KDD 2021 : 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
KDD 2021 : 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

KDD 2021 : 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Event Date: August 14, 2021 - August 18, 2021
Submission Deadline: February 08, 2021
Notification of Acceptance: May 17, 2021
Camera Ready Version Due: June 17, 2021




SIGKDD 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 which advance United Nations Sustainable Development Goals (SDGs) are encouraged.

Call for Papers

Call for Research Track Papers | SIGKDD 2021

August 14-18th, 2021, Singapore

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 which 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 miniscule. In each of these topics we seek submissions which highlight the trade-off between accuracy, stability, robustness and efficiency. Submissions which 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 the 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 which highlight and address technical and behavioral challenges during model deployment, feedback and upgradation.

Important Dates (Time: Anywhere on Earth)

Paper Submission: Feb 8th, 2021
Final Notification: May 17th, 2021
Camera-ready: June 17th, 2021
Presentation Slides (Required): July 12th, 2021
Conference: August 14-18, 2021

Submission Guidelines

SIGKDD 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:

Papers submitted to SIGKDD 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 reviewing. An exception to the rule are 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.

Conference Submission Website:

Program Chairs

Wynee Hsu, National University of Singapore

Sanjay Chawla, Qatar Computing Research Institute


KDD 2021 : 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining will take place in Singapore. It’s a 5 days event starting on Aug 14, 2021 (Saturday) and will be winded up on Aug 18, 2021 (Wednesday).

KDD 2021 falls under the following areas: DATA MINING, etc. Submissions for this Conference can be made by Feb 08, 2021. Authors can expect the result of submission by May 17, 2021. Upon acceptance, authors should submit the final version of the manuscript on or before Jun 17, 2021 to the official website of the Conference.

Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines.

Other Details of the KDD 2021

  • Short Name: KDD 2021
  • Full Name: 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
  • Timing: 09:00 AM-06:00 PM (expected)
  • Fees: Check the official website of KDD 2021
  • Event Type: Conference
  • Website Link:
  • Location/Address: Singapore

Credits and Sources

[1] KDD 2021 : 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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