SIGKDD 2027: Dates, Venue, Tracks & Deadlines
August 1-5, 2027
TL;DR: SIGKDD 2027, the 33rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, runs August 1-5, 2027, at the San Jose McEnery Convention Center in San Jose, California. The four submission tracks...
TL;DR: SIGKDD 2027, the 33rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, runs August 1-5, 2027, at the San Jose McEnery Convention Center in San Jose, California. The four submission tracks (Research, Applied Data Science, Datasets and Benchmarks, and AI for Sciences) already have confirmed program co-chairs and a first-cycle paper deadline of July 26, 2026, but keynote speakers, the full agenda, and registration pricing have not been published yet. This guide covers exactly what’s confirmed, what’s still TBA, and what past editions suggest about what to expect.
Most “event guide” posts you find for a conference this far out are aggregator noise: scraped dates, a stock photo of a convention hall, and a “read more at the official site” link that does the actual work for you. That’s not useful if you’re trying to decide whether to block calendar time or start drafting a paper.
I track AI and data science conferences the same way I track AI tool deals: by checking primary sources before I write a word, not by repeating whatever an aggregator posted first. For SIGKDD 2027, I went straight to the conference’s own site, `kdd2027.kdd.org`, and its four track-specific call-for-papers pages, cross-checked against the SIGKDD parent organization’s conference list. What follows is what’s actually published as of this writing, what’s genuinely still unknown, and why that gap matters if you’re planning around this event.
Key Takeaways
- Dates and venue are locked in. SIGKDD 2027 runs August 1-5, 2027, at the San Jose McEnery Convention Center in San Jose, California, confirmed directly on the conference’s official homepage.
- This is the 33rd edition of a conference running since 1995. SIGKDD became an official ACM Special Interest Group in 1998, and the annual conference has run continuously since 1995, making it one of the longest-running data mining research events in the world.
- Four tracks, four sets of named co-chairs, one confirmed deadline. Research, Applied Data Science, Datasets and Benchmarks, and AI for Sciences each have a published call for papers with a first-cycle paper deadline of July 26, 2026, and real, named program co-chairs from institutions like Texas A&M, LinkedIn, and Carnegie Mellon.
- Keynotes, full agenda, and registration pricing are not public yet. As of this writing, no keynote speakers, session schedule, or 2027 registration fees appear anywhere on the official site. Anyone quoting specific ticket prices for 2027 right now is guessing.
- Historically, this is a selective and expensive conference. Past editions accepted roughly 15-20% of research submissions and charged $1,000-$1,750 for full registration, so budgeting and planning ahead matters more here than at a typical vendor conference.
A conference this far out will always have more unknowns than knowns. The honest move is to report exactly which is which, not to pad the gaps with confident-sounding guesses.
What Is SIGKDD 2027?
SIGKDD 2027 is the 33rd annual ACM SIGKDD Conference on Knowledge Discovery and Data Mining, the flagship research conference of ACM’s Special Interest Group on Knowledge Discovery and Data Mining. It’s widely regarded as one of the most influential venues in the world for data mining, machine learning, and applied AI research, holding the top A* rating from CORE (the independent Computing Research and Education ranking body).
The conference has run every year since 1995, growing out of KDD workshops that Gregory Piatetsky-Shapiro started at AAAI conferences in the late 1980s and early 1990s. SIGKDD itself became a formal ACM Special Interest Group in 1998. Between 1994 and 2015 alone, the conference published roughly 4,489 papers that went on to generate more than 112,570 citations, a scale of academic influence that few applied AI conferences can match.
Unlike a vendor trade show, SIGKDD is peer-reviewed and research-first. Papers go through a formal submission and rebuttal process, and historically only around 15-20% of submissions get accepted (the 2014 edition, for example, accepted 151 of over 1,000 submissions, a 14.6% acceptance rate). That selectivity is exactly why the conference draws serious researchers and applied data science teams rather than a general “AI curious” crowd.
For 2027, the conference has published four submission tracks with real names attached to each, even though the broader program (keynotes, workshops, tutorials, and the detailed daily schedule) hasn’t been announced yet. That’s normal for an event this far in the future. The call-for-papers infrastructure always goes live more than a year ahead of the conference dates because researchers need that runway to write, submit, and revise work.
When and Where Is SIGKDD 2027?
SIGKDD 2027 takes place August 1-5, 2027, at the San Jose McEnery Convention Center in San Jose, California, in the heart of Silicon Valley. Both the dates and venue are stated directly on the conference’s official homepage at `kdd2027.kdd.org`, so this part isn’t a guess or an aggregator estimate.
