Call for Workshop Proposals
Call for Workshop Proposals
The KDD 2022 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. It is a structured means for people with common interest to form communities.
Important Dates (Time: Anywhere on Earth)
Workshop Proposal Dates:
Each workshop should be organized under a well-defined theme focusing on emerging research areas, challenging problems, and/or industrial/governmental applications related to the broadly defined KDD community. 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 over the format, style, and 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 technical, analytical, social and behavioral perspectives of data and information sciences, but are not limited to these. Interdisciplinary workshops that explore the convergence of data mining and data sciences with various disciplines (such as health, pandemic responses, medicine, biology, sustainability, ecology, business, social sciences, economics, public policies, humanities, material science, industrial engineering, transportation, education, or aerospace) are of high interest. Workshops in emerging areas are also highly sought, examples including interpretable machine learning, machine learning for science, robustness of machine learning to adversarial attacks, Bayesian deep learning, fairness, privacy and ethical aspects of data mining and machine learning, computational social sciences, self-supervised learning, federated learning, machine learning and causal analysis, large scale computing, urban computing, political data analysis, dis/mis-information, Internet of Things, computational sustainability, interactive and visual analytics, and computational creativity. Or any other topic relevant to an appreciable fraction of the KDD community.
Organizers of accepted workshops are expected to announce the workshop and disseminate calls for papers, make and maintain the workshop website, gather submissions, conduct the reviewing process, and decide upon the final workshop program. They are also required to prepare workshop proceedings to be distributed online on a website maintained by the workshop organizers. Workshop organizers may choose to form organizing or program committees aiming to accomplish these tasks successfully.
Note: Workshop papers will not be archived in the ACM Digital Library. However, workshop organizers may set up any archived publication mechanism that best suits their workshop.
The proposal should contain the following information:
Hongning Wang, University of Virginia
Elena Zheleva, University of Illinois at Chicago
If you have any questions, please contact: firstname.lastname@example.org.