Tensor Analytics for Emerging Applications @ IEEE DSAA 2021 6-9 October 2021, Online

About the Special Session

Tensor decompositions are the “Swiss knife” of data science, data mining, machine learning and signal processing. They can be used for a variety of problems such as factor analysis; co-clustering; outlier/anomaly detection; and missing value estimation and interpolation; link prediction in time-evolving networks; multivariate time series forecasting; feature extraction in classification, as well as compression/de-noising and pattern recognition in signal processing. The recent research shows that tensor-based methods beat almost all non-tensor-based methods in a wide range of real-world applications. If we want to name a single tool, equivalent to deep learning but in the unsupervised setting that tool is perhaps tensor decompositions. Even, recently it is shown that tensor decompositions have applications in compressing the parameter space of deep learning models. The main reason behind this versatility is that the natural structure of many real-world datasets is highly multi-way in many applications and tensor decompositions are capable of capturing those complex interactions across various ways of data and thus provide a better and more natural model.

The Special session on “Tensor Analytics for Emerging Applications” aims at bringing together computer scientists, data scientists, and domain experts from various application areas to discuss the recent advances in algorithms, models, scalable solutions, and real-life applications. We expect diverse submissions spanning and integrating fields such as healthcare (e.g., analysis of COVID-19 data and phenotyping), industry (e.g., IoT and sensors), internet platforms (e.g., recommender systems and time-evolving social network analysis), transportation (e.g. interpolation of traffic data and analysis of spatio-temporal data), neuroscience (e.g., modelling EEG and fMRI imaging data), and remote sensing (e.g., modelling hyperspectral images).

Important Dates

Paper Submission: June 6, 2021

Paper Notification: July 25, 2021

Paper Camera Ready Due: August 8, 2021

Topics of interest

We invite submissions that report progress in either theoretical, technical or application aspects of Tensor Analytics. The topics include, but are not limited to the following:

  • New Models for Tensor Decompositions
  • New Fitting Algorithms for Tensor Decomposition Models
  • New Fitting Algorithms for Constrained (e.g., non-negative) Tensor Decompositions
  • New Models for Coupled Tensor/Matrix Decompositions
  • New Efficient Algorithms for Sparse Tensors
  • Bayesian/Probabilistic Models
  • Tensor Network
  • Distributed, Parallel and GPU-based solutions for tensor decompositions
  • Sketching Solutions for Tensor Decompositions
  • Incremental/Streaming/Multi-Aspect-Streaming Methods for Tensor Analysis
  • Model and Model Order Selection for Tensors
  • Time-aware Tensor Decompositions
  • Space-time Aware Tensor Decompositions
  • Simulation of Synthetic and Semi-realistic Tensor Data
  • Benchmarking Studies
  • Tensor Decompositions in Deep Learning
  • Tensor-based Feature Extraction for Classification
  • Tensor-based Anomaly Detection
  • Tensor-based Recommender Systems
  • Tensor-based Social Network Analysis
  • Tensor-based Time Series Analysis and Forecasting
  • Tensor-based Data Fusion
  • Tensor-based Embeddings (e.g., RESCAL for knowledge graphs)
  • Tensor Decompositions in Data Interpolation and Missing Value Estimation
  • Applications in Emerging Healthcare Problems (e.g., COVID-19)
  • Applications in Industry (e.g., IoT and Sensors)
  • Applications in Medicine
  • Applications in Neuroscience
  • Applications in Remote sensing
  • Applications in Transportation
  • Applications in Biology
  • Applications in Physics
  • Real-life Case Studies
  • Software Packages for Tensor Decompositions





Program Committee Members


  • Panos Markopoulos, Assistant Professor, Rochester Institute of Technology, USA

  • Kijung Shin, Assistant Professor, KIST, South Korea

  • Xiao Fu, Assistant Professor, Oregon State University, USA

  • Shaden Smith, Senior Research SDE, Microsoft, USA

  • Joyce C Ho, Assistant Professor, Emory University, USA

  • Kejun Huang, Assistant Professor, University of Florida, USA

  • Evangelos E. Papalexakis, Assistant Professor, University of California Riverside, USA

  • Hadi Fanaee-T, Assistant Professor, Halmstad University, Sweden

  • Maryam Amoozegar, Assistant Professor, Kerman Graduate University of Technology, Iran

  • Sofia Fernandes, Postdoc, University of Aveiro, Portugal

  • Mehran Yazdi, Professor, Shiraz University, Iran

  • Mansoor Rezghi, Associate Professor, Tarbiat Modares University , Iran

  • Farnaz Sedighin, Research Scientist, Skoltech, Russia

  • Leila Sorkhabi, Azad University, Iran