Current Projects

Research Projects at the Institute

USA-funded Projects

CyberHeart

CyberHeart

Funding: USA-NSF

Partners: Carnegie Mellon University, University of Maryland, Stony Brook University, Georgia Tech, Rochester Institute of Technology, University of Pennsylvania, Fraunhofer Center for Experimental Software Engineering, Food and Drug Administration (FDA)

Time Frame: started 01. 01. 2015

Research Team: Ezio Bartocci, Scott A. Smolka

The NSF-CPS-Frontiers project CyberHeart is part of the NSF’s initiative to advance the state-of-the-art in Cyber-Physical Systems (CPS): engineered systems that are built from, and depend upon, the seamless integration of computation and physical components. The main goal of the project is to develop far more realistic cardiac device models and controllers than the ones that currently exist. The challenge is in the sheer scale of the human’s cardiac CPS: Six billion cells arranged in a very sophisticated network interact with each other in order to synchronise and contract such that they collectively achieve what is commonly called a heart beat, pumping the blood through the entire body. The CyberHeart platform will provide a basis to test and validate medical devices faster and at a far lower cost than existing methods. It will also provide a platform for designing optimal, patient-specific device therapies, thereby lowering the risk to the patient. CyberHeart includes collaborators from nine leading universities and centres: CMU, RIT, UM, GTech, UPenn, FC-ESE, SBU, TUW and FDA.

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EU-funded Projects

AFarCloud

AFarCloud

Funding: EU-ECSEL Joint Undertaking

Partners: 59 organisations

Time Frame: started 01. 09. 2018

Contact Persons: Christian Hirsch

Research Team: Christian Hirsch

Farming is facing many economic challenges in terms of productivity and cost-effectiveness, as well as an increasing labour shortage partly due to depopulation of rural areas. Reliable detection, accurate identification and proper quantification of pathogens affecting both plant and animal health, must be kept under control to reduce unnecessary costs, trade disruptions and even human health risks.

AFarCloud addresses the urgent need for a holistic and systematic approach. It will provide a distributed platform for autonomous farming, which will allow the integration and cooperation of Cyber Physical Systems in real-time for increased agriculture efficiency, productivity, animal health, food quality and reduced farm labour costs. This platform will be integrated with farm management software and will support monitoring and decision-making, based on big data and real time data mining techniques.

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iDev40 - Enabling Artificial Intelligence in the European Electronic Component and Systems Industry

iDev40 - Enabling Artificial Intelligence in the European Electronic Component and Systems Industry

Funding: EU-ECSEL Joint Undertaking

Time Frame: started 01. 05. 2018

Research Team: Ramin Mohammad Hasani

iDev40 focuses on “digitally connecting” value chains to facilitate and strengthen the innovation capacity of large and small European Electronic Components and System actors for sustainably competitive Electronic Components and Systems “Made in Europe”. Digitalization in integrated product development and production as well as along value chains (digital twin) is the key aspect of the iDev40 project. Linking the digital and the real world with new Industry 4.0 technologies, smarter, more flexible development and production processes are realized as well as a real-time communication via the Internet of Things between value chain partners. These new technologies include data centered learning approaches, cyber physical system based platforms connecting production processes from different sites, knowledge/IP sharing and data protection methods, holistic data validation and exploitation from heterogeneous data sources. The socio-technical context is considered through a human centered knowledge base. We aim to utilize Artificial Intelligence (AI) and Deep Learning (DL) techniques to semantically and automatically enrich data with properties which enable secure storage, tailored protection and validation of data. By integrating design and production data the rate of disconnected data sets will be reduced, which will lead to a decrease in data retrieval time and thus enhance the overall data quality. The elaboration of these technology targets is addressed by (1) Data management, sharing and storage; (2) Gathering, curating, pre-processing and updating/validating knowledge; and (3) Semantical annotation and enrichments of data by adopting AI and DL principles.

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PersoRad

Funding: EU-H2020-EraPerMed

Partners: University Medical Centre Freiburg, TU Wien, IRCCS Humanitas Milano, ICCS Athens, HECOG Athens

Time Frame: started 01. 12. 2019

Prostate cancer (PCa) is the most frequent diagnosed malignancy in male patients and radiation therapy (RT) is a main treatment option. However, RT concepts for PCa have an imminent need to be rectified in order to personalise the RT strategy by considering (i) the individual PCa biology and (ii) the individual disease process of each patient. The consortium concatenates a prospective, non-randomized phase II trial for personalised RT of PCa patients (HypoFocal) with novel tools for patient involvement, advanced “omic” and bioinformatical analyses. In detail: (i) data comprising clinical course and individual PCa radio resistance (measured by yH2AX foci in tissue specimen) will be interpolated by state of the art imaging techniques (PSMA PET/CT and mpMRI) as a radiomics approach and with genomic profiling of the respective tumor tissue samples to identify biomarkers predictive of radio resistance. Additionally, (ii) mobile health tools will be offered to eligible patients of the HypoFocal trial in addition to established health services research approaches, such as interviews and focus groups, in order to increase patient involvement and to obtain patient reported outcomes. Artificial neural network approaches will be employed to support the results which will be implemented in (radio)biological model systems in order to calculate the tumor control probability and normal tissue complication probability of each patient in the future. New online tools with controlled access will address data exchange between project partners ensuring the highest level of data protection. Hereby, the implementation of novel computational components enables (i) unbiased characterization of tumor biology for personalised treatment planning processes (ii) direct integration of patients’ preferences for a personalised follow-up process after RT and (iii) characterization of environmental and personal context factors that are the basis for these preferences.

Adeptness

Adeptness

Funding: EU-H2020-ICT

Partners: Fifty European partners

Time Frame: started 01. 12. 2019

The ADEPTNESS project seeks to implement and investigate a streamlined and automatic workflow that makes methods and tools to be seamlessly used during design phases as well as in operation. We will explore the generation and reuse of test cases and oracles from initial phases of the development, to the system in operation and back to the laboratory for reproduction. Integrated in this workflow, unforeseen situations will also be detected in operation to enhance development models for increasing resilience. We will consider several aspects of uncertainties (such as uncertainties in environment, uncertainty produced due to timing aspects of CPSoS, uncertainty in networks, etc.). Additionally, automatic and synchronized deployment techniques will be investigated to improve the agility of the whole workflow that covers the designoperation continuum.

The CPS group is looking for two PhD candidates to work on this project. Interested candidates should send their applications to Prof. Radu Grosu.

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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.

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IoT4CPS: Trustworthy IoT for CPS

IoT4CPS: Trustworthy IoT for CPS

Funding: AT-FFG

Partners: TU Wien, Austrian Institut of Technology (AIT), Institute of Science and Technology (IST), AVL List GmbH, Donau Uni Krems, Infineon Technologies AG, JKU Linz, Joanneum, NOKIA Österreich, NXP, Salzburg Research, SBA Research, SCCH, Siemens AG Österreich, TTTech Computertechnik AG, TU Graz, X-Net Services GmbH

Time Frame: started 01. 12. 2017

Contact Persons: Ezio Bartocci, Muhammad Shafique

Research Team: Ezio Bartocci (project leader), Muhammad Shafique, Faiq Khalid

IoT4CPS will develop guidelines, methods and tools to enable safe and secure IoT-based applications for automated driving and for smart production. The project will address safety and security aspects in a holistic approach both along the specific value chains and the product life cycles. To ensure the outreach of the project activities and results, the relevant stakeholders will be involved throughout the project and results will be disseminated to expert groups and standardization bodies. IoT4CPS will support digitalization along the entire product lifecycle, leading to a time-to-market acceleration for connected and autonomous vehicles. IoT4CPS will provide innovative components, leading to efficiency increases for the deployment of autonomous driving functions and in smart production environments, which will be validated in a vehicle and in a smart production demonstrator.

LogiCS - Logical Methods in Computer Science

LogiCS - Logical Methods in Computer Science

Funding: AT-FWF

Partners: Vienna University of Technology, Graz University of Technology, Johannes Kepler University Linz, Austrian-wide National Research Network on Rigorous Systems Engineering (RiSE), Vienna Center for Logic and Algorithms (VCLA)

