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Artificial Intelligence

ChatGPT has turned our world upside down, but there are many more domains where AI is rapidly gaining traction. Stay up-to-date on all latest news, projects and events when it comes to AI.

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Artificial Intelligence (AI) offers vast opportunities for our society: more efficient and personalized services, better decision-making in various fields, and advancements in medicine and science. However, critical challenges in privacy, ethics, and transparency need to be addressed to ensure that AI systems are not discriminatory or unpredictable. Additionally, sustainability poses a future challenge, as the energy consumption of AI systems is significant.

TU Wien Informatics is at the forefront of advancements in AI, addressing challenges and developing ethical, human-centered and environmentally sustainable AI systems. Our research focuses on a diverse range of approaches, and the recently established Cluster of Excellence Bilateral AI leverages the potential of combining symbolic (Knowledge Representation and Reasoning) and sub-symbolic (Machine Learning) AI. The combination of symbolic and sub-symbolic AI opens up new avenues for AI that are better at solving problems, adapting to a wide variety of environments, having better reasoning skills, and being more efficient in terms of both computation and data use. These key features allow for a vast range of use cases, starting with drug development and medicine, over planning and scheduling, to autonomous traffic management and recommendation systems. With fairness, transparency, and explainability as top priorities, developing next generation AI systems is also essential for addressing ethical concerns and ensuring a positive impact on our society.

Highlights

Missed something? Scroll down to see all our recent highlights, events, and news.

Our Mission

Artificial Intelligence relies on a wide range of methods - from logics, deep learning, and knowledge representation to large language models. It is essential to understand these different approaches to be at the forefront of the development of next-generation AI systems. These systems require a combination of methods to provide robust, transparent, and fair results. With strong roots in both Symbolic AI and Machine Learning and a reflexive stance on the impact we have on society, TU Wien Informatics provides the perfect environment for this endeavor.

Stefan Woltran, Head of CAIML

We are actively engaged in many research projects and collaborations that explore the opportunities and challenges presented by AI. Our faculty works both within and beyond the field to foster and facilitate research and collaborations that benefit all of society. We are convinced that ethical considerations are crucial to the development of AI, and must be integrated from day one. Our researchers are developing and embedding ethical frameworks into AI to build fair and trustworthy AI systems.

Our research extends across various disciplines, emphasizing the importance of diversity in both the people who conduct research and the methods they use. The recently established Cluster of Excellence, Bilateral AI (BILAI), brings together two main approaches in AI, symbolic and sub-symbolic AI, with the goal to create a Broad AI that is capable of addressing diverse tasks across multiple domains. The Center for Artificial Intelligence and Machine Learning (CAIML) and the the Christian-Doppler Laboratories are at the forefront of AI and Machine Learning research, leading efforts to strengthen both in their foundations and applications. Through the Digital Humanism Initiative, we strive for an inclusive, participatory development of technologies that benefit us all. To ensure a positive impact of AI in all domains, we need robust, trustworthy, explainable, but also sustainable AI systems. If you want to learn more about Sustainability in AI, you can read about current projects, updates, and news here.

Our faculty’s involvement with AI spans a wide range—from theoretical, formal, and ethical approaches to developing secure AI systems that are deployed in medicine, research, industry, and our everyday lives. If you want to learn more about our research projects, have a look at our Research Units; you’re sure to find some interesting projects, topics, and people!

Research

 
Bilateral AI logo

Bilateral AI

  • Cluster of Excellence
  • Since 2024

BILAI unites leading AI experts from TU Wien, JKU Linz, WU Wien, TU Graz, Universität Klagenfurt, and ISTA. The goal is to develop a Broad Artificial Intelligence (AI) that brings two approaches in the field of AI together: symbolic AI and sub-symbolic AI. By combining sub-symbolic AI (Machine Learning) with symbolic AI (Knowledge Representation and Reasoning), Bilateral AI provides the means to develop the foundations of a new level of AI with broader capabilities for skill acquisition and problem-solving: Broad AI.

Center for AI and Machine Learning logo

Center for AI and Machine Learning

  • Inter-Faculty Center
  • Since 2021

Headed by Stefan Woltran and Clemens Heitzinger, the Center for Artificial Intelligence and Machine Learning (CAIML) strives to consolidate and enhance research endeavors in both Artificial Intelligence and Machine Learning. Encompassing both fundamental principles as well as real-world implementations, its goal is to position TU Wien as a beacon of excellence in the field. By fostering interdisciplinary collaboration, it propels breakthroughs at the crossroads of AI and ML.

Center for Logic and Algorithms logo

Center for Logic and Algorithms

  • Inter-Faculty Center
  • Since 2011

Headed by Stefan Szeider and Agata Ciabattoni, the Vienna Center for Logic and Algorithms (VCLA) is promoting international scientific collaboration in logic and algorithms.Its outreach activities are aimed towards raising aspirations of young people for academic pursuits and to raise awareness on the impact of the research done in the areas of logic, philosophy, mathematics, computer science, and artificial intelligence among the general public.

CDL AI and Optimization for Planning and Scheduling logo

CDL AI and Optimization for Planning and Scheduling

  • Christian Doppler Laboratory
  • Since 2017

Headed by Nysret Musliu, the project develops innovative problem-solving techniques based on the synergy of AI and optimization. These methods use machine learning for automatic selection, configuration and design of algorithms. New strategies based on the hybridization of methods from both fields are proposed.

CDL Recommender Systems logo

CDL Recommender Systems

  • Christian Doppler Laboratory
  • Since 2022

Headed by Julia Neidhardt, the project develops methods to model and predict the behavior of various interrelated outcome dimensions (accuracy, diversity, novelty, and serendipity) and biases, taking into account different domains and a multi-level view of users (individual, group, and network levels).

Joint Human-Machine Data Exploration logo

Joint Human-Machine Data Exploration

  • FWF
  • Since 2023

We integrate interactive ML and interactive visualization to learn about data and from data in a joint fashion. To this end, we propose a data-agnostic joint human-machine data exploration (JDE) framework that supports users in the exploratory analysis and the discovery of meaningful structures in the data.

Scalable Reasoning in Knowledge Graphs logo

Scalable Reasoning in Knowledge Graphs

  • WWTF
  • Since 2020

Headed by Emanuel Sallinger, the project aims to develop the theoretical and practical foundations for scalable reasoning in knowledge graphs. To this end, new methods and algorithms are designed that combine the best methodologies and techniques from the database and Knowledge Representation and Reasoning areas with those of selected other fields.

Towards Trustworthy Recommendation Systems for Online Social Networks logo

Towards Trustworthy Recommendation Systems for Online Social Networks

  • WWTF
  • Since 2024

Headed by Stefan Neumann, the project aims to understand the impact of recommender systems in social networks before they are deployed. This is arguably one of the most important challenges in the area of social network analysis, with the goal of minimizing negative societal effects and contributing to the effective regulation of social media algorithms.

Visual Analytics and Computer Vision Meet Cultural Heritage logo

Visual Analytics and Computer Vision Meet Cultural Heritage

  • FWF
  • Since 2023

Visual media such as historical photographs and amateur films are important components of the media collections created by digitization. To capture the contents of these collections and gain new insights, it takes methods that combine efficient automated data analysis with the expertise of specialists. We explore approaches to automatic image analysis and visualization to access historical media collections and make them accessible to a wide range of users.

Education

We are dedicated to excellence in our research activities, but we are equally dedicated to excellence when it comes to teaching and education.

We are at the forefront of mastering complex societal challenges with our research and education in AI. What sets us apart? Academic excellence! Our vision: To create a space where people can thrive, and their curiosity can become cutting-edge research. Beyond our dedication to excellence, our research and education efforts go hand in hand with a strong commitment to society. At TU Wien Informatics, you’ll learn from the best and are integrated into an international research community, and a good balance between teachers and students helps you to reach your full potential.

Contact

Want to get to know the people behind our research? Here are the leading experts and primary contacts for our efforts in AI in alphabetical order:

Contact
Sabine Andergassen

Sabine Andergassen

Machine Learning
Contact
Kees van Berkel

Kees van Berkel

AI Ethics
Contact
Ivona Brandic

Ivona Brandic

High Performance Computing Systems
Contact
Agata Ciabattoni

Agata Ciabattoni

Bilateral AI Board of Directors
Contact
Thomas Eiter

Thomas Eiter

Bilateral AI Deputy Director of Reserach
Contact
Thomas Gärtner

Thomas Gärtner

Machine Learning
Contact
Georg Gottlob

Georg Gottlob

Emerit. Professor
Contact
Radu Grosu

Radu Grosu

Cyber-Physical Systems
Contact
Allan Hanbury

Allan Hanbury

Data Intelligence
Contact
Clemens Heitzinger

Clemens Heitzinger

Director of CAIML
Contact
Martin Kampel

Martin Kampel

Computer Vision
Contact
Peter Knees

Peter Knees

UNESCO Chair on Digital Humanism
Contact
Thomas Lukasiewicz

Thomas Lukasiewicz

Artificial Intelligence Techniques
Contact
Silvia Miksch

Silvia Miksch

Visual Analytics
Contact
Nysret Musliu

Nysret Musliu

CDL AI and Optimization for Planning and Scheduling
Contact
Julia Neidhardt

Julia Neidhardt

CDL Recommender Systems
Contact
Stefan Neumann

Stefan Neumann

WWTF Towards Traustworthy Recommendation Systems for Online Social Networks
Contact
Magdalena Ortiz

Magdalena Ortiz

Knowledge-Based Systems
Contact
Emanuel Sallinger

Emanuel Sallinger

WWTF Scalable Reasoning in Knowledge Graphs
Contact
Stefan Szeider

Stefan Szeider

Algorithms and Complexity
Contact
Hans Tompits

Hans Tompits

Curriculum Coordinator
Contact
Stefan Woltran

Stefan Woltran

Director of CAIML

News and Events

Recent highlights, events, and news from all our efforts in AI.
Want more? Head over to our Newsroom!

Experts in the Media

Here you’ll find the latest media coverage with our AI experts:

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