Tatjana’s research lies at the interface of theoretical computer science and mathematical modelling. Her favourite models are probabilistic, and her favourite application are biological systems. A common thread in Tatjana’s research is the combined use of formal methods (such as model-checking, SAT solvers, automated reasoning in general) and mathematical modelling (such as Markov chains, model reduction, statistical inference, machine learning), as well as the domain-specific modelling languages and theories (such as rule-based modelling, stochastic chemical kinetics). Tatjana’s recent interests include data-informed and learning methods, targeting explanatory models of collective behaviour.
Five most recent publications
- Andreea Beica, Jérôme Feret, Tatjana Petrov: Tropical Abstraction of Biochemical Reaction Networks with Guarantees, The Ninth International Workshop on Static Analysis in Systems Biology (SASB 2018, poster)
- Mirco Giacobbe, Calin C. Guet, Ashutosh Gupta, Thomas A. Henzinger, Tiago Paixão, Tatjana Petrov: Model checking the evolution of gene regulatory networks. Acta Inf. 54(8): 765-787 (2017)
- Przemyslaw Daca, Thomas A. Henzinger, Jan Kretínský, Tatjana Petrov: Faster Statistical Model Checking for Unbounded Temporal Properties. ACM Trans. Comput. Log. 18(2): 12:1-12:25 (2017)
- Przemyslaw Daca, Thomas A. Henzinger, Jan Kretínský, Tatjana Petrov: Linear Distances between Markov Chains. CONCUR 2016: 20:1-20:15
- Przemyslaw Daca, Thomas A. Henzinger, Jan Kretínský, Tatjana Petrov: Faster Statistical Model Checking for Unbounded Temporal Properties.TACAS 2016: 112-129