Time Frame: started 01. 01. 2014

Contact Persons: Ezio Bartocci, Ana Oliveira da Costa

Research Team: Ezio Bartocci, Ana Oliveira da Costa, Stefan Szeider

Logical Methods in Computer Science (LogiCS) research project lies in the intersection of two broad areas: Databases and Artificial Intelligence, where logic is used to model, store, analyze and predict information about the outside world including the Internet. And the second one is Verification, where logic is used to model, analyze and construct computer programs themselves. The logical and algorithmic questions which underlie both application areas are studied in the area of Computational Logic. In the LogiCS curriculum, all three directions are prominently represented. Moreover our commitment to international and interdisciplinary collaboration is prerequisite for today’s challenges: Computer science has reached a state where many of the basic engineering questions are reasonably well understood. Many of the big open research questions, however, require computers to perform non-trivial reasoning tasks that permeate computer science, other sciences such as economy, physics, medicine and biology, as well as every-day life. Nowadays, there are a lot of important and interesting research questions in the rapidly expanding domains of cyber-physical (CPS) and biological systems (BS). For instance: ‘How does one formally specify emergent behavior of a system?’ or ‘How does one efficiently predict and detect its onset?’ It is obvious that the formal specification of emergent behavior is a great challenge: first, the logic has to be able to capture temporal properties, which manifest themselves both in the time and in the (dual) frequency domains. Second, the logic has to be able to capture spatial properties, which manifest themselves both in the space and in the (dual) spatial-frequency domains. So right now we are trying to extend temporal logic with frequency domain properties based on different forms of transforms: Fourier, Gabor, Wavelet and to develop a formalism to describe various significant patterns based on combinations of ideas from spatial/temporal logics and signal processing.

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iDev40 - Integrated Development 4.0

iDev40 - Integrated Development 4.0

Time Frame: started 01. 05. 2018

The project proposal “Integrated Development 4.0” aims at achieving significant improvement in digitalising development processes in the European Electronic Components and Systems industry by means of information and communication technologies. In this context, iDev40 is positioned as “Innovation Action” and addresses a holistic system approach of consistently focusing on three main pillars (digital production, knowledge-based and digitized development) enabling a beyond state-of-the-art approach of digitalisation. The newly developed concept of introducing seamlessly integrated development together with automation and network solutions as well as enhancing the transparency of data, their consistency, flexibility and overall efficiency will lower development efforts and, at the same time, lead to a significant reduction of the time to market. By seamlessly bringing together Intelligent Machines, Advanced Analytics and People Excellence, the project embodies the essence of the Industry 4.0 / Industrial Internet and thus strengthens the international leadership of the European industry by means of the “Integrated Development”.

Digital Modeling of Asynchronous Integrated Circuits

Funding: Austrian Science Fund (FWF)

Collaborators: Matthias Függer (ENS Paris-Saclay),Thomas Nowak (Université Paris-Sud), Sayan Mitra (U. Illinois at Urbana-Champaign), Laura Nenzi (U. of Trieste), Ezio Bartocci (TU Wien)

Time Frame: started 01. 07. 2019

Contact Persons: Ulrich Schmid

Research Team: Ulrich Schmid, Jürgen Maier

The project Digital Modeling of Asynchronous Integrated Circuits (DMAC) is devoted to the development of a purely digital model for asynchronous circuits, which enables accurate and fast dynamic timing analysis and is a mandatory prerequisite for any attempt on practical formal verification of such designs. The envisioned model shall be accurate and realistic (= faithful), in the sense that the behavior of circuits described in the model is exactly, i.e., within the modeling accuracy, the same as the behavior of the corresponding real circuit. In contrast to analog models, which are known to be faithful but suffer from excessive simulation times, we target continuous-time discrete-value models here, which essentially boil down to elaborate delay models for gates and/or interconnecting channels.

The project builds on our novel involution model, which considers input-to-output delays as as function of the history in the signal trace. DMAC will fully explore this avenue scientifically, by answering questions such as how to enlarge the class of circuits where the involution model and variants thereof are faithful, how to compose gates with different electrical properties, in particular, different threshold voltage levels, how to accurately model subtle delay variation originating in the Charlie effect in multi-input gates, how to parametrize and characterize the model for a given technology and given operating conditions, and how to possibly further improve the modeling coverage and accuracy. In addition, we will also make the first steps to incorporate our models into existing timing analysis and verification tools for validation purposes.

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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, Laura Kovacs

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.

Reasoning about Knowledge in Byzantine Distributed Systems

Funding: Austrian Science Fund (FWF)

Time Frame: started 01. 07. 2020

Contact Persons: Roman Kuznets, Ulrich Schmid

Research Team: Roman Kuznets, Ulrich Schmid, Giorgio Cignarale, Krisztina Fruzsa, Hugo Rincon Galeana, Thomas Schlögl

Reasoning about fault-tolerant distributed systems, which consist of networked processors without a central control that need to achieve a common goal, is notoriously difficult due to the many sources of uncertainty: varying execution speeds, unpredictable transmission delays, and partial failures make it difficult for a processor to establish local knowledge sufficient to safely perform required actions.

The goal of the proposed project is to incorporate arbitrarily misbehaving agents into an epistemic reasoning framework and to supplement the standard Kripke semantics with a suitable topological semantics for distributed systems, while exploiting, and being informed by, the success of Dynamic Epistemic Logic in capturing the dynamics of knowledge evolution in modal logic.

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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.