KDD 2027 — ACM SIGKDD Conference on Knowledge Discovery and Data Mining

KDD 2027, the 33rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, runs 1-5 August 2027 at the San Jose McEnery Convention Center in San Jose, California. It is the flagship research conference of ACM's Special Interest Group on Knowledge Discovery and Data Mining, and the venue where industrial data-science teams publish alongside academia — the Applied Data Science track exists specifically for deployed systems. The 2027 edition runs four submission categories: Research, Applied Data Science, Datasets and Benchmarks, and a separate AI for Sciences track. Research-track topics span foundations of knowledge discovery, modern AI and big data (deep learning, large language models, generative models, reinforcement learning, neural-symbolic integration), trustworthy and responsible data science, systems for scalable AI, and applications. The first submission cycle closed 26 July 2026, with a two-week author rebuttal from 29 September and decisions on 14 November 2026.

• Author rebuttal period opens (first cycle)Sep 29, 2026
• Author rebuttal period closes (first cycle)Oct 13, 2026
• Notification of decisions (first cycle)Nov 14, 2026

Date

August 1–5, 2027

Location

San Jose McEnery Convention Center, California

Format

In-Person

Event Type

Conference

Status

Upcoming

Pricing

Paid

Organizer

ACM SIGKDD

Added

Aug 3, 2026

Updated

Aug 3, 2026

• ✓Chief data officers and VPs of data
• ✓Applied data scientists and ML engineers
• ✓Recommender systems and search relevance teams
• ✓ML platform and infrastructure leads
• ✓Responsible AI, privacy and fairness practitioners
• ✓Academic data mining researchers
• ✓PhD students in data science and machine learning

KDD is the one top-tier venue where the Applied Data Science track means what it says: papers describe systems running in production at scale, with the failure modes and infrastructure costs included. For a CDO or VP of data, that makes the ADS and Systems for Scalable AI sessions a direct read on which recommender, forecasting, fraud-detection and LLM-serving architectures actually survive contact with real traffic. The 2027 edition returns to San Jose, so the exhibition and recruiting floor sit inside Silicon Valley. Acceptance has run near 20 percent against roughly 2,000 submissions, so what reaches the stage has been filtered hard.

• ✓Research Track covering foundations, modern AI and big data, trustworthy data science, scalable systems and applications
• ✓Applied Data Science (ADS) Track for deployed production systems
• ✓Datasets and Benchmarks Track
• ✓AI for Sciences Track, separated from general applied submissions
• ✓Research-track topic area: Modern AI and Big Data — deep learning, large language models, generative models, reinforcement learning and neural-symbolic integration
• ✓Research-track topic area: Trustworthy and Responsible Data Science — privacy-preserving methods, fairness, interpretability, adversarial robustness and ethical frameworks
• ✓Research-track topic area: Systems for Data Science and Scalable AI — distributed computing, federated learning and large-scale infrastructure
• ✓Two-cycle submission model with a formal two-week author rebuttal period

• ★The Applied Data Science track means what it says — papers describe systems running in production at scale, with failure modes and infrastructure costs included, which is rare among top-tier venues
• ★Historically selective: roughly 20% acceptance in 2024 against 2,046 submissions, with an average near 16.5% across 2000-2024
• ★A new dedicated AI for Sciences track separates scientific-discovery work from general applied submissions
• ★Four parallel submission categories — Research, Applied Data Science, Datasets and Benchmarks, and AI for Sciences — so evaluation work is judged on its own terms rather than against algorithm papers
• ★The 33rd edition returns to Silicon Valley, putting the exhibition and recruiting floor inside the largest concentration of data-platform vendors

KDD 2027 registration rates are not published yet. As of August 2026 the organisers have released the four track calls for papers and the first-cycle review calendar, but no pass tiers, early-bird cutoff, ACM-member rate or student rate. KDD typically publishes rates in the spring before the August conference.

When is KDD 2027?

KDD 2027 takes place August 1–5, 2027 in San Jose, California.

Where is KDD 2027 held?

KDD 2027 is held in San Jose, California. The event format is in-person.

What is the KDD 2027 submission deadline?

The next KDD 2027 deadline is author rebuttal period opens (first cycle) on Sep 29, 2026. Remaining dates: Author rebuttal period closes (first cycle) — Oct 13, 2026; Notification of decisions (first cycle) — Nov 14, 2026.

How much does KDD 2027 cost?

KDD 2027 is paid. KDD 2027 registration rates are not published yet. As of August 2026 the organisers have released the four track calls for papers and the first-cycle review calendar, but no pass tiers, early-bird cutoff, ACM-member rate or student rate. KDD typically publishes rates in the spring before the August conference.

Who should attend KDD 2027?

KDD 2027 is aimed at Chief data officers and VPs of data, Applied data scientists and ML engineers, Recommender systems and search relevance teams, ML platform and infrastructure leads, Responsible AI, privacy and fairness practitioners, Academic data mining researchers, PhD students in data science and machine learning.

What topics does KDD 2027 cover?

KDD 2027 covers Data Mining, Applied Data Science, Large Language Models, Recommender Systems, Trustworthy and Responsible AI, Federated Learning, Graph Learning, Time Series Analysis, Scalable ML Systems, AI for Science.

This page was written from 3 sources, 1 on domains other than kdd2027.kdd.org.

1. 1.kdd2027.kdd.org — kdd2027.kdd.orgvendor
2. 2.kdd2027.kdd.org — research track call for papersvendor
3. 3.myhuiban.com — 136