- joshua at cs.rwth-aachen.de
- Room 4210
- +49 241 80 21203
I was Post-Doc at this chair since April 2019, where I was working on probabilistic programs as a member of the FRAPPANT project.
Formerly, I was a PhD student at the Radboud University in Nijmegen at the Software Science department. Under supervision of Frits Vaandrager, Bas Terwijn, and Alexandra Silva I worked on nominal techniques and black box testing for automata learning.
My research interests also include formal verification, PAC learning, coalgebraic methods, functional programming, black-box testing theory, and category theory.
Bachelor or Master Theses
If you are looking for doing in theses in our group, then I would be happy to supervise you. Here are some ideas for theses. Other topics are of course also possible. Just come by or send an email and then we can talk about it!
- Learning parametric Markov chains. Imagine, there is some parametric Markov chain, but we have no description of it. Can we infer the structure by just looking at its behaviour? Theoretically, this should be learnable to some extend, but there are no implementations. Topics: theory of weighted automata, probability theory, automata learning, a bit of implementation. Some preliminary (and vague) ideas can be found on my blog.
Duality in automata learning.Duality is ubiquitous in computer science. It gives a correspondence between state transformers and predicate transformer, between forward and backward reasoning in Bayesian networks, between modal logic and state spaces, and so on. Can we leverage this for automata learning? Topics: automata learning, duality theory, a bit of implementation Learning MDPs with SMT solving.Can we infer the structure of an Markov decision process (up to equivalence) by only observing its behaviour? Topics: probability theory, SMT solving, implementation. What is the probability of finding a counterexample?Black box testing of a finite state machine is the problem of checking equivalence between a specification and implementation by tests only. Surprisingly, this can be done in a sound and complete way (under some assumption), making it a formal method. Can we give quantitative measures to compare different test generation methods? Topics: Black box testing, state machines, probability theory, implementation. Gradient Descent for Parametric Markov Chains.Can gradient descent (or another iterative method) be useful for parameter synthesis? I think so, but we will have to implement it and benchmark it. Topics: Markov chains, model checking, implementation. This could be supervised together with Jip Spel. I have written some preliminary (and vague) theory on my blog.
See also this list.
Publications (in RWTH UB)
|[bibtex] [issue]||Linus Heck, Jip Josephine Spel, , Joshua Moerman, Joost-Pieter Katoen. Gradient-Descent for Randomized Controllers Under Partial Observability, 23rd International Conference on Verification, Model Checking, and Abstract Interpretation (VMCAI 2022), Volume 13182, Theoretical Computer Science and General Issues of LNCS, 127-150, Springer, 2022.|
|[bibtex] [issue]||Joshua Moerman, . Residuality and Learning for Nondeterministic Nominal Automata, Logical methods in computer science 18 (1), pages 29, Department of Theoretical Computer Science, Technical University of Braunschweig, 2022.|
|[bibtex] [issue]||Joshua Moerman. Orbit-Finite-Dimensional Vector Spaces and Weighted Register Automata, 16 Seiten, 2021. https://arxiv.org/abs/2104.02438, ,|
|[bibtex] [issue]||Lutz Klinkenberg, Kevin Batz, Benjamin Lucien Kaminski, Joost-Pieter Katoen, Joshua Moerman, Tobias Winkler. Generating Functions for Probabilistic Programs, 30th International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR2020), Volume 12561 of Theoretical Computer Science and General Issues, 231-248, Springer, 2021.|
|[bibtex] [issue]||Joshua Moerman, . Residuality and Learning for Nondeterministic Nominal Automata, 27 Seiten, 2021. https://arxiv.org/abs/1910.11666v4|