Scope

For the research track, we invite submission of papers describing innovative research on all aspects of knowledge discovery and data science, ranging from theoretical foundations to novel models and algorithms for data science problems in science, business, medicine, and engineering. Visionary papers on new and emerging topics are also welcome, as are application-oriented papers that make innovative technical contributions to research.  Topics of interest include, but are not limited to:

    Data Science: Methods for analyzing social networks, time series, sequences, streams, text, web, graphs, rules, patterns, logs, IoT data, spatio-temporal data, biological data, scientific and business data; recommender systems, computational advertising, multimedia, finance, bioinformatics.
    Big Data: Large-scale systems for data analysis, machine learning, optimization, sampling, summarization; parallel and distributed data science (cloud, map-reduce, federated learning); novel algorithmic and statistical techniques for big data; algorithmically-efficient data transformation and integration.
    Foundations: Models and algorithms, asymptotic analysis; model selection, dimensionality reduction, relational/structured learning, matrix and tensor methods, probabilistic and statistical methods; deep learning, transfer learning, representation learning, meta learning, reinforcement learning; classification, clustering, regression, semi-supervised learning, self-supervised learning, few shot learning and unsupervised learning; personalization, security and privacy, visualization; fairness, interpretability, ethics and robustness.