Probabilistic Programs Professors and Supervisors in Germany

Lars GrunskeView profile

Professor

Max Planck Institute for Security and PrivacyGermany

Lars Grunske is a Professor in the Department of Computer Science at Humboldt University of Berlin, Germany. His academic career spans multiple institutions across Germany, Australia, and internationally, with a strong focus on research and conference participation in software engineering. His educational background includes a PhD in computer science from the University of Potsdam (Hasso-Plattner-Institute for Software Systems Engineering) in 2004. Professor Grunske's research interests center on modeling and verification of systems and software, with particular emphasis on automated analysis techniques. His work primarily focuses on probabilistic and timed model checking and model-based dependability evaluation of complex software intensive systems. He has made significant contributions to software testing, program repair, formal methods, and the application of machine learning techniques to software engineering problems. His publication record shows consistent contributions to top software engineering conferences over the past decade, with recent work exploring the intersection of AI/ML with traditional software engineering challenges. His research demonstrates an evolution from foundational model checking techniques toward more practical applications in software testing and repair. Boeing Postdoctoral Research Fellow Professor Grunske actively mentors through conference activities including chairing mentoring circles at ICSE 2021. He serves on numerous program committees for major software engineering conferences including ASE, ICSE, ESEC/FSE, and others, demonstrating his standing in the academic community. His involvement spans multiple roles from committee member to track chair and award committee positions. He maintains an active research laboratory focused on software verification and testing, as indicated by his departmental affiliation and research website.

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Đorđe ŽikelićView profile

Assistant Professor

Max Planck Institute for Security and PrivacyGermany

Đorđe Žikelić is an Assistant Professor of Computer Science at the School of Computing and Information Systems at Singapore Management University (SMU) in Singapore. He completed his PhD in 2023 at the Institute of Science and Technology Austria (ISTA) under Krishnendu Chatterjee and Petr Novotný, receiving both Outstanding PhD Thesis and Outstanding Scientific Achievement awards. Prior to his doctorate, he earned bachelor's and master's degrees in mathematics from the University of Cambridge. His educational background includes: PhD in Computer Science, Institute of Science and Technology Austria (ISTA), 2023 Bachelor's and Master's in Mathematics, University of Cambridge Dr. Žikelić's research focuses on advancing formal methods to ensure software and AI systems are correct, safe, and trustworthy. His work bridges theoretical aspects of formal reasoning about probabilistic systems with practical automated verification methods. His primary research interests span three interconnected areas: Program Analysis and Verification: He develops techniques for analyzing probabilistic programs, numerical programs, and efficient quantifier elimination methods, addressing fundamental challenges in verifying complex software systems. Trustworthy AI and Safe Autonomy: He creates formal verification frameworks for learning-enabled control systems and neural networks, ensuring AI operates safely in uncertain environments through methods like runtime monitoring and certificate repair. Probabilistic System Verification: He explores broader applications including bidding games on graphs and blockchain protocol analysis, extending formal methods to novel domains beyond traditional finite-state verification. His publication trajectory shows a consistent progression from theoretical foundations to practical applications, with recent work increasingly focused on integrating formal verification with machine learning. His 2024-2025 publications demonstrate growing expertise in verifying learning-based systems and developing practical tools like PolyQEnt for quantified entailment solving. His scientific achievements have been recognized with: Outstanding PhD Thesis Award at ISTA Outstanding Scientific Achievement Award at ISTA Distinguished Paper Award at FM 2024 Dr. Žikelić serves on program committees for major conferences including TACAS, PLDI, AAAI, and CAV. He actively mentors through the Programming Languages Mentoring Workshop (PLMW) at PLDI 2025. His research group at SMU focuses on developing novel algorithms for verifying correctness of programs and AI systems, with current projects spanning formal methods, artificial intelligence, and programming languages.

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Michael KaliskeView profile

Professor

Bochum University of Applied SciencesGermany

Professor Michael Kaliske is a leading researcher at Dresden University of Technology's Institute of Statics and Dynamics of Structures, where he conducts cutting-edge research in computational mechanics and structural engineering. His work bridges theoretical developments with practical applications across civil, mechanical, and materials engineering disciplines. Professor Kaliske's research focuses on computational mechanics with particular expertise in finite element methods, multiscale modeling, and material behavior under various loading conditions. His work spans diverse application areas including concrete technology, wooden structures, elastomeric materials (particularly tires), and uncertainty quantification in structural design. He has developed sophisticated numerical frameworks for analyzing complex structural behaviors, especially in contexts where traditional analytical methods fall short. His research demonstrates strong interdisciplinary connections between civil engineering, mechanical engineering, and computational science with practical applications in infrastructure durability and material design. His publication record shows consistent advancement in computational methods for structural analysis, with recent work emphasizing multiscale approaches, uncertainty quantification, and experimental validation. The research themes demonstrate progression from fundamental material modeling to increasingly complex system-level analysis, particularly in infrastructure durability and tire-road interaction phenomena. His work increasingly incorporates data-driven approaches and uncertainty modeling to address real-world engineering challenges. Professor Kaliske actively leads multiple research projects funded by the German Research Foundation (DFG), including ongoing Priority Programs and Material Grants. His project portfolio demonstrates significant contributions to both theoretical developments in computational mechanics and practical applications in civil infrastructure and industrial manufacturing. His research has particularly strong connections to industrial applications in tire manufacturing and road infrastructure design.

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Nicola FerdinandView profile

Professor

Bergische University WuppertalGermany

Prof. Dr. Nicola Ferdinand serves as a Professor and Chair holder at the University of Wuppertal, leading research in neurocognitive development and behavioral regulation. Her laboratory investigates cognitive functions across the human lifespan with particular emphasis on error processing, executive control mechanisms, and learning systems. Her research program centers on developmental trajectories of cognitive functions from childhood through aging, examining how motivational states and emotional contexts modulate learning and memory processes. Key investigations include neurophysiological correlates of feedback processing using ERP methodologies, implicit versus explicit knowledge acquisition, and social-emotional influences on decision-making. Her work bridges basic cognitive neuroscience with practical applications in educational and clinical settings, particularly regarding ADHD interventions and age-related cognitive changes. Analysis of her publication record (2012-2025) reveals consistent focus on feedback processing mechanisms across developmental stages, with increasing emphasis on social factors in adolescence and aging populations. Methodologically, she integrates behavioral paradigms with electrophysiological measures to dissect cognitive control components, demonstrating how emotional valence modulates learning efficiency particularly in challenging contexts. While specific grant details aren't provided in the source material, her extensive collaborative network is evident through multi-institutional publications. She maintains an active laboratory team at the University of Wuppertal focused on lifespan cognitive development, with current projects examining social exclusion effects on adolescent risk-taking and neurocognitive markers of subjective cognitive decline in healthy aging.

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