Joanna Slawinska

Assistant Research Fellow

Email: joanna@pusan.ac.kr

Phone: +82-51-510-7751

Research Interests

  • Machine learning approaches for dynamical systems
  • modern data analysis and statistical modeling
  • Nonlinear dynamics and complex systems
  • Climate dynamics and its predictability
  • High-performance computing

Education

2011 Ph.D. University of Warsaw, Department of Physics

Work Experience

2011 2015 NewYork University
2015 2017 Rutgers University
2017 2020 University of Wisconsin-Milwaukee

Publications

  • Das, S., D. Giannakis, and J. Slawinska, 2020: Reproducing kernel Hilbert space compactification of unitary evolution groups. in revision. https://arxiv.org/abs/1808.01515
  • Wang, X., J. Slawinska, and D. Giannakis, 2020: Extended-range statistical ENSO prediction through operator-theoretic techniques for nonlinear dynamics. Scientific Reports. https://www.nature.com/articles/s41598-020-59128-7
  • Slawinska, J., Ourmazd, A., and D. Giannakis, 2019: A Quantum Mechanical Approach for Data Assimilation in Climate Dynamics. Proceedings of 36th International Conference on Machine Learning, Workshop on ‘Climate Change: How Can AI Help?.’ https://www.climatechange.ai/CameraReady/30/CameraReadySubmission/manuscript.pdf
  • Marrouch, N., J. Slawinska, D. Giannakis, and H. Read, 2019: Data-driven Koopman operator approach for computational neuroscience. Annals of Mathematics and Artificial Intelligence, Special Issue on Cognition and Neurocomputation, Ed. Dr. Alex Frid, Prof. Larry Manevitz, Prof. Bernardete Ribeiro. https://link.springer.com/article/10.1007%2Fs10472-019-09666-2
  • Giannakis, D., A. Ourmazd, J. Slawinska, and Z. Zhao, 2018: Spatiotemporal pattern extraction by spectral analysis of vector-valued observables. Journal of Nonlinear Science. https://link.springer.com/article/10.1007/s00332-019-09548-1
  • Slawinska, J., A. Ourmazd, and D. Giannakis, 2018: A new approach to signal processing of spatiotemporal data. Proceedings of 2018 IEEE Statistical Signal Processing Workshop, Freiburg im Breisgau, Germany. IEEE Xplore Digital Library, https://ieeexplore.ieee.org/document/8450704
  • Marrouch, N., H. Read, J. Slawinska, and D. Giannakis, 2018: Data-driven spectral decomposition of ECoG signal from an auditory oddball experiment in a marmoset monkey: Implications for EEG data in humans. IEEE World Congress on Computational Intelligence, The International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil. IEEE Xplore Digital Library, https://ieeexplore.ieee.org/abstract/document/8489475
  • Wang, X., D. Giannakis, and J. Slawinska, 2018: Antarctic Circumpolar Wave and Its Seasonality: Intrinsic Traveling Modes and ENSO Teleconnections. International Journal of Climatology. https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.5860
  • Giannakis, D., and J. Slawinska, 2018: Indo-Pacific Variability on Seasonal to Multidecadal Timescales. Part II: Multiscale Atmosphere-Ocean Interactions. J. Climate, 31, 693-725. https://journals.ametsoc.org/doi/full/10.1175/JCLI-D-17-0031.1
  • Slawinska, J., and A. Robock, 2018: Impact of Volcanic Eruptions on Decadal to Centennial Fluctuations of Arctic Sea Ice Extent during the Last Millennium and on Initiation of the Little Ice Age. \emph{J. Climate}, 31, 2145-2167.
  • Giannakis, D., J. Slawinska, A. Ourmazd, and Z. Zhao, 2017: Vector-Valued Spectral Analysis of Space-Time Data. Proceedings of 2017 Time Series Workshop, Neural Information Processing System Conference, Long Beach, CA. https://pdfs.semanticscholar.org/c851/294e146084a9130201209f83ee39a9ff4758.pdf
  • Zambri, B., A. LeGrande, A. Robock, and J. Slawinska, 2017: Winter Warming and Summer Monsoon Reduction after Volcanic Eruptions over the Last Millennium. Journal of Geophysical Research: Atmospheres, 122, 7971–7989. \href{https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017JD026728}{doi:10.1002/2017JD026728}.
  • Slawinska, J., and D. Giannakis, 2017: Indo-Pacific Variability on Seasonal to Multidecadal Timescales. Part I: Intrinsic SST Modes in Models and Observations. J. Climate, 30, 5265–5294. https://journals.ametsoc.org/doi/full/10.1175/JCLI-D-16-0176.1
  • Slawinska, J., E. Sz’ekely, and D. Giannakis, 2017: Data-driven Koopman analysis of tropical climate space-time variability. Proc. Workshop on Mining Big Data in Climate and Environment, 17th SIAM Int. Conf. on Data Mining, Houston, TX, American Statistical Association. https://sites.google.com/a/umn.edu/mbdce-2017/accepted-papers
  • Slawinska, J., and D. Giannakis, 2016: Spatiotemporal Pattern Extraction with Data-Driven Koopman Operators for Convectively Coupled Equatorial Waves. \emph{A. Banerjee, W. Ding, J. Dy, V. Lyubchich, A. Rhines (Eds.), Proceedings of the 6th International Workshop on Climate Informatics: CI 2016. NCAR Technical Note NCAR/TN-529+PROC, Sept 2016}, 49-52. https://opensky.ucar.edu/islandora/object/technotes:543
  • Slawinska, J., O. Pauluis, A. Majda, and W. W. Grabowski, 2016: Multiscale Interactions in an Idealized Walker Cell: Analysis with Isentropic Streamfunctions. J. Atmos. Sci., 73, 1187-1203.
  • Giannakis, D., J. Slawinska, and Z. Zhao, 2015: Spatiotemporal Feature Extraction with Data-Driven Koopman Operators. Journal of Machine Learning Research, Proceedings of the 1st International Workshop on ‘Feature Extraction: Modern Questions and Challenges’ and Neural Information Processing System Conference, 44, 103-115. http://proceedings.mlr.press/v44/giannakis15.html
  • Slawinska, J., O. Pauluis, A. Majda, and W. W. Grabowski, 2015: Multiscale Interactions in an Idealized Walker Cell: Simulations with Sparse Space–Time Superparameterization. Mon. Wea. Rev., 143, 563-580. https://journals.ametsoc.org/doi/full/10.1175/MWR-D-14-00082.1
  • Slawinska, J., O. Pauluis, A. Majda, and W. W. Grabowski, 2014: Multiscale Interactions in an Idealized Walker Circulation: Mean Circulation and Intraseasonal Variability. J. Atmos. Sci., 71, 953-971. https://journals.ametsoc.org/doi/full/10.1175/JAS-D-13-018.1
  • Pauluis, O., A. Mrowiec, J. Slawinska, and F. Zhang, 2013: Isentropic Analysis applied to Convection, Hurricanes and Walker Circulation. Proceedings for 6th Northeast Tropical Workshop.
  • Slawinska, J., W. W. Grabowski, H. Pawlowska, and H. Morrison, 2012: Droplet Activation and Mixing in Large-Eddy Simulation of a Shallow Cumulus Field. J. Atmos. Sci., 69, 444-462. https://journals.ametsoc.org/doi/full/10.1175/JAS-D-11-054.1
  • Grabowski, W. W., J. Slawinska, H. Pawlowska, and A. Wyszogrodzki, 2011: Macroscopic impacts of cloud and precipitation processes in shallow convection. Acta Geophysica, 56, 1184-1204. https://link.springer.com/article/10.2478/s11600-011-0038-9
  • van Zanten et al., 2010: Controls on precipitation and cloudiness in simulations of trade-wind cumulus as observed during RICO. J. Adv. Model. Earth Syst., 3, M06001. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2011MS000056
  • Slawinska, J., W. W. Grabowski, and H. Morrison, 2009: The impact of atmospheric aerosols on precipitation from deep organized convection: A prescribed flow-model study using double-moment bulk microphysics. Q. J. R. Meteorol. Soc., 644, 1906-1913. https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.450
  • Slawinska, J., W. W. Grabowski, H. Pawlowska, and A. A. Wyszogrodzki, 2008: Optical Properties of Shallow Convective Clouds Diagnosed from a Bulk-Microphysics Large-Eddy Simulation. J. Climate, 21, 1639–1647. https://journals.ametsoc.org/doi/abs/10.1175/2007JCLI1820.1
  • Dziembowski, W. A., and J. Slawinska, 2005: On the Nature of Regular Pulsation in Two LBV Stars of NGC 300. Acta Astronomica, 55, 195–204. http://acta.astrouw.edu.pl/Vol55/n2/a_55_2_3.html