dr Piotr Pokarowski

Research interests

• Statistics: maximum likelihood, exploratory data analysis, prediction
• Monte Carlo algorithms: design, error estimation, numerical experiments
• Markov chains: aggregation, Markov Chain Monte Carlo, hidden Markov models
• Protein structure: prediction, simulation of folding and crystal growth processes

Selected papers

• A. Gambin, P. Krzyzanowski and P. Pokarowski, Aggregation Algorithms for Perturbed Markov Chains with Applications to Networks Modeling. SIAM Journal on Scientific Computing, 31 (1), 45-73, 2008.
• W. Niemiro and P. Pokarowski, Fixed Precision MCMC estimation by Median of Products of Averages. Preprint, 2007.
• S. Kawashima, P. Pokarowski, M. Pokarowska, A. Kolinski, T. Katayama and M. Kanehisa, AAindex: amino acid index database, progress report 2008. Nucleic Acids Research 36 (Suppl.1), D202-D205, 2008.
• P. Pokarowski, A. Kloczkowski, S. Nowakowski, M. Pokarowska, R. L. Jernigan and A. Kolinski, Ideal amino acid exchange forms approximate substitution matrices. Proteins: Structure, Function and Bioinformatics, 69, 379-393, 2007. Supplemental Material
• L. Gajek, W. Niemiro and P. Pokarowski, Optimal sequential Monte Carlo algorithms with fixed relative precision. Preprint, 2005.
• P. Pokarowski, K. Droste and A. Kolinski, A minimal protein-like lattice model: an alpha-helix motif. Journal of Chemical Physics, 122, 214915, 2005.
• P. Pokarowski, A. Kloczkowski, R. L. Jernigan, N. Kothari, M. Pokarowska and A. Kolinski, Inferring ideal amino acid interaction forms from statistical protein contact potentials. Proteins: Structure, Function and Bioinformatics, 59(1):49-57, 2005. Supplemental Material
• A. Kolinski, D. Gront, P. Pokarowski and J. Skolnick, A simple lattice model that exhibits a protein-like cooperative all-or-none folding transition. Biopolymers, 69(3):399-405, 2003.
• P. Pokarowski, A. Kolinski and J. Skolnick, A minimal physically realistic protein-like lattice model: Designing an energy landscape that ensures all-or-none folding to a unique native state. Biophysical Journal, 84(3):1518-1526, 2003.
• A. Gambin and P. Pokarowski, A new combinatorial algorithm for large Markov chains. Computer Algebra in Scientific Computing - CASC 2001, V.G. Ghanza, E.W. Mayr, E.V. Vorozthsov (Eds.), Springer, Berlin 2001, pp. 195-212.
• A. Gambin and P. Pokarowski, A combinatorial aggregation algorithm for computing stationary distributions of large Markov chains. Fundamentals of Computation Theory 13th International Symposium - FCT 2001, R. Freivalds (Ed.), Springer LNCS 2138, 2001, pp. 384-388.
• A. Kierzek, P. Pokarowski and P. Zielenkiewicz, Microscopic model of protein crystal growth. Biophysical Chemistry, 87(1): 43-61, 2000.
• J. Jachymski, L. Gajek and P. Pokarowski, The Tarski-Kantorovitch principle and the theory of iterated function systems. Bulletin of the Australian Mathematical Society, 61:247-261, 2000.
• A. Kierzek, P. Pokarowski and P. Zielenkiewicz, Lattice simulation of protein crystal formation. Biophysical Chemistry, 77:123-137, 1999.
• P. Pokarowski, Directed forests with applications to algorithms related to Markov chains. Applicationes Mathematicae, 26(4):395-414, 1999.
• P. Pokarowski, Directed forests and algorithms related to Markov chains (in Polish). PhD thesis, Institute of Mathematics of the Polish Academy of Sciences, 1998.
• P. Pokarowski, Uncoupling measures and eigenvalues of stochastic matrices. Journal of Applied Analysis, 4(2):261-269, 1998.
• W. Niemiro and P. Pokarowski, Tail events of some non-homogeneous Markov chains. Annals of Applied Probability, 5(1):261-293, 1995.

Teaching:

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