| Type | Reviewing |
|---|---|
| Organisation | ACM SIGKDD (KDD conference) |
| Field | Machine learning engineers and Researchers and academics |
| Open to | Global |
| Cost | Free to apply. No fee appears on the programme's own pages; this is an absence of a fee rather than a quoted statement. |
| Cycle | Closed · Annual |
| Deadline | 2026 cycle closed (application reviews done by Jan 31, 2026) |
| Selection | Not stated |
| Evidence | Verified . 2 verbatim quotes from the programme's own pages are held on file. |
What this is
KDD 2026 public call for reviewers (Research Track) is a reviewing run by ACM SIGKDD (KDD conference). It is aimed at machine learning engineers and researchers and academics. Open to global. Free to apply. No fee appears on the programme's own pages; this is an absence of a fee rather than a quoted statement.
Who it is for
Researchers, scholars and practitioners; requires demonstrated expertise (at least 3 papers in KDD or similar ML/AI/data-science venues) and commitment to the review schedule.
Eligibility
Application form + expertise bar; 2026 cycle closed (reviewing Feb 19-Mar 19 2026; invites by Jan 31, 2026).
How you apply
self_apply
When it closes
Closed for the current cycle. It recurs annual. The next cycle is not dated on the source page.
Source note: confidence=0.95 next_occurrence=KDD 2027 reviewer call expected ~late 2026/early 2027
How competitive it is
On selectivity, the source says: Expertise filter (3+ venue papers) + PC needs
Getting in: open to approach.
Roughly 1 hours to prepare an application.
What it produces
If you take part, the documentation this tends to generate:
Potential profile relevance
The activity type each pathway's published criteria name, and nothing more than that. This is a description of an activity, not an assessment of eligibility, and it is not legal advice — see the disclosures.
| Pathway | Activity type | Why it is listed |
|---|---|---|
| O-1A | Peer review | This opportunity involves peer review. |
| EB-1A | Peer review | This opportunity involves peer review. |
| EB-2 NIW | Peer review | This opportunity involves peer review. |