www.tuwien.at/en/etit/e354/microwave

The Microwave Engineering Group covers research areas from fundamental to applied research. This includes, for example, nonlinear device modeling, load/pull-based design/modeling, antenna measurements, EMC, robust communication systems, microwave sensing, SDRs, and indoor localization. As successful research on microwave topics increasingly requires a holistic view of the entire RF system, ranging from antenna to low-level signal processing, has moved into the group’s focus. To complement the group’s expertise on microwave devices and systems, a new research group shall be established to research disruptive approaches for future antennas, combining materials, manufacturing techniques, active circuits, and signal processing.
The Vienna Research Group will be embedded in the Microwave Engineering Group the Institute of Electrodynamics, Microwave and Circuit Engineering. With a state-of-the-art microwave lab (currently 70 GHz), access to 3D-EM simulators, a nearfield/farfield antenna measurement system (currently 40 GHz), and well-established links to other groups working on antenna signal processing, the Vienna Research Group can be perfectly embedded into the existing group and the faculty.

Scientific contact persons: Thomas Lukasiewicz, Email:  thomas.lukasiewicz@tuwien.ac.at

Webpage of your present research group: https://www.cs.ox.ac.uk/people/publications/date/Thomas.Lukasiewicz.html, öffnet eine externe URL in einem neuen Fenster

The last years have been landmark years in artificial intelligence (AI); computers are more intelligent and learning faster than ever. Deep learning, an advanced machine learning technology based on neural networks, is achieving revolutionary results in tasks such as speech recognition/generation, computer vision, language-related tasks, game playing, and self-driving vehicles. AI has the potential to dramatically change huge parts of the economy and society for the better. However, despite this impressive progress, deep-learning-based technologies still do have well-known limitations and drawbacks. One major drawback is that deep neural networks are generally "black boxes", lacking interpretability and explainability. However, explaining decisions, processes, and services in a human-intelligible way is crucial for transparency and accountability, as also highlighted by the EU General Data Protection Regulation, dated 25 May 2018. Among others, this regulation enforces the right to explanations for users impacted by algorithmic decisions, e.g., in healthcare, law, or finance. Deep-learning technologies may also be biased, and lack robustness. As an example of bias problems, Amazon has recently stopped using an AI recruiting tool that had shown bias against women. In nearly all application domains of deep learning (ranging from cancer detection to self-driving cars), we would like to be sure that the applied deep learning technologies are fair and robust. The Vienna Research Group aims to identify and address open questions in explainable AI in order to build explainable, fair, and robust AI systems. The main areas of interest are natural language processing and vision-language understanding, with a special focus on applications in healthcare and the legal domain.

Scientific contact persons: Prof. Joachim Schöberl, Email: joachim.schoeberl@tuwien.ac.at

Webpage of your present research group: https://www.asc.tuwien.ac.at/?id=contact&name=schöberl, öffnet eine externe URL in einem neuen Fenster

The research of the workgroup on Computational Mathematics in Engineering led by Prof. Joachim Schöberl focuses on numerical methods for the computer simulation of partial differential equations appearing in various fields within science and engineering. 

The new VRG will be concerned with the development and implementation of efficient and accurate numerical methods for high frequency electromagnetics, which is a basis for modern quantum information and communication technologies. The developed tool shall be able to simulate, for example, microcavities, sub-wavelength structures, plasmonic resonators, Bragg resonators, dielectric resonators, and photonic crystals.

Scientific contact person: Prof. Matteo Maffei, Email matteo.maffei@tuwien.ac.at

Webpage of your present research group: https://secpriv.wien/, öffnet eine externe URL in einem neuen Fenster

Security and Privacy of Information and Communication Technologies. 

The new research group will be embedded in the Security and Privacy research unit.