San Jose is a logical choice for a conference this deep into applied AI and data science. It sits inside Silicon Valley, within reach of the research labs and applied AI teams at Google, Meta, Apple, and dozens of AI-native startups that regularly publish at this conference. International attendees typically fly into San Jose Mineta International Airport or San Francisco International Airport, both a short ride from the convention center.
Compare that to the two prior editions: KDD 2025 was held at the Metro Toronto Convention Centre in Toronto, Canada (August 3-7, 2025), and KDD 2026 moves to Jeju, Korea (August 9-13, 2026). SIGKDD rotates its host city and continent nearly every year, which is part of why checking the current, conference-specific site instead of relying on last year’s information matters so much here.
Quick facts:
| Detail | Information |
|---|---|
| Event | SIGKDD 2027 (33rd ACM SIGKDD Conference) |
| Dates | August 1-5, 2027 |
| Venue | San Jose McEnery Convention Center, San Jose, California, USA |
| Format | In-person research conference (format details for 2027 not yet published) |
| Tracks | Research, Applied Data Science, Datasets and Benchmarks, AI for Sciences |
| First-cycle paper deadline | July 26, 2026 |
| Notification date | November 14, 2026 |
| Official site | kdd2027.kdd.org |
Who Should Attend SIGKDD 2027?
SIGKDD is built for people who do or apply data mining and machine learning research, not for a general business audience looking for a high-level AI trends overview. The four tracks make that audience explicit: academic researchers submitting to the Research Track, applied scientists and engineers submitting to the Applied Data Science Track, teams releasing new benchmark data through the Datasets and Benchmarks Track, and interdisciplinary scientists working at the intersection of AI and other fields through the AI for Sciences Track.
You’ll get real value from this conference if you fit one of these profiles:
- Academic researchers and PhD students working on core data mining, machine learning, or AI methods who want to publish in an A*-rated, highly cited venue.
- Applied data scientists and ML engineers at large tech companies with a deployed system and real post-launch performance data, since the Applied Data Science Track specifically requires quantified in-production results, not just a proof of concept.
- Domain scientists in physical sciences, biomedical research, or environmental studies working with AI-driven methods, who the new AI for Sciences Track is built to reach.
- Teams building and releasing benchmark datasets that the broader data mining community can adopt and cite.
- Corporate research and applied AI leads scouting talent and technique two conference cycles ahead of when it becomes mainstream product practice.
Here’s the honest caveat. If you’re looking for a business-facing AI conference with product demos, vendor booths, and a track built for buyers rather than researchers, SIGKDD isn’t that event. This is a peer-reviewed academic and applied-research conference where the bar for entry (specifically for presenting) is a rigorous review process, not a ticket purchase.
When you first look at the SIGKDD 2027 call for papers, the question often isn’t whether to attend. It’s which track to submit to. Say your team has a fraud model that has been in production for 14 months with solid post-launch metrics: that makes it a stronger fit for the Applied Data Science Track (built exactly for deployed systems with real performance data) than the Research Track, which prioritizes methodological novelty over production results. That distinction, matching your actual work to the right track’s evaluation criteria, is worth working through months before a submission deadline, not the week before.
SIGKDD 2027 Tracks and Program Leadership
SIGKDD 2027 has published four tracks, each with named program co-chairs and a first submission cycle already open. No general conference chairs or keynote speakers have been announced yet, so what follows is exactly what has a name attached to it right now.
| Track | Focus | Program Co-Chairs |
|---|---|---|
| Research Track | Core data mining and machine learning methods and theory | James Caverlee (Texas A&M University), Jingrui He (University of Illinois Urbana-Champaign), Xiangliang Zhang (University of Notre Dame) |
| Applied Data Science (ADS) Track | Deployed, production AI and data science systems | Dawn Woodard (LinkedIn), Romer Rosales (YouTube/Google), Faisal Farooq (Pinterest) |
| Datasets and Benchmarks Track | New benchmark datasets for the data mining community | Not yet published |
| AI for Sciences Track | AI applied to physical, biomedical, and environmental sciences | Carl Yang (Emory University), Yuan Fang (Singapore Management University), Leman Akoglu (Carnegie Mellon University) |
The Applied Data Science Track’s co-chair lineup is a useful signal on its own. Having program leadership from LinkedIn, YouTube/Google, and Pinterest on the committee tells you this track is genuinely built around real, deployed industry systems, not academic simulations dressed up as applications. The track’s submission rules back that up: papers without quantified post-launch performance metrics face automatic desk rejection, with only narrow exceptions for regulatory constraints.
What Will the SIGKDD 2027 Agenda Cover?
The confirmed scope comes from each track’s call for papers, since the detailed session schedule, workshops, and tutorials haven’t been published yet. Based on what’s already public, here’s what each track is looking for.
