After much consideration, the General Chairs, Executive Committee and Organizing Committee for KDD 2020 have decided to take the conference fully virtual. The events of the past few months and the continued safety concerns have led us to make this difficult decision. Our focus now is on creating a virtual conference with the vibrancy, excellence and sense of community that KDD has become known for.

In the coming weeks, the agenda and more programmatic details will be announced, watch the website and our social media posts. Our plans include all the tracks and content you’ve come to count on at KDD, and five exciting keynote speakers! Registration is now open, clear your calendar for August 23-27, 2020, and enjoy access to all the virtual content live and on demand the week of the event.

We’ll look forward to seeing you next year, when we will hope to be together in a safe manner. We are also pursuing a KDD 2023 Conference in San Diego, so that we can realize the hard work this committee has put in and showing the KDD community this beautiful location!

Until then, please spread the word, and feel free to drop us a note with any questions or virtual best practices that you’d like to share.

All the Best,

Rajesh Gupta and Yan Liu
KDD 2020 General Chairs

KDD Cup 2020 Call for Proposals

This Call for Proposals invites industrial or academic institutions to submit their proposals for organizing the 2020 KDD Cup competition. Since 1997, KDD Cup has been the premier annual Data Mining competition held in conjunction with the ACM SIGKDD conference on Knowledge Discovery and Data Mining.

Contact email:

Important dates:

  • February 21th, 2020 - Proposal submission deadline
  • March 11th, 2020 - Decision notification
  • March 18th, 2020 – Tentative start of the competition
  • May 20th, 2020 - Announcement of the KDD Cup Winner

SIGKDD-2020 will take place in San Diego. The KDD Cup competition is anticipated to last for 2-4 months, and the winners will be notified by mid-July 2020. The winners will be honored at the KDD conference opening ceremony and will present their solutions at the KDD Cup workshop during the conference.

We are looking for strong proposals that meet the following requirements: a novel and motivated goal, an interesting challenge and a broad outreach for the data science community, a rigid and fair setup, a challenging yet manageable task, and domain accessibility to the general public. A broad societal or business impact of the proposed problem is encouraged.

  1. A novel and motivated goal. Of particular interest are tasks that a novel to the data science community, representative of business problems, and call for novel approaches to solve them. Examples of challenging problems include incrementally arriving data and evaluation on the accumulated error; prediction given a limited amount of resources; learning with mostly unlabeled data; addressing cold-start issues in learning; learning over multiple types of data; hierarchical models on multiple sources of data with different levels of problem representation (high-volume low-level unstructured along with highly structured with pre-selected features), applications of deep learning models, etc.
  2. A rigid and fair setup. The organizers should guarantee the availability of the data and the confidentiality of the test set (to prevent information leakages at any cost). The evaluation metrics should be both meaningful for the application in-hand and statistically sound for the objective comparison. The baseline should be established to show that non-trivial results can be achieved. An estimate of what constitutes a significant difference in the performance will be much appreciated.
  3. A challenging yet manageable task. The task should be challenging in the sense that there is enough room for improvement from the basic solutions, and novel ideas are required to succeed in the competition. The task should be manageable in about 3 months’ time.
  4. Domain accessibility. The notions presented in the competition description should be accessible to the majority of machine learning and data mining practitioners who might not have an excessive domain knowledge or access to a powerful computational infrastructure.
  5. Proposal should cover all the important details such as dates, submission and evaluation of results, etc. and describe the competition rules clearly. As a rule of thumb, prepare a proposal as close as possible to the version you would publish on the competition’s Web-page.

In the proposal, we suggest to cover the following:

  1. How does the proposed challenge meet the five requirements?
  2. How does the proposed challenge address the two concerns?
  3. Which competition infrastructure do you plan to use (e.g., Kaggle, or on your own)? Is the competition platform you chose equally accessible to participants all over the world?
  4. What resources (including people, time, and award money) do you plan to invest?
  5. What is your time schedule for the competition?
  6. Is there any concern of the privacy about the released data? Have you obtained the rights to release the data for the competition from your legal counsels?
  7. What type of report, presentation, code do you require to submit for the final winning solutions?
  8. How would you handle Q&A and possible revisions during the competition?
  9. To which extent you have explored this problem and what is the baseline solution?
  10. Provide some data samples.
  11. How do you plan to promote the competition on your end?
  12. and also include:

13. Names, affiliations, email addresses, phone numbers, and short biographies of the organizers.

14. An endorsement letter from the executive-level management of your organization.

Please keep the proposal concise and strictly confidential. Please send your proposals in the PDF format to by February 21, 2020. Follow the updates provided on the Web-site.

How can we assist you?

We'll be updating the website as information becomes available. If you have a question that requires immediate attention, please feel free to contact us. Thank you!

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