The topics of interest include but are not limited to 

- intersection between Machine Learning and Security and Privacy

- usable security 

- formal methods for security

- system and network security

- applied cryptography

Scientific contact person: Thomas Lukasiewicz <thomas.lukasiewicz@tuwien.ac.at>

Webpage of your present research group: https://informatics.tuwien.ac.at/orgs/e192-07, öffnet eine externe URL in einem neuen Fenster

Intelligent medical image segmentation is one of the most important tasks of computer-aided clinical diagnosis and treatment. It aims to improves the efficiency and accuracy of clinical disease screening, diagnosis, and treatment by automatically delineating the precise boundaries of organs, lesions, and other interesting objects in medical images. The current intelligent medical image segmentation system is mainly based on deep learning computer vision technologies, so a large number of medical image data with accurate segmentation masks (i.e., annotations) are needed to properly train the deep models. However, obtaining high-quality segmentation masks of medical images is very challenging in clinical practices: First, delineating the pixel-wise segmentation masks require high-level expertise, so it can only be done by doctors with rich clinical experiences. Second, when the lesion’s boundary is blurry (which is not rare in clinical practices), different doctors may have different subjective judgments depending on their experience and expertise, so it usually requires to conduct cross-validation on multiple labeling results of different experts to ensure the accuracy and consistency of labeling. Third, the heavy-loaded and homogeneous annotation work can make doctors feel tired and thus negatively affect the accuracy of annotation. Consequently, the lack of high-quality segmentation labels is one of the key factors that restrict the clinical application of intelligent medical image segmentation systems. This Vienna Research Group (VRG) will work on intelligent medical image segmentation with limited annotations and aim to realize effective and efficient medical image segmentation under very low annotation workload. New methods and technologies in the interdisciplinary fields of artificial intelligence, computer vision, and medicine will be explored, so it has great theoretical and scientific value for the research community. In addition, the perspective achievements of this VRG will accelerate the clinical application process of intelligent medical image segmentation system.

The VRG will be embedded in the research unit "Artificial Intelligence Techniques" (E192-07).

 

Scientific contact person: Prof. Agata Ciabattoni, Email: agata.ciabattoni@tuwien.ac.at

Webpage of your present research group: https://www.logic.at/staff/agata/, öffnet eine externe URL in einem neuen Fenster

Logic offers precision and trust and it is at the base of many applications in various fields. Classical logic is not adequate for all of them. We are looking for outstanding candidates that apply logics different from classical logic to Computer Science and/or Artificial Intelligence.

Scientific contact person: Prof. Ivona Brandic, Email: ivona.brandic@tuwien.ac.at

Webpage of your present research group: http://hpc.ec.tuwien.ac.at/, öffnet eine externe URL in einem neuen Fenster

Edge computing can utilize rich data and the availability of powerful computing resources and bring intelligence closer to where IT services are necessary. Edge Intelligence has been the goal of edge cloud research for a decade, supporting fast data analysis and cost reduction to business's efficiency and digitally transformed society.

However, the data proliferation generated by widely distributed devices and data centers creates significant bottlenecks and vertical traffic in the hierarchical network structure while contributing significantly to the overall CO2 footprint of the overall IT infrastructures.

In this VRG we want to explore the paradigm shift: instead of data centric AI/ML we plan to devise radically novel context centric approaches to facilitate low carbon AI/ML.



 

Scientific contact person: Prof. Georg Gottlob, Email: georg.gottlob@tuwien.ac.at

Webpage of your present research group: https://www.cs.ox.ac.uk/people/georg.gottlob/, öffnet eine externe URL in einem neuen Fenster

Current examples include the processing of large and complex queries of relational data, which is entirely outside of the capabilities of state of the art systems, and  counting patterns in network structures (effectively graph databases), a topic which is of broad interest in biology and chemistry, but current methods are limited to very small patterns thus limiting such techniques.