The Research Track spans five broad areas: foundations of knowledge discovery (core algorithms, clustering, probabilistic methods), modern AI and big data (deep learning, large language models, generative models, neural-symbolic integration), trustworthy and responsible data science (privacy, fairness, adversarial robustness), systems for scalable AI (distributed computing, federated learning, streaming), and applied domains ranging from legal data to quantum data science.
The Applied Data Science Track wants papers on deployed systems across advertising, finance, geospatial systems, industrial applications, scientific computing, responsible AI, and generative AI applications, evaluated on real business or engineering outcomes rather than theoretical novelty.
The AI for Sciences Track is where this edition gets genuinely interesting. It’s explicitly built to unite AI researchers with domain scientists in physical sciences, biomedical and life sciences, environmental studies, robotics, transportation, and social sciences, with a stated preference for papers with collaborative authorship between AI researchers and domain experts, not AI researchers working in isolation on a science-adjacent dataset.
All three tracks with published details share the same first-cycle timeline: abstract deadline July 19, 2026, paper deadline July 26, 2026, author rebuttal period September 29 to October 13, 2026, and notification November 14, 2026. The Research Track page also references a second submission cycle, though specific 2027 Cycle 2 dates haven’t been published yet.
How to Actually Submit a Paper to SIGKDD 2027
The July 26, 2026 paper deadline gets quoted everywhere, but the mechanics behind it are what actually get papers desk-rejected before a reviewer ever reads them. Here’s what the official Research Track call for papers spells out, straight from `kdd2027.kdd.org`, so you’re not learning it the hard way at 11pm the night before the deadline.
Submissions run through OpenReview, not email or a custom portal. Every listed author needs a complete OpenReview profile (institutional affiliations going back at least five years, homepage, DBLP, and ORCID), and an incomplete profile is on its own sufficient grounds for desk rejection. New profiles created without an institutional email go through a moderation queue that can take up to two weeks, so don’t create your account the week of the deadline.
The format rules are strict and enforced by desk rejection. Papers must use the double-column ACM proceedings template (LaTeX users set \documentclass[sigconf,anonymous,review]{acmart}, and an Overleaf version exists too). Submissions are capped at eight content pages plus references and an optional unlimited appendix, and the review is double-blind, so you strip author names, affiliations, and giveaway self-citations. Accepted papers get one extra content page (12 pages total, 9 of them content) for the camera-ready version.
A few more rules that quietly trip people up:
- Two cycles a year. July 26, 2026 is Cycle 1; there is a second cycle with a February 2027 deadline. You pick one track per paper, with no transfers between tracks.
- Every deadline is Anywhere-on-Earth (AoE), and the organizers state plainly there are no extensions for any reason.
- Author lists lock at the abstract deadline. No adding, removing, or reordering authors after July 19, 2026.
- Maximum seven submissions per author per cycle in the Research Track; anything past the seventh gets desk-rejected by submission ID.
- You may have to review. Every submission must nominate at least one qualified reviewer, and any author listed on three or more papers can be auto-enrolled as a reviewer.
- Withdraw after reviews and you’re locked out. Pulling a paper after reviews are revealed triggers a 12-month waiting period before you can submit to KDD again.
Two cost facts that never make the headline registration price. First, every accepted paper needs its own distinct full 5-day, non-student registration to appear in the proceedings, so two papers means two registrations even when the authors overlap. Second, ACM’s 2027 open-access model means authors from institutions outside ACM Open pay an article processing charge, subsidized for 2027 at $500 for ACM/SIG members and $750 for non-members (a 29% discount ACM is funding directly). Budget for both before you celebrate an acceptance.
SIGKDD 2027 Registration: What’s Published and What Isn’t
As of this writing, SIGKDD 2027 has no published registration fees, no announced early-bird window, and no ticket tiers. If you see a specific 2027 price quoted anywhere outside `kdd2027.kdd.org` itself, treat it as an estimate at best.
What we do have is the actual pricing from the most recent prior edition, which is a reasonable planning reference even though it is not a promise of what 2027 will cost.
| Registration Type | KDD 2025 Early Bird | KDD 2025 Standard | KDD 2025 Onsite |
|---|---|---|---|
| Non-member | $1,250 | $1,500 | $1,750 |
| SIGKDD member | $1,000 | $1,250 | $1,500 |
| Student | $500 | $600 | $700 |
Figures above are for KDD 2025 in Toronto, sourced from the official KDD 2025 registration page. They are historical reference only, not confirmed 2027 pricing.
Two patterns are worth planning around even before 2027 numbers exist. First, SIGKDD registration has consistently run in the $1,000-$1,750 range for full conference access, a meaningfully higher price point than most vendor or practitioner conferences, reflecting its academic-conference cost structure (venue, proceedings, and reviewing infrastructure) rather than sponsor-subsidized pricing. Second, student rates have consistently been roughly 40-60% below the non-member rate, so if you’re a grad student with an accepted paper, budget for the student tier specifically rather than assuming the headline price applies to you.
