33. Magda Markowska, Tomasz Cąkała, Błażej Miasojedow, Bogac Aybey, Dilafruz Juraeva, Johanna Mazur, Edith Ross, Eike Staub, Ewa Szczurek: CONET: Copy number event tree model of evolutionary tumor history for single-cell data. Genome Biology 23, no. 1, 1-35, 2022.

32. Krzystof Gogolewski, Błażej Miasojedow, Małgorzata Sadkowska-Todys, Małgorzata Stepień, Urszula Demkow, Agnieszka Lech, Ewa Szczurek, Daniel Rabczenko, Magdalena Rosińska, Anna Gambin: Data-driven case fatality rate estimation for the primary lineage of SARS-CoV-2 in Poland. Methods 203, 584-593, 2022.

31. Piotr Gwiazda, Błażej Miasojedow, Jakub Skrzeczkowski, and Zuzanna Szymańska: Convergence of the EBT method for a non-local model of cell proliferation with discontinuous interaction kernel. IMA Journal of Numerical Analysis, 2022.

30. Wei Jiang, Małgorzata Bogdan, Julie Josse, Szymon Majewski, Błażej Miasojedow, Veronika Ročková, and TraumaBase® Group: Adaptive Bayesian SLOPE: Model Selection with Incomplete Data.: Journal of Computational and Graphical Statistics 31, no. 1, 113-137, 2022.

29.Zuzanna Szymańska, Jakub Skrzeczkowski, Błażej Miasojedow, Piotr Gwiazda: Bayesian inference of a non-local proliferation model. Royal Society open science 8, no. 11, 2021.

28. Maria Giovanna Dainotti, Malgorzata Bogdan, Aditya Narendra, Spencer James Gibson, Blazej Miasojedow, Ioannis Liodakis, Agnieszka Pollo: Predicting the Redshift of γ-Ray-loud AGNs Using Supervised Machine Learning. The Astrophysical Journal 920, no. 2, 2021.

27. Tomasz Ca̧kała, Błażej Miasojedow, Wojciech Niemiro: "Particle MCMC With Poisson Resampling: Parallelization and Continuous Time Models." Journal of Computational and Graphical Statistics 30, no. 3, 2021: 671-684.

26.Błażej Miasojedow, Wojciech Niemiro, Wojciech Rejchel: Asymptotics of maximum likelihood estimators based on Markov chain Monte Carlo methods. In Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, vol. 57, no. 2, pp. 815-829. Institut Henri Poincaré, 2021.

25. Małgorzata Bogdan, Błażej Miasojedow, Jonas Wallin, Discuss: A Novel Algorithmic Approach to Bayesian Logic Regression (with Discussion), Bayesian Analysis 15(1):295–301, 2020.

24. Michał Aleksander Ciach, Błażej Miasojedow, Grzegorz Skoraczyński, Szymon Majewski, Michał Startek, Dirk Valkenborg, Anna Gambin Masserstein: linear regression of mass spectra by optimal transport Rapid Communications in Mass Spectrometry 2020.

23. Tomasz Cąkała, Błażej Miasojedow, Wojciech Niemiro, Particle MCMC with Poisson Resampling: Parallelization and Continuous Time Models, Journal of Computational and Graphical Statistics 1–36, 2020.

22. Alain Durmus, Szymon Majewski, Błażej Miasojedow,Analysis of Langevin Monte Carlo via Convex Optimization, Journal of Machine Learning Research 20(73):1–46, 2019.

21. Mateusz K. Łącki, Frederik Lermyte, Błażej Miasojedow, Michał P. Startek, Frank Sobott, Dirk Valkenborg, Anna Gambin, Masstodon: a tool for assigning peaks and modeling electron transfer reactions in top-down mass spectrometry, Analytical Chemistry 91(3):1801–1807, 2019.

20. Belhal Karimi, Błażej Miasojedow, Eric Moulines, Hoi-To Wai, Non-asymptotic analysis of biased stochastic approximation scheme Conference on Learning Theory 1944–1974, 2019. 19. Neo Christopher Chung, Błażej Miasojedow, Michał Startek, Anna Gambin, Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data, BMC Bioinformatics 20(15):1-11, 2019.

18. Błażej Miasojedow, Wojciech Rejchel,Sparse Estimation in Ising Model via Penalized Monte Carlo Methods, Journal of Machine Learning Research 19:1–26, 2018.

