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IEEE VIS for data scientists and machine learning experts


  • Symposium - Visualizaton in Data Science (VDS) - The IEEE Symposium on Visualization in Data Science (VDS) brings together practitioners and researchers in data science and visualization to discuss practical issues and identify open research problems in the field of Data Science, the practice of deriving insight from data at the intersection of statistical methodology, computer science and a scientific domain

  • Tutorial - Visual Analytics for High-Dimensional Data


  • Tutorial - Vis+ML: Symbiosis of Visualization and Machine Learning

  • Workshop - Visual Analytics for Deep Learning (VADL 2017) - The workshop brings together researchers from both the machine learning and visual analytics fields and provides an opportunity to discuss and explore ways to harmonize the power of automated techniques and exploratory nature of interactive visualization.

  • Workshop - Data Systems for Interactive Analysis (DSIA) - DSIA fosters innovative research at the intersection of databases, machine learning, and interactive visualization

  • Symposium - Large Data Analysis and Visualization (LDAV) - The IEEE Symposium on Large Scale Data Analysis and Visualization (LDAV) brings together domain scientists, data analytics and visualization researchers, High Performance Computing (HPC) specialists, and database technology providers, fostering innovative solutions to problems unique to extremely large scale data.

  • Discovery Jam - A live hackathon to scientific data discovery. You’ll leave the workshop with skills for communicating with scientists, approaches to cross-disciplinary collaboration, and new ideas to pursue further.


  • Paper Session - High-dimensional Data - 8:30am-10:10am, Room: 301-C
    • LDSScanner: Exploratory Analysis of Low-Dimensional Structures in High-Dimensional Datasets
    • Pattern Trails: Visual Analysis of Pattern Transitions in Subspaces
    • SkyLens: Visual Analysis of Skyline on Multi-dimensional Data
    • Visualizing Big Data Outliers through Distributed Aggregation
    • The Subspace Voyager: Exploring High-Dimensional Data along a Continuum of Salient 3D Subspaces
  • Paper Session - ML1: Deep Learning - 10:30am-12:10pm, Room: 301-C
    • Analyzing the Training Processes of Deep Generative Models
    • Understanding Hidden Memories of Recurrent Neural Networks
    • ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
    • DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks
  • Paper Session - Text Analytics - 10:30am-12:10pm, Room: 207 LECTURE HALL
    • Survey on Visual Approaches for Analyzing Scientific Literature and Patents
    • A Metadata Collection about IEEE Visualization (VIS) Publications
    • ConceptVector: Text Visual Analytics via Interactive Lexicon Building using Word Embedding
    • PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models
    • Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework
  • Paper Session - Multidimensional Data - 2:00pm-3:40pm, Room: 301-D
    • Exploring Multivariate Event Sequences using Rules, Aggregations, and Selections
    • Skeleton-based Scagnostics
    • Keshif: Rapid and Expressive Tabular Data Exploration for Novices
    • Visual Exploration of Semantic Relationships in Neural Word Embeddings
    • Indexed-Points Parallel Coordinates Visualization of Multivariate Correlations


  • Paper Session - Text and Machine Learning - 8:30am-10:10am, Room: 301-D
    • LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks
  • Panel - How do Recent Machine Learning Advances Impact the Data Visualization Research

  • Paper Session - Interaction in the Analysis Process - 2:00pm-3:40pm, Room: 301-C
    • Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study
  • Paper Session - ML2: Cluster Analysis - 4:15pm-5:55pm, Room: 301-C
    • Visualizing Confidence in Cluster-based Ensemble Weather Forecast Analyses
    • SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance
    • Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics
    • Clustervision: Visual Supervision of Unsupervised Clustering


  • Paper Session - ML3: Classification - 8:30am-10:10am, Room: 102-ABC
    • Do Convolutional Neural Networks learn Class Hierarchy?
    • Visual Diagnosis of Tree Boosting Methods
    • A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations
    • TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees

Make sure to also check out the full program of the conference which can be found here: Depending on your specific domain and current data analysis challenges you will likely find more interesting events there!