2014 IEEE Scientific Visualization Conference (SCIVIS)

The IEEE Scientific Visualization conference is soliciting papers on all topics in visualization and visual computing research. Besides the traditional scientific visualization research areas, we encourage submissions from related areas such as visual computing, machine learning, data analytics, data sciences etc. that will broaden the foundation of scientific visualization. We also welcome papers that showcase novel use of scientific visualization across the full range of application domains. Suggested topics for papers include, but are not limited to: Visualization, rendering, and manipulation of spatial data Scalar, vector and tensor fields, flow fields, regular and unstructured grids, point-based data, temporal data, volumetric data, topology-based and geometry-based techniques, PDEs, time-varying data, multidimensional multi-field, multi-modal, and multivariate data, streaming data, multi-resolution, compression. Interaction techniques and devices User interfaces, interaction design, coordinated and multiple views, data editing for validation, manipulation and deformation, multimodal input devices, haptics for visualization, mobile and ubiquitous visualization, visual interaction for data science and eScience. Data science and eScience Large-scale computing, storage and data analytics, distributed, cluster, and grid computing, scalable data management on and off the cloud, high-performance computing on multi-core, GPUs, FPGA, and embedded devices, information extraction and knowledge discovery from big data, petascale visualization, application of computer vision techniques, statistical modeling, data mining, visual steering for data retrieval. Display technologies Large and high-res displays, giga-pixel displays, wrist-displays, stereo displays, immersive and virtual environments, mixed and augmented visualization, projector-camera systems, perception and cognition coupled displays. Foundations Collaborative and distributed visualization, visual design and design studies, mathematical theories for visualization, scalability issues, uncertainty visualization, view-dependent visualization, information theoretic approaches, machine-learning, perception theory, color, texture, scene and motion perception, knowledge-assisted visualization. Evaluation Usability studies and task analysis, design and user studies, validation and verification visualization, statistical techniques, crowd-sourcing, human computation. Visual computing systems and methodologies System and toolkit design, glyph-based techniques, illustrative visualization, integrating spatial and non-spatial data visualization, applications of visual analytics approaches, computational steering. Visual computing applications Mathematics, physical sciences and engineering, earth, space, and environmental sciences, flow fields, terrain visualization, geographic/geospatial visualization, molecular, biomedical and medical visualization, bioinformatics visualization, software visualization, business and finance visualization, social and information sciences, education, humanities, for the masses, multimedia (image/video/music). Visual computing for emerging applications Nano-assembly, live cell imaging, imaging genetics, micro-biology, robotics, sensor networks, cybersecurity, and others.