17. Michał Aleksander Ciach, Mateusz Krzysztof Łącki, Błażej Miasojedow, Frederik Lermyte, Dirk Valkenborg, Frank Sobott, Anna Gambin, Estimation of Rates of Reactions Triggered by Electron Transfer in Top-Down Mass Spectrometry, Journal of Computational Biology 25(3):282– 301, 2018.

16. Szymon Majewski, Michał Aleksander Ciach, Michał Startek, Wanda Niemyska, Błażej Miasojedow, Anna Gambin, The Wasserstein distance as a dissimilarity measure for mass spectra with application to spectral deconvolution, 18th International Workshop on Algorithms in Bioinformatics (WABI 2018) 2018.

15. Grzegorz Skoraczyński, Piotr Dittwald, Błażej Miasojedow, Sara Szymkuć, Ewa P. Gajewska, Bartosz Grzybowski, Anna Gambin, Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient? Scientific Reports 7(1):1–9, 2017.

14. Michał Aleksander Ciach, Mateusz Krzysztof Łącki, Błażej Miasojedow, Frederik Lermyte, Dirk Valkenborg, Frank Sobott, Anna Gambin, Estimation of Rates of Reactions Triggered by Electron Transfer in Top-Down Mass Spectrometry, Bioinformatics Research and application (ISBRA 2017), 10330:96-107, 2017.

13. Błażej Miasojedow, Wojciech Niemiro, Geometric ergodicity of Rao and Teh’s algorithm for Markov jump processes and CTBNs, Electronic Journal of Statistics 11(2):4629–4648, 2017.

12. Błażej Miasojedow, Wojciech Niemiro, Jan Palczewski, Wojciech Rejchel, Adaptive Monte Carlo maximum likelihood, Challenges in Computational Statistics and Data Mining 247–270, 2016.

11. Mateusz Krzysztof Łącki, Błażej Miasojedow, State-dependent swap strategies and automatic reduction of number of temperatures in adaptive parallel tempering algorithm, Statistics and Computing 26(5):951–964, 2016.

10. Błażej Miasojedow, Wojciech Niemiro, Geometric ergodicity of Rao and Teh’s algorithm for homogeneous Markov jump processes, Statistics & Probability Letters 113:1–6, 2016.

9. Piotr Gwiazda, Błażej Miasojedow, Magdalena Rosińska, Bayesian inference for age-structured population model of infectious disease with application to varicella in Poland, Journal of Theoretical Biology 407:38–50, 2016.

8. Błażej Miasojedow, Wojciech Niemiro, Jan Palczewski, Wojciech Rejchel, Asymptotics of Monte Carlo maximum likelihood estimates, Probability and Mathematical Statistics-Poland 36(2):295– 310, 2016.

7. Benjamin Jourdain, Tony Lelièvre, Błażej Miasojedow, Optimal scaling for the transient phase of the random walk Metropolis algorithm: the mean-field limit, Annals of Applied Probability 25(4):2263–2300, 2015.

6. Paweł Błażej, Błażej Miasojedow, Małgorzata Grabińska, Paweł Mackiewicz, Optimization of mutation pressure in relation to properties of protein-coding sequences in bacterial genomes, PloS one 10(6):e0130411, 2015.

5. Benjamin Jourdain, Tony Lelièvre, Błażej Miasojedow, Optimal scaling for the transient phase of Metropolis Hastings algorithms: the longtime behavior Bernoulli 20(4):1930–1978, 2014.

4. Błażej Miasojedow, Hoeffding’s inequalities for geometrically ergodic Markov chains on general state space, Statistics & Probability Letters 87:115–120, 2014.

3. Błażej Miasojedow, Eric Moulines, Matii Vihola, An adaptive parallel tempering algorithm, Journal of Computational and Graphical Statistics 22(3):649–664, 2013.

2. Krzysztof Łatuszyński, Błażej Miasojedow, Wojciech Niemiro, Nonasymptotic bounds on the estimation error of MCMC algorithms, Bernoulli 19(5A):2033–2066, 2013.

1. Krzysztof Łatuszyński, Błażej Miasojedow, Wojciech Niemiro, Nonasymptotic bounds on the mean square error for MCMC estimates via renewal techniques, Proceedings of Monte Carlo and Quasi-Monte Carlo Methods 2010, 539-555, 2012.

2019-2022, leader of (Polish) National Science Centre Grant Opus 2018/31/B/ST1/00253 ‘Computational methods for high dimensional statistical learning’

2016-2018, leader of (Polish) National Science Centre Grant Sonata 2015/17/D/ST1/01198 ‘Monte Carlo methods for Markov jump processes.’