Scientific contact person: Prof. Agata Ciabattoni, Email: agata.ciabattoni@tuwien.ac.at

Webpage of your present research group: https://www.logic.at/staff/agata/, öffnet eine externe URL in einem neuen Fenster

Before Artificial Intelligent systems can be deployed in real-world settings, it is imperative that they satisfy legal and ethical requirements, as well as social expectations. We are searching for excellent candidates that work on theoretical and/or technical foundations for AI systems sensitive to (legal, social, and ethical) norms.

Scientific contact person: Prof. Stefanie Elgeti (stefanie.elgeti@tuwien.ac.at) and Prof. Jürgen Stampfl (juergen.stampfl@tuwien.ac.at)

Webpage of your present research group: www.tuwien.at/mwbw/ilsb/, öffnet eine externe URL in einem neuen Fenster  and http://info.tuwien.ac.at/stampfl/, öffnet eine externe URL in einem neuen Fenster

Throughout the product life cycle — from the original idea all the way to recycling of products — uncertainties are a constant companion. This starts with numerical design, where both the chosen models and their parameters are subject to deviations, e.g., caused by different material batches, continues throughout the manufacturing process, where process deviations need to be compensated for with appropriate controls, and is finally relevant during the product usage under individual and generally unknown conditions. It will be the task of the Vienna Research Group (VRG) to develop appropriate mathematical methods to quantify these uncertainties and translate these methods into robust optimization and control approaches. With its method-oriented focus, the VRG has the potential to establish collaborations into many fields, from virtual product design to biomedical engineering.

Scientific contact person: Prof. Ivona Brandic, Email: ivona.brandic@tuwien.ac.at

Webpage of your present research group: http://hpc.ec.tuwien.ac.at/, öffnet eine externe URL in einem neuen Fenster

HPC research group led by Univ. Prof. Dr. Ivona Brandic conducts research in the area of sustainable systems ranging from smartphones to warehouse scale data centers. In the past the group has developed foundational mechanisms for the energy efficient resource allocation in IaaS Clouds and novel algorithms for the energy efficient deployment of ultra-scale applications in geographically distributed massive multi Clouds (TCC 2016a, TCC 2016b). The recent focus of the HPC group is the utilization of probabilistic and statistical methods for energy efficient resource allocation in different near-real time systems (e.g., geographically distributed AI/ML) (CCGrid 2018, TPDS 2021, SIGMETRICS 2020). Additionally, the focus of the group is the development of novel application decomposition models and tools for the execution on hybrid systems, e.g., classic/quantum (eScience 2022).

With the VRG "Post Moore Era Sustainable and Scalable Data-Intensive Systems hosted" at the HPC research group (TU Wien) we want to address grand challenges of real life ML applications in the transition process to post Moore era, in particular addressing cost-based parallelisation and distribution schemes for ML systems on a hybrid quantum/classic system and focusing on data pipeline integration and optimisation for ML lifecycles.

Scientific contact persons: Thomas Lukasiewicz, Email:  thomas.lukasiewicz@tuwien.ac.at

Webpage of your present research group: https://www.cs.ox.ac.uk/people/publications/date/Thomas.Lukasiewicz.html, öffnet eine externe URL in einem neuen Fenster

