ACM SIGKDD International Conference on Knowledge discovery and data mining(SIGKDD 2020)

August 22-27, 2020, San Diego, United States

Scope & Topics

The ACM SIGKDD International Conference on Knowledge discovery and data mining(SIGKDD 2020)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 24th, 2020.

Topics of interest include, but are not limited to, the following

• Data protection
• Design of experiments
• Interpretable models
• Addressing bias in deployed systems
• Addressing vulnerabilities in on-field deployment
• Improving reliability of deployed systems
• Privacy-sensitive applications of learning systems
• Ethical consideration in applications
• Applications involving explainable aspects of algorithms
• Validation and verification approaches for learning systems
• Applications that support broader goals on sustainability

Submission of Papers

KDD is a dual track conference hosting both a Research track and an Applied Data Science track. Due to the large number of submissions, papers submitted to the Research track will not be considered for publication in the Applied Data Science track and vice versa. Authors are encouraged to read the track descriptions carefully and to choose an appropriate track for their submissions. Following KDD conference tradition, reviews are not double-blind, and author names and affiliations should be listed.KDD is a dual track conference hosting both a Research track and an Applied Data Science track. Due to the large number of submissions, papers submitted to the Research track will not be considered for publication in the Applied Data Science track and vice versa. Authors are encouraged to read the track descriptions carefully and to choose an appropriate track for their submissions. Following KDD conference tradition, reviews are not double-blind, and author names and affiliations should be listed.

Important Dates