Bio
Justyna Zwolak is an adjunct assistant professor in the Institute for Advanced Computer Studies (UMIACS). She is also a Mathematician at NIST and a QuICS Affiliate Fellow. Her research focuses on using machine learning algorithms and artificial intelligence, especially deep convolutional neural networks, in quantum computing platforms. In particular, she is investigating methods to automatically identify stable configurations of electron spins in semiconductor-based quantum computing. She is also developing a complete software suite that enables modeling of quantum dot devices, train recognition networks, and -- through mathematical optimization -- auto-tune experimental setups.
Justyna received an M.Sc. in Mathematics from The Faculty of Mathematics and Informatics, Nicolaus Copernicus University, and a Ph.D. in Physics from the Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, in Toruń, Poland.
Recent Publications
Machine-learning enhanced dark soliton detection in Bose-Einstein condensates
, , Mach. Learn.-Sci. Technol., 2, (2021)Ray-based classification framework for high-dimensional data
, , Proceedings of the Machine Learning and the Physical Sciences Workshop at NeurIPS 2020, Vancouver, Canada, (2020)Practitioner s guide to social network analysis: Examining physics anxiety in an active-learning setting
, , Phys. Rev. Phys. Educ. Res., 15, 020105, (2019)
Related Events
- April 7, 2022 10:00 amQuICS Special Seminar
Tuning arrays with rays: Physics-informed tuning of quantum dot charge states
Justyna Zwolak(NIST)