The last years have been landmark years in artificial intelligence (AI): Deep learning, an advanced machine learning technology based on neural networks, is achieving revolutionary results in tasks such as speech recognition/generation, computer vision, language-related tasks, game playing, and self-driving vehicles. Compared to information processing in the human brain, however, there are still at least two very striking differences and serious drawbacks of deep learning, namely, that the human brain is highly universal and extremely energy-efficient, both very much in contrast to current deep learning technologies. This suggests that while deep learning certainly seems to realize some facets of human intelligence, there are still crucial aspects missing from how information processing in the human brain really works. A very prominent theory of how the human brain may work is the free-energy principle, which is closely connected to the Bayesian brain hypothesis, and which is partially realized via predictive coding (PC), a theory of brain function in which the brain is constantly generating and updating a mental model of the environment. PC originated in theoretical neuroscience as a model of information processing in the cortex. While these abstract theories of how the brain may work seem very appealing, they still have no concrete implementations to date. However, there has recently been substantial progress on the understanding of PC. In particular, recent work has developed PC into a general-purpose algorithm able to train neural networks using only local computations. In this task, PC shows a substantially greater flexibility against deep learning, since PC-trained networks can function as classifiers, generators, and associative memories simultaneously, and they can also be defined on arbitrary graph topologies (including cycles). Furthermore, PC-trained networks show a similar performance as deep neural networks in classification tasks, and PC is also closely connected to control theory and applications in robotics. Interestingly, PC is based on a novel form of credit assignment (called prospective configuration), differently from backpropagation-based deep learning, which moves PC closer to how the human brain may work. Furthermore, PC has an efficient approximation based only on local computations without external control, called parallel PC, which may be efficiently implemented on neuromorphic hardware. This VRG will explore PC as an alternative approach to train deep neural networks, especially towards more human-level AI and higher energy efficiency.

Scientific contact persons: Thomas Lukasiewicz, Email:  thomas.lukasiewicz@tuwien.ac.at

Webpage of your present research group: https://www.cs.ox.ac.uk/people/publications/date/Thomas.Lukasiewicz.html, öffnet eine externe URL in einem neuen Fenster

Relational learning and reasoning in artificial intelligence (AI) includes a class of challenging problems that can be naturally characterized on relational structures, such as graphs, knowledge bases, or more general representations. Relational data is very prominent in real-world domains, ranging from systems in life sciences (e.g., biomedical, chemical data) to social networks, with diverse applications including drug discovery/repurposing, protein folding/synthesis, recommender systems, and traffic forecasting. This is a highly interactive field, where techniques from machine learning (e.g., deep learning, graph representation learning, and probabilistic inference), knowledge representation (e.g., logical reasoning), and theoretical computer science (complexity and graph theory) are relevant. The vision behind the research agenda of this VRG is to develop machine learning systems that can reliably learn from relational patterns and reason over them, combining the advantages of traditional and modern approaches in AI.

Scientific contact persons: Florian Michahelles, Email: Florian.michahelles@tuwien.ac.at

Webpage of your present research group: Http://Media.tuwien.ac.at, öffnet in einem neuen Fenster 

Advances in sensing, processing, and machine learning enable machines to take initiative of interactions and to proactively approach their users, while gradually fading into sensitive application areas with societal and ethical impact. This development is propelled by new modalities of interaction, such as voice, haptic feedback, virtual/augmented reality, emerging sensing opportunities introspecting users and new capabilities of predicting user needs and intentions.

A new group focusing on shared control would implement and evaluate use cases of humans and machines working towards a joint goal where machine precision and human ingenuity would complement each other. Compelling areas of application and research would range from control of vehicles over to manufacturing to the field of arts.

Scientific contact persons: Prof. Ivona Brandic, E-mail: ivona.brandic@tuwien.ac.at

Webpage of your present research group: http://hpc.ec.tuwien.ac.at/, öffnet eine externe URL in einem neuen Fenster 

Scientific applications comprising simulation, analysis and visualization tasks are usually resource intensive and their execution requires highly heterogeneous infrastructures including clusters, data centers, edge devices, and other special infrastructures. To facilitate the sustainable convergence of AI, Big Data and HPC into advanced scientific workflows, these applications should be deployed on hyper-heterogeneous infrastructures including GPUs, TPUs, quantum processing units, neuromorphic processors, and other emerging non-conventional architectures. The aim of the VRG is to develop foundations for the sustainable execution of comprehensive in-silico scientific workflows for domains like molecular dynamics, earth sciences and astrophysics in this heterogeneous landscape.

