Call for Research Track Papers
Call for Research Track Papers
Important Dates (Time: Anywhere on Earth)
KDD is a dual track conference hosting both a Research and an Applied Data Science track. A paper should either be submitted to the Research or the Applied Science track but not both. Research track submissions are limited to nine (9 pages), including references, must be in PDF and use ACM Conference Proceeding templates. An additional two pages of supplemental material focused on reproducibility can be provided. Additionally, proofs and pseudo-code that could not be included in the main nine-page manuscript may also be included in the two-page supplement. Template guidelines are here: https://www.acm.org/publications/proceedings-template.
- Paper Submission: Feb 8th, 2021
- Final Notification: May 17th, 2021
- Camera-ready: June 7th, 2021
- Video Presentation Recorded in SlidesLive: June 25th, 2021
- Video Presentation Ready for Upload to DL: July 1st, 2021
- Conference Date: August 14-18th, 2021
KDD is the premier Data Science conference. We invite original technical research contributions in all aspects of the data science lifecycle including but not limited to: data cleaning and preparation, data transformation, mining, inference, learning, explainability, data privacy, and dissemination of results. Technical data science contributions that advance United Nations Sustainable Development Goals (SDGs) are encouraged.
Data Cleaning and Preparation: A significant part of the data science lifecycle is spent on data cleaning and preparation. In several domains, data cleaning tasks continue to be rule-based and are often brittle, i.e., they break down in face of a constantly changing and evolving environment. Learning-based approaches for data cleaning and preparation which are generalizable and adaptive across domains are highly sought.
Data Transformation and Integration: The process of mapping data from one representation into another is at the heart of data science. The mapping can be query driven, based on a statistical task, or might involve integrating data from myriad sources. We seek original contributions that address the trade-off between the complexity of the transformation and algorithmic efficiency.
Mining, Inference, and Learning: These topics are the kernel of knowledge discovery from databases (KDD) paradigm and continue to witness massive growth. While classical aspects of supervised learning have been mainstreamed into the development cycle, new variations on unsupervised learning like self-supervision, few shot learning, prescriptive learning (reinforcement learning), transfer learning, meta learning, and representational learning are pushing the research boundary in a world where the proportion of labeled and annotated data is becoming minuscule. In each of these topics, we seek submissions that highlight the trade-off between accuracy, stability, robustness, and efficiency. Submissions that propose “new” inference tasks are strongly encouraged.
Explainability: As data science models are becoming part of daily human activity there is a need, often being expressed in law, that the models be fair, interpretable, and provide mechanisms to explain how a prediction or decision by the model was arrived at. Interpretable models will lead to their wider acceptance in society at large and increase the value of Data Science as a discipline in its own right.
Data Privacy and Ethics: Data privacy or lack thereof, continues to be the achilles heel of the whole data science enterprise. We seek technical contributions that advance the state of data science methods while guaranteeing individual privacy, respect for societal norms and ethical integrity.
Model Dissemination: Migrating a data science model from a research lab to a real-world deployment is non-trivial and potentially a continuous ongoing process. We seek research submissions that highlight and address technical and behavioral challenges during model deployment, feedback, and upgradation.
Papers submitted to KDD follow a double-blind review process. Every effort must be made to preserve the anonymity of the authors. Papers that have been presented as technical reports with listed authors will not be considered for review. An exception to the rule is the papers that have been submitted to arXiv at least one month prior to the deadline (January 8th, 2021). Authors can submit these papers but with a different title and abstract. Papers that appear in arXiv after Jan 8th, 2021 until the end of the review process will not be accepted. After the submission deadline, authors are not allowed to add additional authors to the submitted papers.
Conference Submission Website: https://cmt3.research.microsoft.com/KDD2021
Wynee Hsu, National University of Singapore
Sanjay Chawla, Qatar Computing Research Institute