Current Projects

Research Projects at the Cyber-Physical Systems Group

EU-funded Projects

LogiCS@TUWien (Doctoral Programme)

Funding: EU-H2020 Marie Skłodowska-Curie COFUND

Time Frame: started 01. 06. 2022

Contact Persons: Ezio Bartocci

Research Team: Ezio Bartocci (vice-chair for admission program), Stefan Szeider (chair), Thomas Eiter, Magdalena Ortiz (vice-chair for publicity and outreach), Stefan Woltran, Robert Ganian, Agata Ciabattoni (vice-chair for ethics), Pavol Cerny, Georg Gottlob, Georg Weissenbacher, Matteo Maffei, Laura Kovacs, Florian Zuleger (vice-chair for research and training)

LogiCS@TUWien is an interdisciplinary Marie Skłodowska-Curie COFUND doctoral programme at Technische Universität Wien (TU Wien) that educates 20 PhD students for 4 years on logical methods in Computer Science and their applications, in particular to Artificial Intelligence, Databases, Verification, Algorithms, Security and Cyber-Physical Systems.


AT-funded Projects

CPS/IoT Ecosystem: Preparing Austria for the Next Digital Revolution

CPS/IoT Ecosystem: Preparing Austria for the Next Digital Revolution

Funding: AT-BMWFW: HRSM Grant

Partners: TU Wien, Austrian Institut of Technology (AIT), Institute of Science and Technology (IST)

Time Frame: started 01. 06. 2017

Research Team: Manfred Gruber (AIT), Thomas A. Henzinger (IST), Schahram Dustdar, Dejan Nickovic (AIT), Gerti Kappel

Cyber-physical systems (CPS) are spatially-distributed, time-sensitive, multiscale networked embedded systems, connecting the physical world to the cyber world through sensors and actuators. The Internet of Things (IoT) is the backbone of CPS. It connects the Sensors and Actuators to the nearby Gateways and the Gateways to the Fog and the Cloud. The Fog resembles the human spine, providing fast and adequate response to imminent situations. The Cloud resembles the human brain, providing large storage and analytic capabilities.

In this project we will make Austria a major player in Real-Time (RT) CPS/IoT, by building on its national strengths. In collaboration with renowned Austrian companies such as TTTech or ams AG, we will create an RT CPS/IoT-Ecosystem with more than 5000 sensors and actuators, where we can all experiment with new ideas, and develop this way an Austrian know-how. This effort will be aligned with the strategic Austrian initiatives, Industry 4.0 and Silicon-Austria. The ecosystem will be distributed across Vienna in collaboration with our partners at AIT and IST.


ProbInG: Distribution Recovery for Invariant Generation of Probabilistic Programs

ProbInG: Distribution Recovery for Invariant Generation of Probabilistic Programs

Funding: AT-WWTF

Partners: TU Wien

Time Frame: started 01. 05. 2020

Contact Persons: Ezio Bartocci

Research Team: Ezio Bartocci, Efstathia Bura (co-PI), Laura Kovacs (co-PI)

Probabilistic programming is a new emerging paradigm adopted by high-tech giants, such as Google, Amazon and Uber, to simplify the development of AI/machine learning based applications, such as route planning and detecting cyber intrusions.

Probabilistic programming languages include native constructs for sampling distributions allowing to freely mix deterministic and stochastic elements. The resulting flexible framework comes at the price of programs with behaviors hard to analyze, leading to unpredictable adverse consequences in safety-critical applications.

One of the main challenges in the analysis of these programs is to compute invariant properties that summarize loop behaviors. Despite recent results, full automation of invariant generation is at its infancy and only targets expected values of the program variables, which is insufficient to recover the full probabilistic program behavior.

Our project aims at developing novel and fully automated approaches to generate invariants over higher-order moments and the value distribution of program variables, without any user guidance. We will employ methods from symbolic summation, polynomial algebra and statistics and combine them with static analysis techniques.

Our results will reduce the need of expert knowledge in ensuring the safety and security of computer systems and will cut the design costs of applications based on probabilistic programs, bringing crucial intellectual and economic benefits to our society.

