Zainteresowania naukowe:

Procesy stochastyczne, dyfuzja anomalna, szeregi czasowe, uczenie maszynowe.

Publikacje:

1. H. Loch, J. Janczura, A. Weron (2016) Ergodicity testing using an analytical formula for a dynamical functional of alpha-stable autoregressive fractionally integrated moving average processes. Physical Review E 93, 043317, doi: 10.1103/PhysRevE.93.043317.
2. H. Loch-Olszewska, G. Sikora, J. Janczura, A. Weron (2016) Identifying ergodicity breaking for fractional anomalous diffusion: Criteria for minimal trajectory length. Physical Review E 94, 052136, doi: 10.1103/PhysRevE.94.052136.
3. H. Loch-Olszewska, J. Szwabiński (2018) Detection of ε-ergodicity breaking in experimental data — A study of the dynamical functional sensibility. The Journal of Chemical Physics 148, 204105, doi: 10.1063/1.5025941.
4. M. Balcerek, H. Loch-Olszewska, J. A. Torreno-Pina, M. F. Garcia-Parajo, A. Weron, C. Manzo, K. Burnecki (2019) Inhomogeneous membrane receptor diffusion explained by a fractional heteroscedastic time series model. Physical Chemistry Chemical Physics 21, 3114, doi: 10.1039/C8CP06781C.
5. H. Loch-Olszewska (2019) Properties and distribution of the dynamical functional for the fractional Gaussian noise. Applied Mathematics and Computation 356, 252, doi: 10.1016/J.AMC.2019.03.038.
6. P. Kowalek, H. Loch-Olszewska, J. Szwabiński (2019) Classification of diffusion modes in single-particle tracking data: Feature-based versus deep-learning approach. Physical Review E 100, 032410, doi: 10.1103/PhysRevE.100.032410.
7. J. Janczura, P. Kowalek, H. Loch-Olszewska, J. Szwabiński, A. Weron (2020) Classification of particle trajectories in living cells: Machine learning versus statistical testing hypothesis for fractional anomalous diffusion. Physical Review E 102, 032402, doi: 10.1103/PhysRevE.102.032402.
8. H. Loch-Olszewska, J. Szwabiński (2020) Impact of feature choice on machine learning classification of fractional anomalous diffusion, Entropy 22, 1436, doi: 10.3390/e22121436.