GRK 2236:  UNRAVEL - UNcertainty and Randomness in Algorithms, VErification, and Logic

• Subject Area: Computer Science
• Term: since 2017
• Website: Homepage
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 282652900

Uncertainty is nowadays more and more pervasive in computer science. It is important both in big data and at the level of events and control. Applications have to treat large amounts of data, often from unreliable sources such as noisy sensors and untrusted web pages. Data may also be subject to continuous changes, may come in different formats, and is often incomplete. Robots, trains, and production machines have to deal with unpredictable environments. The growing use of machine-learning components — often providing weak guarantees — forms an additional factor of uncertainty. Probabilistic modelling and randomisation are key techniques for dealing with uncertainty. Many trends witness this. Probabilistic programming exceeds the capabilities of probabilistic graphical models and automates statistical inference. Probabilistic databases deal with noisy data by associating probabilities to the possible worlds. Probabilistic model checking emerged as a key systems verification technique allowing to integrate correctness checking and performance analysis. Similar developments take place in automata, logic, and game theory.The pervasiveness of uncertainty urges to make substantial enhancements in probabilistic modelling and reasoning so as to get deeper insight into, reason about, and master uncertainty. The aim of this RTG is and was to significantly advance various theoretical concepts (in algorithms, logic, verification) as well as their connection to deal with uncertainty and randomness, and to tailor and apply these techniques to problems in application areas such as railway engineering, network dynamics, and cyber-physical systems. This challenge is faced by a unique mixture of scientists from theoretical and applied computer science, management science, mechanical engineering, and railway engineering. The qualification and supervision concept aims at offering the Ph.D. students an optimal environment to carry out their research. Every Ph.D. student has two supervisors; the rights and duties of the supervisors and students are laid down in a written supervision agreement. Progress and quality control is realised through regular individual meetings with the supervisors and regular talks at the RTG events. The curriculum consists of bi-weekly research seminars, soft-skill courses, reading groups, workshops (twice per year), a summer school in the first Ph.D. year, and (various new) advanced lectures.

• DFG Programme: Research Training Groups
• Applicant Institution: Rheinisch-Westfälische Technische Hochschule Aachen
• Spokesperson: Professor Dr. Joost-Pieter Katoen
• Participating Researchers: Professorin Dr. Christina Büsing; Professor Dr. Jürgen Giesl; Professor Dr. Martin Grohe; Professor Gerhard Lakemeyer, Ph.D.; Privatdozent Dr. Christof Löding, until 10/2024; Professor Dr. Christopher Morris; Professor Dr.-Ing. Nils Nießen; Professorin Dr. Britta Peis; Professor Michael Schaub, Ph.D.; Professor Dr. Sebastian Trimpe; Professor Dr.-Ing. Gerhard Woeginger; Professorin Dr. Erika Ábrahám