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Leo Bronstein received his M.Sc. in mathematics from the Westfälische Wilhelms-Universität in Münster (Germany) in 2014. He has been working as an assistant scientist at the Bioinspired Communication Systems Lab since March 2014.
His research focuses on inference, approximation and model reduction for stochastic reaction dynamics and stochastic processes in general.
- Bronstein, L., & Koeppl, H. (2018). The marginal process Framework: A model reduction tool for Markov jump processes. Accepted for publication in Physical Review E. arxiv:1805.07118
- Bronstein, L., & Koeppl, H. (2018). A variational approach to moment-closure approximations for the kinetics of biomolecular reaction networks. Journal of Chemical Physics, 148 (1), 014105. arXiv:1709.02963.
- Schneider, C., Bronstein, L., Diemer, J., Koeppl, H., & Suess, B. (2017). ROC'n'Ribo: Characterizing a riboswitching expression system by modeling single-cell data. ACS Synthetic Biology, 6 (7).
- Bronstein, L., & Koeppl, H. (2016). Scalable inference using PMCMC and parallel tempering for high-throughput measurements of biomolecular reaction networks. In Decision and Control (CDC), 2016 IEEE 55th Conference on. IEEE.
- Bronstein, L., & Koeppl, H. (2016). A diagram technique for cumulant equations in biomolecular reaction networks with mass-action kinetics. In Decision and Control (CDC), 2016 IEEE 55th Conference on. IEEE.
- Bronstein, L., Zechner, C., & Koeppl, H. (2015). Bayesian inference of reaction kinetics from single-cell recordings across a heterogeneous cell population. Methods, 85.