Leading the Way with Computing: Meet Our New Professors
Inaugural lectures with our new professors Maria Christakis, Henderik Proper, Katja Hose and Thomas Lukasiewicz.
TU Wien, Campus Freihaus
1040 Vienna, Treitlstraße 3
Leading the Way with Computing
We are pleased to welcome our four new professors who joined the Faculty of Informatics last year. Maria Christakis, Henderik Proper, Katja Hose, and Thomas Lukasiewicz will present their work. Don’t miss this opportunity to meet them in person and learn more about their exciting fields of work!
The event will be moderated by the Dean of TU Wien Informatics Gerti Kappel.
Inaugural lectures by:
After the presentations, we look forward to a panel discussions with all four professors.
We would like to invite you to a reception with food and drinks at the end of the event.
Rigorous Software Engineering
My research vision is to make program analysis applicable to a broad range of users and software. I am primarily interested in developing theoretical foundations and practical tools for building more reliable software and increasing developer productivity. My main focus is on investigating rigorous methods in software engineering, such as automatic test generation and static program analysis. In this talk, I will give an overview of my current research and delve deeper into techniques for testing program analyzers and machine-learning models.
Since September 2022, Maria Christakis is a full professor at TU Wien, leading the Software Engineering research unit. Her techniques and tools explore novel ways in writing, specifying, analyzing, testing, and debugging programs in order to make them more robust while improving the developer experience. Before joining TU Wien, she was doing research at the Max Planck Institute for Software Systems in Germany, the University of Kent in England, Microsoft Research in the US, and ETH Zurich in Switzerland. Since 2022, Maria was awarded an ERC Starting grant, a WWTF ICT grant as well as a Google Research Scholar award.
Business Informatics: Bridging between Models, Understanding and Computing
Business informatics is a multifaceted engineering discipline, which studies informatics in its social, technical, economical, and ethical, context. As is the case for many engineering disciplines, models have a central role to play in business informatics as well. They enable us to capture many different aspects of IT systems in relation to the context in which they need to fit. Key in the use of models in a business informatics setting is the fact that they essentially need to act as a “boundary object” between the worlds of computing and human understanding. For instance, from a traditional “model-driven systems engineering” point of view, models are used to capture the human understanding regarding the application domain as well as the requirements and motivations for the systems to be developed; thus bridging from understanding to computing. An example, in the opposite direction, i.e. bridging from computing to understanding, is the use of models in the context of explaining AI, where models are needed to capture the reasoning of (sub-symbolic) AI systems, to then enable humans to understand this reasoning. What is also exciting is the fact that IT can actually aid in the creation and (human) understanding of models. For instance, computing can aid in the sensing, capturing, and analysing existing situations. A typical example of such a scenario is business process mining. Conversely, computing can be used to improve human-model interaction. Examples include the use of advanced user-interfacing and visualisation techniques to bring “static” and “paper-style” models to “life” by making them navigable or even more interactive. In this short presentation, I will also position some of our relevant past work, as well as discuss the main research challenges we will focus on the years to come. In doing so, I will also identify our research priorities regarding possible application domains and societal challenges such as sustainability, as well as our priorities towards teaching that emanate from this.
Prof. Dr. Henderik A. Proper, Erik for friends, is a Full Professor in Enterprise and Process Engineering in the Business Informatics Group at the TU Wien. Erik has a mixed background, covering a variety of roles in both academia and industry. His general research interest concerns the foundations and applications of domain modelling. In his research, Erik takes his inspiration from fundamental challenges he observes in domain modelling practices. Over the past 20 years, he has applied this research drive and general research in the context of enterprise design management. Erik is also co-initiator of the ArchiMate research project, which also resulted in the ArchiMate standard for enterprise architecture modelling. Presently, he is also the vice-chair of the IFIP 8.1 working group on information systems, while also being the representative for the Netherlands in IFIP’s TC8 technical committee. Furthermore, he is the Stellvertretender Sprecher (vice chair) of the working group on enterprise modelling and information systems architectures (EMISA) of the German Computer Science Society (Gesellschaft für Informatik).
Bringing Meaning to Large Amounts of Data… with, without, and for AI
While large amounts of data are being generated and collected, we are still struggling to efficiently exploit, interpret, and extract meaningful insights from such heterogeneous data. Achieving this goal requires a variety of techniques relying on data engineering and machine learning. This is not only important for direct interaction with humans but also in the context of recent advances in artificial intelligence. Since such systems learn from the data they are trained on, it becomes crucial to provide them with verifiable knowledge, reliable facts, patterns, and a deeper understanding of the domains that they are used for. This talk will chart a number of challenges for managing and bringing meaning to large amounts of heterogeneous data and discuss opportunities with, without, and for artificial intelligence emerging from my research situated at the confluence of data management, data and knowledge engineering, and machine learning.
Katja Hose is a full professor of Data Management at TU Wien, databases and AI research unit. As a professor in the Department of Computer Science at Aalborg University, she has been leading the Data, Knowledge, and Web Engineering group. Prior to joining Aalborg University, she was a postdoc at the Max Planck Institute for Informatics in Saarbrücken, Germany, and received her PhD in Computer Science from Ilmenau University of Technology, Germany. Her research is situated at the confluence of data management, knowledge engineering, and machine learning. It is spanning theory, algorithms, and applications of Data Science including graph databases, knowledge graphs, query optimization, analytics, and machine learning. In the past couple of years, she has also gained experience in interdisciplinary data science in collaborations with colleagues from bioscience, medicine, and environmental assessment.
Neurosymbolic AI and Predictive Coding for Explainable, Fair, Safe, and Robust Intelligent Systems towards Human-level Intelligence
I explore artificial intelligence (AI) techniques, ranging from machine learning to symbolic techniques. My main focus is currently especially on neurosymbolic artificial intelligence (AI) and predictive coding to develop explainable, fair, safe, and robust intelligent systems towards human-level intelligence. Neurosymbolic AI combines the strengths of symbolic reasoning and neural network learning, while predictive coding is a neural network model that aims to explain how the brain processes sensory information and makes predictions. Both approaches will help to overcome the limitations of current AI systems, and enhance the interpretability, ensure the fairness and safety, and improve the robustness of AI systems.
Thomas Lukasiewicz is a Professor and research unit Head for Artificial Intelligence Techniques at TU Wien, Austria, since April 2022. He also holds an AXA Chair on “Explainable Artificial Intelligence in Healthcare” since 2019. Before moving to TU Wien, he was a Professor of Computer Science at the Department of Computer Science, University of Oxford, UK, since 2010, heading the Intelligent Systems Lab within the Artificial Intelligence and Machine Learning Theme. Thomas Lukasiewicz is a Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) since 2022 and a Fellow of the European Association for Artificial Intelligence (EurAI; formerly ECCAI) since 2020. He received the KR 2022 Test-of-Time Award, the 2019 ACM PODS Alberto O. Mendelzon Test-of-Time Award, the RuleML 2015 Best Paper Award, the AIJ Prominent Paper Award 2013, and the IJCAI-01 Distinguished Paper Award. He is or has been a PC area chair, senior PC member, or PC member for more than 200 conferences and workshops (more than 20 of which co-chaired). He is also an area editor for ACM TOCL, and has recently been an associate editor for JAIR and AIJ.