I am an experimental physicist with PhD in Nanosciences and a strong interest in working with data: data analysis and machine learning. My recent research is focused on the Machine learning applications for analysis of scientific data. Currently I employ ML techniques for X-ray spectroscopy in Finland.
My works are implemented in Python which I use for scientific data analysis (numpy/scipy/pandas/matplotlib etc.), for machine&deep learning applications (sklearn&pytorch), for GUI development (PyQt). In addition, I am fluent in Matlab and R. I also create 3D images in POV-Ray, 2D drawings in Adobe Illustator, prepare documents in LaTeX, manage self-written liquid-helium management website (implemented in PHP/MySQL). In addition, I have an experience with MS Azure, MS Power BI, knowledge of C/C++/C#/Rust/Javascript/React.js.
My experimental expertise includes 3D printing, condensed matter phycics & molecular electronics, various microscopy techniques (SEM, TEM, STM, AFM), cleanroom experience, nanofabrication.
Vladyka, A. et al. Towards structural reconstruction from X-ray spectra Phys. Chem. Chem. Phys., 25, 6707-6713 (2023)
Vladyka, A. et al. In-situ formation of one-dimensional coordination polymers in molecular junctions. Nature Communications 10, 262 (2019)
Press-releaseVladyka, A. & Albrecht, T. Unsupervised classification of single-molecule data with autoencoders and transfer learning. Mach. Learn.: Sci. Technol. 1, 035013 (2020)