Discounts and Group Rates for SIGKDD 2027
There is no discount code, partner promotion, or early-access deal published for SIGKDD 2027 right now, from ZPlatform or anyone else. Anyone offering a “SIGKDD 2027 discount code” today is not working from anything on the official site.
Historically, SIGKDD conferences have offered a small number of standing discount categories rather than promotional codes: SIGKDD membership rates (roughly 20% off the non-member price), student rates (40-60% off), and an early-bird window that typically closes a few weeks before the conference. Corporate sponsorship has also historically helped keep student and international attendee costs down, though that’s a structural subsidy, not a code you enter at checkout.
If registration pricing, an early-bird deadline, or a sponsor discount gets published for SIGKDD 2027 before this guide is updated, it will show up on `kdd2027.kdd.org` first. The most useful thing you can do right now is subscribe for AI event and deal alerts so you’re not the one manually checking the official site every few weeks between now and the registration window opening.
Is SIGKDD 2027 Worth It? My Honest Take
For academic researchers, applied data scientists, and domain scientists doing genuinely publishable work, SIGKDD remains one of the strongest venues in the field, and that reputation is not something 2027 needs to prove; it is 30-plus years and 4,489-plus papers in the making. The four confirmed tracks, and the caliber of the co-chairs already named for them, suggest 2027 is being built with the same rigor as prior editions.
The honest caveat is what this article opened with. If you’re deciding whether to attend purely as an observer or to send a non-research team for exposure, you are currently deciding with incomplete information. No keynotes, no full agenda, and no registration pricing exist yet for 2027, and anyone telling you otherwise right now is filling in gaps that the organizers themselves haven’t filled in.
Here’s my buy-or-wait framing, the same lens I apply to AI tools I’ve actually tested before recommending them. If you have research or a deployed system worth submitting, the decision is already made for you: the July 26, 2026, paper deadline is the real deadline that matters, over a year before the conference itself. If you’re weighing attendance only, wait for registration pricing and the keynote lineup to publish, likely sometime in the first half of 2027, before committing travel budget to San Jose.
Frequently Asked Questions
When and where is SIGKDD 2027?
SIGKDD 2027 runs August 1-5, 2027, at the San Jose McEnery Convention Center in San Jose, California. Both dates and venue are confirmed directly on the official conference site, kdd2027.kdd.org.
What is the paper submission deadline for SIGKDD 2027?
The first-cycle abstract deadline is July 19, 2026, and the paper deadline is July 26, 2026, with author rebuttals running September 29 to October 13, 2026, and notifications going out November 14, 2026. These dates apply to the Research, Applied Data Science, and AI for Sciences tracks.
How much does SIGKDD 2027 registration cost?
Registration pricing for SIGKDD 2027 has not been published yet. For reference, the prior edition (KDD 2025 in Toronto) charged $1,000-$1,750 for full non-member and member registration and $500-$700 for students, depending on the registration window.
Who are the keynote speakers at SIGKDD 2027?
No keynote speakers have been announced for SIGKDD 2027 as of this writing. The only named leadership currently public are the program co-chairs for the Research, Applied Data Science, and AI for Sciences tracks.
Is there a SIGKDD 2027 discount code?
No. There is no published discount code or promotional partnership for SIGKDD 2027 from any source, including ZPlatform. Historical discounts have come from SIGKDD membership, student rates, and early-bird registration windows, not promo codes.
How is SIGKDD 2027 different from KDD 2026 in Jeju?
SIGKDD 2027 is the following year’s edition of the same annual conference, held in San Jose, California, in August 2027, roughly a year after KDD 2026 in Jeju, South Korea. Each edition has its own venue, program committee, and submission deadlines, so details from one year don’t carry over to the next.
The Bottom Line
SIGKDD 2027 is a real, confirmed conference with locked-in dates (August 1-5, 2027), a locked-in venue (San Jose McEnery Convention Center), and four active submission tracks with named program leadership from institutions like Texas A&M, LinkedIn, Google, and Carnegie Mellon. What it does not have yet, publicly, is a keynote lineup, a full session agenda, or registration pricing, and this guide is not going to pretend otherwise just to sound more complete.
The insight worth taking with you: for a research conference like this, the paper deadline is the deadline that actually matters to most people who benefit from attending. If your team has deployed work with real performance data, July 26, 2026, arrives faster than August 2027 feels like it should.
Your concrete next step for SIGKDD 2027: bookmark the official conference site directly rather than a secondhand aggregator, and check back as registration and the keynote lineup get announced. In the meantime, browse the AI conferences and events I’m tracking for other research and industry events worth planning around this year.