Topics include:

IEEE Conference on Visual Analytics Science & Technology (VAST)

• Individual and collaborative reasoning including cognition and perception, analytic discourse, knowledge discovery, creativity and expertise, and operational, ethical, and value-based decision-making using interactive visualization systems
• Integration of data analysis, interaction, and visualization, including the use of machine learning, artificial intelligence, and deep learning techniques to support interactive analysis.
• Visual representations and interaction techniques including the principles for depicting information, new visual paradigms, statistical graphics, geospatial visualizations, the science of interaction, and approaches for generating visual analytic visualization and interactions
• Data management and knowledge representation including scalable data representations for high volume and stream data, statistical and semantic signatures, and synthesis of information from diverse data sources
• Presentation, production, and dissemination methods including methods and tools for capturing the analytics process, methods for elicitation of stakeholder constraints, priorities & processes for incorporation in analysis, and storytelling for specific and varying audiences
• Applications of visual analysis techniques, including but not limited to applications in science, engineering, humanities, business, public safety, commerce, and logistics as far as they contribute to visual analytics are of particular interest
• Explainable AI and trust in machine learning and automation, including the design and use of novel visual and interactive techniques that help users to understand, appropriately trust, and effectively manage artificially intelligent machine partners
• Evaluation methods, including ethical analysis, privacy, security, & regulatory compliance, interoperability, and application practice & experience
• Devices and technologies which are fundamental for visual analytics, including user and device adaptivity, web interfaces and mobile or other novel devices

IEEE Conference on Information Visualization (InfoVis)

• Visual encoding and interactive visualization techniques for a broad range of data types, such as: causality and uncertainty data; graphs (networks), trees (hierarchies), and other relational data; heterogeneous data; high-dimensional and multivariate data; non-numeric data (categorical data, nominal data, etc.); streaming or time-varying data; text and documents; time-series & temporal event data; geospatial data
• Interaction techniques for supporting the data analysis process, such as: focus + context and overview + detail methods; zooming, navigation, and distortion techniques; brushing and linking; coordinated multiple views; data labeling, editing, and annotation
• Visualization using different modalities and devices, such as: mobile and ubiquitous devices; large displays; post-WIMP interactions (pen, touch, speech, gestures, mixed reality, etc.); immersive environments; augmented reality; physicalization
• Visualization fundamentals and methodologies, such as: cognition and perception; visual design and aesthetics; taxonomies and models; research methods, methodologies, and frameworks; task and requirements analysis; metrics and benchmarks; broad range of evaluation approaches that include quantitative, qualitative, and replication studies; novel algorithms
• Application of visualization to a variety of contexts, such as: provenance tracking; storytelling; education and teaching; visualization toolkit design; very large data; non-expert audiences; personal and social; specific application domains like biology, sports, digital humanities, finance, etc; visual data mining and visual knowledge discovery; museums and public environments; situated visualization

IEEE Conference on Scientific Visualization (SciVis)

• Visualization, rendering, and manipulation of spatial data: scalar, vector, and tensor fields; multidimensional, multi-field, multi-modal, and multivariate data; time-varying data; regular and unstructured grids; point-based data; and volumetric data
• Foundations: visualization taxonomies and models; mathematical theories for visualization; perception (theory, color, texture, scene, motion); cognition; aesthetics; information theoretic approaches; knowledge-assisted visualization; presentation; production; dissemination; and visual design
• Systems and methodologies: system and toolkit design; topology-based and geometry-based techniques; feature extraction and pattern analysis; glyph-based, texture-based, and pixel-oriented techniques; uncertainty, view-dependent, and illustrative visualization; visual storytelling; computational steering; sonification; collaborative and distributed visualization; and integrating spatial and non-spatial data visualization
• Large data visualization: parallel, distributed, cluster, and grid computing; high-performance computing on multi-core, GPU, FPGA, and embedded devices; petascale and exascale visualization; scalability; visualization over networks; compression; multi-resolution techniques; and streamingdata
• Data science: scalable data management on and off the cloud; storage and data analytics; information extraction and knowledge discovery from big data; statistical modeling; data mining; machine learning, including deep learning; clustering techniques; application of computer vision techniques; and visual steering for data retrieval
• Interaction: human-computer interaction for visualization; interaction design; coordinated multiple views; brushing & linking; focus & context; zooming and navigation; data editing, manipulation, and deformation; guided visualization; multimodal input devices; haptics for visualization; mobile and ubiquitous visualization; and interaction with visualizations in different display environments
• Display techniques: large and high-resolution displays; gigapixel displays; small displays; mobile devices; wrist and wearable displays; stereo displays; immersive and virtual environments; mixed and augmented visualization; and projector-camera systems
• Evaluation and user studies: task and requirements analysis; metrics and benchmarks; qualitative evaluation; quantitative evaluation; laboratory studies; eye tracking studies and studies with other physiological sensors; field studies; usability studies; design studies; validation and verification; crowdsourcing; and human computation
• Application areas of visualization: mathematics; physical sciences and engineering; earth, space, and environmental sciences; urban science; business and finance; social and information sciences; education; humanities; multimedia (image/video/music); robotics; sensor networks; cybersecurity; visualization for visualization research; visualization for the masses; terrain visualization; geographic/geospatial visualization; software visualization; bioinformatics; and molecular, biomedical, and medical visualization