The KDD 2018 organizing committee solicits proposals for full-day and half-day workshops to be held in conjunction with the main conference. The purpose of a workshop is to provide an opportunity for participants from academia, industry, government and other related parties to present and discuss novel ideas on current and emerging topics relevant to knowledge discovery and data mining. The workshops will be scheduled on August 19th, 2018.
Each workshop should be organized under a well-defined theme focusing on emerging research areas, challenging problems, and industrial/governmental applications. The goal of the workshops is to provide an informal forum to discuss important research questions and practical challenges in data mining and related areas. Novel ideas, controversial issues, open problems and comparisons of competing approaches are strongly encouraged as workshop topics. In particular, we would like to encourage organizers to avoid a mini-conference format by (i) encouraging the submission of position papers and extended abstracts, (ii) allowing plenty of time for discussions and debates, and (iii) organizing workshop panels.
Organizers have free control on the format, style, and the building blocks of the workshop. Possible contents of a workshop include but are not limited to invited talks, regular papers/posters, panels, and other pragmatic alternatives. In case workshop proposers need extra time to prepare their workshop, early decisions may be considered if justified.
Topics of Interest
Possible workshop topics include all areas of data mining and knowledge discovery, machine learning, statistics, and data and information sciences, but are not limited to these. Interdisciplinary workshops with applications of data mining and data sciences to various disciplines (such as health, medicine, biology, sustainability, ecology, social sciences, humanities, or aerospace) are of high interest. Workshops in emerging areas are also highly sought, examples including Internet of Things, causality, interpretable machine learning, fairness in machine learning, ethical and privacy aspects of data mining, MOOCs, fake news, computational social sciences, cost-sensitive learning and learning from imbalanced data, large scale computing, political data analysis, computational sustainability, interactive and visual analytics, and computational creativity.