Call for Research Track Papers

Important Dates

  • Paper Submission: Feb 2, 2023
  • Author/Reviewer Interaction and Reviewer Discussion: April 6-27, 2023 
  • Final Notification: May 16, 2023
  • Camera-ready: June 4, 2023
  • Conference: August 6-10, 2023 

All deadlines are at 11:59 PM anytime in the world. 
Access the submission site at OpenReview.

Submission and Formatting Instructions

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 Track or the Applied Data Science Track but not both. Research Track submissions are limited to 9 pages, excluding references, must be in PDF and use ACM Conference Proceeding templates (two column format). Submissions to the Research Track are double blind (no author names should be listed). The recommended setting for Latex file of anonymous manuscript is:
\documentclass[sigconf, anonymous, review]{acmart}.
Additional supplemental material focused on reproducibility can be provided. Proofs, pseudo-code, and code may also be included in the supplement, which has no explicit page limit. As in previous years, the supplement should be included in the same file with the main manuscript. The supplement format could be either single column or double column. The paper should be self-contained, since reviewers are not required to read the supplement. Note that the supplement will not be included in the proceedings.

The Word template guideline can be found here:
The Latex/overleaf template guideline can be found here:

Research Track Aim and Scope

KDD is the premier Data Science conference. We invite submission of papers describing innovative research on all aspects of knowledge discovery and data science, ranging from theoretical foundations to novel models and algorithms for data science problems in science, business, medicine, and engineering. Visionary papers on new and emerging topics are also welcome, as are application-oriented papers that make innovative technical contributions to research. Topics of interest include, but are not limited to:

  • Data Science: Methods for analyzing scientific and business data, social networks, time series; mining sequences, streams, text, web, graphs, rules, patterns, logs data, IoT data, spatio-temporal data, biological data; recommender systems, computational advertising, multimedia, finance, bioinformatics.
  • Big Data: Large-scale systems for text and graph analysis, machine learning, optimization, sampling, parallel and distributed data science (cloud, map-reduce, federated learning), novel algorithmic and statistical techniques for big data, data cleaning and preparation that uses learning, algorithmically efficient data transformation and integration.
  • Foundations: Models and algorithms, asymptotic analysis; model selection, dimensionality reduction, relational/structured learning, matrix and tensor methods, probabilistic and statistical methods; deep learning, transfer learning, representation learning, meta learning, reinforcement learning; classification, clustering, regression, semi-supervised, self-supervised learning, few shot learning and unsupervised learning; personalization, security and privacy, visualization; fairness, interpretability, ethics and robustness.

Important policies

Authors are strongly recommended to review the following instructions.

PC Member/Reviewer Application

Those who are interested in serving as a Research Track PC Member/Reviewer are welcome, to that end, please fill an application using this form. The PC Co-chairs will review the application.

Review Contribution

All authors will be required to register as reviewers for KDD. Not all authors will be requested to provide reviews, but if an author is requested to provide up to 3 timely reviews for KDD and declines to do so when requested, their submission will be rejected.

Blinded Review and No Concurrent Submissions

Submitted papers must describe work that is substantively different from work that has already been published, or accepted for publication in an archival venue. Papers submitted to the KDD Research Track follow a double-blind review process. If a previous version of the paper was submitted to a non-archival venue, such as a workshop or to arXiv, the title and abstract must be changed in the KDD submission. KDD submissions must not be in concurrent submission to any archival conference or journal during the KDD review period. Papers that appear in arXiv after Jan 2th, 2023 until the end of the review process will not be accepted.


Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. Authors are strongly encouraged to make their code and data publicly available after the review process. Algorithms and resources used in a paper should be described as completely as possible to allow reproducibility. This includes model parameters, experimental methodology, empirical evaluations, and results. The reproducibility factor will play an important role in the assessment of each submission. Papers that do not have a clear reason for using publicly available data (such as the use of confidential patient data) should aim to provide simulated data that has the same properties as the dataset they are studying, and/or find publicly available datasets to test their approach.


Every person named as the author of a paper must have contributed substantially to the work described in the paper and/or to the writing of the paper. Every listed author must take responsibility for the entire content of a paper. Persons who do not meet these requirements may be acknowledged, but should not be listed as authors. Post-submission changes to the set of authors list are not allowed. Authorship may not be modified after the paper submission deadline.


Accepted papers will be published in the conference proceedings by ACM and also appear in the ACM Digital Library. The rights retained by authors who transfer copyright to ACM can be found here.

Official Publication Date

The official publication date is the date the proceedings are made available in the ACM Digital Library. This date for KDD 2023 is on or after July 15, 2023. The official publication date affects the deadline for any patent filings related to published work.


By submitting paper(s) to KDD 2023, the authors agree that the reviews and discussions may be made public for all accepted papers.


At least one author of each accepted paper must register for KDD to present their work in person.


Leman Akoglu (Carnegie Mellon University)
Dimitrios Gunopulos (National and Kapodistrian University of Athens)
Xifeng Yan (University of California at Santa Barbara)

Research Track PC Co-chairs of KDD-2023