A PhD position is available (fully paid for 4 years with the possibility of extension) at the Electrical and Computer Engineering Department of Utah State University. The expected starting date is early January 2020. PhD application information is available at: https://engineering.usu.edu/ece/files/pdfs/ece-graduate-program-application-info.pdf
Synthetic biology and nanotechnology place increasing demands on design methodologies to ensure dependable and robust operation. Consisting of noisy and unreliable components, these complex systems have large and often infinite state spaces that include extremely rare error states. Probabilistic model checking techniques have demonstrated significant potential in quantitatively analyzing such system models under extremely low probability. Unfortunately, they generally require enumerating the model’s state space, which is computationally intractable or impossible. Therefore, addressing these design challenges in emerging technologies requires enhancing the applicability of probabilistic model checking. Motivated by this problem, this project investigates an automated probabilistic verification framework that integrates approximate probabilistic model checking and counterexample-guided rare-event simulation to improve the analysis accuracy and efficiency.
This multi-institution collaborative project focuses on verifying infinite-state continuous-time Markov chain (CTMC) models with rare-event properties. It addresses the scalability problem by first applying property-guided and on-the-fly state truncation techniques to prune unlikely states to obtain finite state representations that are amenable to probabilistic model checking. In the case of false or indeterminate verification results, probabilistic counterexamples are generated and utilized to improve the accuracy of the state reductions. Furthermore, it mines these critical counterexamples as automated guidance to improve the quality and efficiency for rare-event probabilistic simulations. This verification framework will be integrated within existing state-of-the-art probabilistic model checking tools (e.g., the PRISM model checking tool), and benchmarked on a wide range of real-world case studies in synthetic biology and nanotechnology.
The PhD position at Utah State University will be advancing and developing efficient model abstraction and state space truncation techniques for the infinite-state CTMC models. In particular, we are interested in investigating:
– Model abstraction techniques on chemical reaction networks for synthetic biology
– Approximation techniques for state space truncation and abstraction
– Property-guided state space pruning techniques
Applicants must have a bachelor’s degree in Computer Science, Computer Engineering, or a related field. A master’s degree is preferred. The successful candidate is expected to demonstrate strong background and interest in formal methods and algorithms, and preferably basic knowledge of probability and random process. He/She should be confident in independently developing academic software tools. Good writing and presentation skills in English are important as well. Knowledge of synthetic biology is preferred, but not required.
For questions about this position, please contact: