Juraj Pálenik


PhD student

Visual analytics

 Team Hauser

Juraj is a PhD candidate in the Visualization group at UiB's Institute of Informatics. He holds a MSc in Computer Graphics and BSc in Physics from the Masaryk University in Brno. He is working on applying mathematical methods in scientific visualization, in particular the use of implicit surfaces in visual parameter space analysis. In his collaboration with the Geophysical Institute he is working on developing and validating a model of atmospheric convection. He is also a member of the gLidar project (glidar-project.github.io) aimed at multimodal observation and analysis of atmospheric convection using paragliders and Light Detection and Ranging (LIDAR) systems.



    [PDF] [DOI] [Bibtex]
    author={P\'{a}lenik, Juraj and Spengler, Thomas and Hauser, Helwig},
    journal={IEEE Transactions on Visualization and Computer Graphics},
    title={{IsoTrotter: Visually Guided Emprical Modelling of Atmospheric Convection}},
    abstract={Empirical models, fitted to data from observations, are often used in natural sciences to describe physical behaviour and support discoveries. However, with more complex models, the regression of parameters quickly becomes insufficient, requiring a visual parameter space analysis to understand and optimize the models. In this work, we present a design study for building a model describing atmospheric convection. We present a mixed-initiative approach to visually guided modelling, integrating an interactive visual parameter space analysis with partial automatic parameter optimization. Our approach includes a new, semi-automatic technique called IsoTrotting, where we optimize the procedure by navigating along isocontours of the model. We evaluate the model with unique observational data of atmospheric convection based on flight trajectories of paragliders.},


    [PDF] [DOI] [Bibtex]
    author={J. P\'{a}lenik and J. By\v{s}ka and S. Bruckner and H. Hauser},
    journal={IEEE Transactions on Visualization and Computer Graphics},
    title={Scale-Space Splatting: Reforming Spacetime for Cross-Scale Exploration of Integral Measures in Molecular Dynamics},
    keywords={Data visualization;Computational modeling;Time series analysis;Atmospheric measurements;Particle measurements;Analytical models;Kernel;Scale space;time-series;scientific simulation;multi-scale analysis;space-time cube;molecular dynamics},
    pdf = "pdfs/scale-space-splatting.pdf",
    images = "images/scale-space-teaser.png",
    thumbnails = "images/scale-space-teaser-thumb.png",
    abstract = "Understanding large amounts of spatiotemporal data from particle-based simulations, such as molecular dynamics, often relies on the computation and analysis of aggregate measures. These, however, by virtue of aggregation, hide structural information about the space/time localization of the studied phenomena. This leads to degenerate cases where the measures fail to capture distinct behaviour. In order to drill into these aggregate values, we propose a multi-scale visual exploration technique. Our novel representation, based on partial domain aggregation, enables the construction of a continuous scale-space for discrete datasets and the simultaneous exploration of scales in both space and time. We link these two scale-spaces in a scale-space space-time cube and model linked views as orthogonal slices through this cube, thus enabling the rapid identification of spatio-temporal patterns at multiple scales. To demonstrate the effectiveness of our approach, we showcase an advanced exploration of a protein-ligand simulation.",