Scientific contact persons: Prof. Michael Feiginov, E-mail: michael.feiginov@tuwien.ac.at

Webpage of your present research group: https://www.tuwien.at/en/etit/e354/thz  

THz Electronics group is a part of EMCE Institute at ETIT Department of TU Wien. The main research focus of the THz Electronics group is on investigation of resonant-tunnelling diodes (RTDs) and THz oscillators/sources on their basis. RTDs are the highest-frequency active electronic devices nowadays and they have high potential as an enabling technology for real-world sub-THz and THz applications. That is especially true for future wireless communication systems, which are gradually shifting to sub-THz and THz frequencies. The THz Electronics group studies the limitations of RTD oscillators in the terms of their operating frequency and output power, and also investigates the fundamental physics behind limitations of RTDs and other tunnel structures. State of the art performance RTD oscillators has been demonstrated by the group. The new VRG group should complement the THz Electronics group with other THz technologies for detectors, modulators, tuning elements, etc. with the objective of implementation and demonstration of the whole THz systems, integration of components and demonstration of applicability of the systems for practical applications, particularly in the field of wireless communication.

Scientific contact persons: Prof. Ezio Bartocci, Email: ezio.bartocci@tuwien.ac.at

Webpage of your present research group: http://www.eziobartocci.com/team.php, öffnet eine externe URL in einem neuen Fenster   

Despite the incredible recent advances in building autonomous CPS, providing safety and security assurance for these systems remains an open and very timely research challenge. The lack of predictability, that results from using data-driven learning-enabled components, requires thinking of novel approaches for providing assurance for such safety-critical systems. We are looking for outstanding candidates that can investigate new methods to model, design, and verify autonomous cyber-physical systems equipped with artificial intelligence (AI) and machine learning (ML) components.

Scientific contact person: Prof. Hannes Kaufmann, Email: hannes.kaufmann@tuwien.ac.at

Webpage of your present research group: https://informatics.tuwien.ac.at/orgs/e193-03, öffnet eine externe URL in einem neuen Fenster

Intelligent virtual agents (IVAs) represent a promising extension to a range of application areas. IVAs are autonomously acting virtual humanoid representations that simulate intelligent behavior. For IVAs to be a helpful tool, topics such as the establishment of trustful human-agent relationships (e.g., through the expression of emotions and empathy), as well as ethical, social, and legal aspects, need to be thoroughly researched.

Scientific contact person: Prof. Silvia Miksch, Email: silvia.miksch@tuwien.ac.at

Webpage of your present research group: https://www.cvast.tuwien.ac.at/, öffnet eine externe URL in einem neuen Fenster

Visual Analytics as ”the science of analytical reasoning facilitated by
interactive visual interfaces” aims to enable the exploration and the understanding of large and complex
data sets by intertwining interactive visualization, data analysis, human-computer interaction, and cognitive
and perceptual science. Visual Analytics  make complex information structures more comprehensible, facilitate new insights, and enable process of
information and knowledge discovery. At this, human abilities as well as users' needs and tasks are central issues to
assist in situations where complex decisions need to be made. Data and information are a broad field – we focus
particularly on the temporal, spatial and spatial-temporal dimensions.

The WWTF YRG will be embedding in the research unit "Visual Analytics" # 193-07

Scientific contact person: Prof. Markus Rupp, Email: markus.rupp@tuwien.ac.at

Webpage of your present research group:  https://tiss.tuwien.ac.at/fpl/research-unit/index.xhtml?cmnOrgUnitId=1000630, öffnet eine externe URL in einem neuen Fenster

In the broad field of mobile communications, our group focuses on major topics within the scope of next-generation mobile cellular networks: link layer measurements and simulations of fifth generation (5G) and beyond mobile communications, simulation and optimization of heterogeneous cellular networks with distributed antennas and reconfigurable intelligent surfaces, traffic analysis and simulation at the IP layer and cross-layer optimizations.
The prospective research group should complement our ongoing research by considering aspects like resilience and scalability in wireless networks