Edited Books & Special Issues

Proc. of HSCC 2022

Havelund Festschrift (LNCS 13065), 2021

Smolka Festschrift (LNCS 11500), 2019

IEEE/ACM TCBB vol. 15(4), 2018

LNCS Vol. 10457, 2018

Vol 51 (1), FMSD, 2017

LNBI Vol. 9859, CMSB 2016

Vol. 4, STTT 2016

LNCS Vol. 9333, RV 2015

Vol. 236, Inf & Comp 2014

LNCS Vol. 7976, SPIN 2013

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Running projects


MSCA COFUND Doctoral Programme LogiCS@TUWien (2022-2027)


 European Union’s Horizon 2020 (Marie Skłodowska-Curie grant)TU Wien (20 PhD positions)

Principal Investigators:

Ezio Bartocci (Vice-Chair for Programme Admission), Pavol Cerny, Agata Ciabattoni (Vice-Chair for Ethics), Thomas Eiter, Robert Ganian, Laura Kovács, Matteo Maffei, Magdalena Ortiz (Vice-Chair for Publicity and Outreach), Stefan Szeider (Chair), Georg Weissenbacher, Stefan Woltran, 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.

Doctoral College for Secure and Intelligent Human-Centric Digital Technologies (SecInt) (2020-2024)


 TU Wien (10 PhD positions)

Principal Investigators:

Ezio Bartocci, Efstathia Bura, Thomas Gärtner, Laura Kovacs, Andreas Kugi, Martina Lindorfer, Matteo Maffei (Speaker), Semeen Rehman, Georg Weissenbacher, Tanja Zseby (co-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 stake holders and the attention of media, digital technologies that so intimately affect the 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 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.

ProbInG: Distribution Recovery for Invariant Generation of Probabilistic Programs (2020-2024)


 Vienna Science and Technology Fund (WWTF)

Contact Persons:

Ezio Bartocci (Scientific Coordinator), Efstathia Bura (co-PI), Laura Kovacs (co-PI)

Research Team:

Ezio Bartocci, Efstathia Bura, Marcel Moosbrugger, Miroslav Stankovic, Miroslav Stankovic, 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.


Doctoral College Resilient Embedded Systems (2018-2024)


 Bundesministerium Bildung, Wissenschaft und Forschung (BMBWF)

Advisory Board Members:

E. Bartocci, K. M. Göschka M. Horauer, W. Kastner, D. Meyer, M. Shafique.


The research field of Resilient Embedded Systems investigates novel methods to design, verify and implement safe, secure and dependable computing architectures subject to real-time constraints. The topical span of the doctoral college covers all aspects of the direct interaction of computer systems and their environment, from the lowest level of circuit and hardware architectures to safety-critical cyber-physical systems like industrial automation, building automation & smart grids, healthcare, spacecraft, and automotive including networking infrastructures. Designing such systems is challenging, both from a scientific and technological perspective: Many functions are directly implemented in hardware for performance reasons, distributed and parallel processing is omnipresent, digital signal processing is often required, real-time and power/thermal constraints must be met, energy-efficiency is crucial, stopping operation in the case of failures is often not feasible, unique security issues and threats exist, "trial-and-error-style programming" is not an option in many applications, asserting system correctness by means of testing may be insufficient for the required dependability reliability level, emergent behavior originating from autonomous operation must be understood and controlled, integration and complexity issues created by the upcoming Internet of Things must be managed, etc.

Doctoral Program Logical Methods in Computer Science (2018-2022)


 The Austrian Science Fund (FWF)



Principal Investigators:

E. Bartocci, A. Biere, R. Bloem, A. Ciabattoni, T. Eiter, G. Gottlob (Speaker), R. Grosu, L. Kovacs, M. Maffei, M. Ortiz, U. Schmidt, S. Szeider (co-Speaker), G. Weissenbacher, S. Woltran


The LogiCS doctoral program is a PhD degree program funded by the Austrian Science Fund FWF and run jointly by the three Austrian universities Vienna University of Technology, Graz University of Technology and Johannes Kepler University Linz. This program is aimed at highly motivated students who want to work in one of three fundamental fields of computer science: Logic is a powerful reasoning tool. Originally invented as an aid for sound argumentation, it reached maturity in the form of mathematical logic and analytic philosophy in the early 20th century, with significant contributions from Vienna. We continue this tradition, using logic as a tool that enables computer programs to reason about the world. These reasoning tasks allow a natural classification into two broad areas: In Databases and Artificial Intelligence, logic is used to model, store, analyze and predict information about the outside world including the Internet. In Verification, 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.