BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Venue Radar//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:KDD 2026 Calendar
BEGIN:VEVENT
UID:kdd-2026-deadline-abstract@scientific-conference-calendar
DTSTAMP:19700101T000000Z
DTSTART:20260202T115900Z
DTEND:20260202T125900Z
SUMMARY:[Data Mining & Data Science][Machine Learning] KDD 2026 - Abstract 
 submission deadline
DESCRIPTION:Conference title: ACM SIGKDD Conference on Knowledge Discovery 
 and Data Mining\nWebsite: https://kdd2026.kdd.org/\nSource URL: https://k
 dd2026.kdd.org/research-track-call-for-papers/\nLocation: Jeju\, Korea\nT
 opics: Data Mining & Data Science\, Machine Learning\, Time Series & Sequ
 ential Data\, Federated & Distributed Learning\nSize: XL\nSubmission type
 : Paper-based / mixed tracks\nConfidence: confirmed\nLast checked date: 2
 026-08-20\nNotes: Official SIGKDD and KDD 2026 pages list conference date
 s and research-track Cycle 2 deadlines.\nScope: Knowledge discovery and d
 ata science\, centered on extracting useful structure from large\, hetero
 geneous data. Characteristic areas include clustering\, structured and re
 lational learning\, graph and text mining\, recommender systems\, scalabl
 e data analysis\, and responsible data use. Generative AI is relevant whe
 n it advances data science\, not simply because a paper uses an LLM.\nAdd
 itional topics: Deep Representation Learning\, Natural Language Processin
 g & LLMs\, Explainability & Interpretability\, Privacy-preserving ML\, ML
  Systems & Infrastructure\nScope sources (2027): https://kdd2027.kdd.org/
 research-track-call-for-papers/\nScope last checked: 2026-10-07
URL:https://kdd2026.kdd.org/research-track-call-for-papers/
END:VEVENT
BEGIN:VEVENT
UID:kdd-2026-deadline-full-paper@scientific-conference-calendar
DTSTAMP:19700101T000000Z
DTSTART:20260209T115900Z
DTEND:20260209T125900Z
SUMMARY:[Data Mining & Data Science][Machine Learning] KDD 2026 - Paper sub
 mission deadline
DESCRIPTION:Conference title: ACM SIGKDD Conference on Knowledge Discovery 
 and Data Mining\nWebsite: https://kdd2026.kdd.org/\nSource URL: https://k
 dd2026.kdd.org/research-track-call-for-papers/\nLocation: Jeju\, Korea\nT
 opics: Data Mining & Data Science\, Machine Learning\, Time Series & Sequ
 ential Data\, Federated & Distributed Learning\nSize: XL\nSubmission type
 : Paper-based / mixed tracks\nConfidence: confirmed\nLast checked date: 2
 026-08-20\nNotes: Official SIGKDD and KDD 2026 pages list conference date
 s and research-track Cycle 2 deadlines.\nScope: Knowledge discovery and d
 ata science\, centered on extracting useful structure from large\, hetero
 geneous data. Characteristic areas include clustering\, structured and re
 lational learning\, graph and text mining\, recommender systems\, scalabl
 e data analysis\, and responsible data use. Generative AI is relevant whe
 n it advances data science\, not simply because a paper uses an LLM.\nAdd
 itional topics: Deep Representation Learning\, Natural Language Processin
 g & LLMs\, Explainability & Interpretability\, Privacy-preserving ML\, ML
  Systems & Infrastructure\nScope sources (2027): https://kdd2027.kdd.org/
 research-track-call-for-papers/\nScope last checked: 2026-10-07
URL:https://kdd2026.kdd.org/research-track-call-for-papers/
END:VEVENT
BEGIN:VEVENT
UID:kdd-2026-conference@scientific-conference-calendar
DTSTAMP:19700101T000000Z
DTSTART;VALUE=DATE:20260809
DTEND;VALUE=DATE:20260814
SUMMARY:[Data Mining & Data Science][Machine Learning] KDD 2026 - Conferenc
 e
DESCRIPTION:Conference title: ACM SIGKDD Conference on Knowledge Discovery 
 and Data Mining\nWebsite: https://kdd2026.kdd.org/\nSource URL: https://w
 ww.kdd.org/conferences\nLocation: Jeju\, Korea\nTopics: Data Mining & Dat
 a Science\, Machine Learning\, Time Series & Sequential Data\, Federated 
 & Distributed Learning\nSize: XL\nSubmission type: Paper-based / mixed tr
 acks\nConfidence: confirmed\nLast checked date: 2026-08-20\nNotes: Offici
 al SIGKDD and KDD 2026 pages list conference dates and research-track Cyc
 le 2 deadlines.\nScope: Knowledge discovery and data science\, centered o
 n extracting useful structure from large\, heterogeneous data. Characteri
 stic areas include clustering\, structured and relational learning\, grap
 h and text mining\, recommender systems\, scalable data analysis\, and re
 sponsible data use. Generative AI is relevant when it advances data scien
 ce\, not simply because a paper uses an LLM.\nAdditional topics: Deep Rep
 resentation Learning\, Natural Language Processing & LLMs\, Explainabilit
 y & Interpretability\, Privacy-preserving ML\, ML Systems & Infrastructur
 e\nScope sources (2027): https://kdd2027.kdd.org/research-track-call-for-
 papers/\nScope last checked: 2026-10-07
URL:https://www.kdd.org/conferences
LOCATION:Jeju\, Korea
END:VEVENT
END:VCALENDAR