High-dimensional statistical learning: New methods to advance economic and sustainability policy

High-dimensional statistical learning: New methods to advance economic and sustainability policy

Funding: AT-FWF

Partners: TU Wien, WU Wien, WIFO Wien

Time Frame: started 01. 08. 2019

Contact Persons: Laura Nenzi

Research Team: Ezio Bartocci, Laura Nenzi

Recent years have seen a tremendous surge in the availability of socioeconomic data characterized by vast complexity and high dimensionality. However, prevalent methods employed to inform practitioners and policy makers are still focused on small to medium-scale datasets. Consequently, crucial transmission channels are easily overlooked and the corresponding inference often suffers from omitted variable bias. This calls for novel methods which enable researchers to fully exploit the ever increasing amount of data.

In this project, we aim to investigate how the largely separate research streams of Bayesian econometrics, statistical model checking, and machine learning can be combined and integrated to create innovative and powerful tools for the analysis of big data in the social sciences. Thereby, we pay special attention to properly incorporating relevant sources of uncertainty. Albeit crucial for thorough empirical analyses, this aspect is often overlooked in traditional machine learning techniques, which have mainly been centered on producing point forecasts for key quantities of interest only. In contrast, Bayesian statistics and econometrics are based on designing algorithms to carry out exact posterior inference which in turn allows for density forecasts.

Our contributions are twofold: From a methodological perspective, we develop cutting-edge methods that enable fully probabilistic inference of dynamic models in vast dimensions. In terms of empirical advances, we apply these methods to highly complex datasets that comprise situations where either the number of observations, the number of potential time series and/or the number of variables included is large. More specifically, empirical applications will center on four topical issues in the realm of sustainable development and socioeconomic policy: income inequality, economic growth and climate change, cryptocurrencies, and urban mobility. In these applications, we focus on probabilistic forecasting using real-time data to perform model validation in an efficient way. Moreover, we address structural inference. As policy makers are typically interested in evaluating their policies quantitatively, robust econometric tools are crucial for counterfactual simulations. In light of the increasing complexity of the economy, however, large information sets need to be exploited to appropriately recover the underlying causal structures and provide a rich picture of potential transmission channels of policy interventions. The team constitutes a genuinely collaborative partnership of five young high-potential researchers composed of statisticians, machine learning experts, macro-, ecological and regional economists as well as social and computer scientists. Together, the group has the methodological, empirical, and theoretical expertise required for this project.

SecInt - Secure and Intelligent Human-Centric Digital Technologies (Doctoral College)

SecInt - Secure and Intelligent Human-Centric Digital Technologies (Doctoral College)

Funding: AT-TU Wien

Partners: TU Wien

Time Frame: started 01. 09. 2020

Contact Persons: Ezio Bartocci, Tanja Zseby, Matteo Maffei

Research Team: Ezio Bartocci, Tanja Zseby (co-speaker), Thomas Gärtner, Martina Lindorfer, Andreas Kugi, Georg Weissenbacher, Semeen Rehman, Laura Kovacs, Efstathia Bura, Matteo Maffei (speaker)

Digitalization is transforming our society, making our everyday life more and more dependent on computing platforms and online services. These are built so as to sense and process the environment in which we live as well as the activities we carry on, with the ultimate goal of returning predictions and taking actions to support and enhance our life. Prominent examples of this trend are autonomous systems (e.g., self-driving cars and robots), cyber-physical systems (e.g., implanted medical devices), apps in wearable devices (e.g., Coronavirus contact tracing apps), and so on. Despite the interest of stakeholders and the attention of the media, digital technologies that so intimately affect human life are not yet ready for widespread deployment, as key technical and ethical questions are open, such as trustworthiness, security, and privacy. If these problems are not solved, supposedly intelligent human-centric technologies can lead to death or to other undesirable consequences: e.g., the learning algorithms of autonomous cars can be fooled so as to cause crash accidents, implanted medical devices can be remotely hacked to trigger unwanted defibrillations, and contact tracing apps can be misused towards an Orwellian surveillance system or to inject false at-risk alerts.

The goal of SecInt is to develop the scientific foundations of secure and intelligent human-centric digital technologies. This requires interdisciplinary research, establishing synergies in different research fields (Security and Privacy, Machine Learning, and Formal Methods). Research highlights brought forward by the synergies across projects include the design of machine learning algorithms resistant to adversarial attacks, the design of machine learning algorithms for security and privacy analysis, the security analysis of personal medical devices, the design of secure and privacy-preserving contact tracing apps, and the enforcement of safety for dynamic robots.

The research development is accompanied by a supporting educational and training programme, which encompasses the ethics of secure and intelligent digital technologies, interdisciplinary technical knowledge, as well as internships in international elite research partners, which expressed interest to collaborate with SecInt.