Student Research Showcase
Call for Submissions
CERTAIN 2026 invites submissions to its Student Research Showcase. The showcase is designed as a non-archival two-page abstract track. We welcome work in progress and encourage submissions that would benefit from community feedback.
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Categories
Who can submit
The pre-doctoral category includes master's students and prospective PhD applicants not currently enrolled in a PhD programme.
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Awards
The senior PC will select four 10-minute Student Award Talks: one per category where suitable, and one overall. Selected presenters will receive awards and an optional mentoring session with a senior PC member to refine their slides and delivery.
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Requirements
What to submit:
For multi-author projects, submissions must list all contributors and clarify the participant's own contribution and the contribution of the full team.
Work presented in the NeurIPS 2026 main programme may not appear at the workshop; already finalised work is discouraged.
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Timeline
Submission: 29 August 2026, AoE
PhD reviews: 12 September 2026, AoE
Senior PC reviews: 19 September 2026, AoE
Senior PC meeting: 21 September 2026
Final notification: 25 September 2026, AoE
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Visa Support
We will offer an early visa-support track for authors who need additional time to apply for travel documentation for Sydney. Early submissions will be due by 18 August 2026, AoE, with poster decisions communicated by 1 September 2026, AoE. Eligibility will be determined through a brief self-certification of need, and places will be capped to ensure timely, high-quality review. Student Award Talk selections will be announced for all on 25 September 2026, AoE.
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Submission Link
The submission link will be provided upon acceptance by the NeurIPS Workshop Committee.
Note: Submissions will be non-archival, so students can receive feedback without blocking future conference or journal submissions.
Scope
CERTAIN sits at the intersection of AI and formal methods. It is not a general workshop on AI safety, privacy, or ethics. We include those topics only when they rest on specifications, proofs, certificates, or formal analysis.
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Formal Methods for AI
The specification, analysis, and verification of AI-enabled systems.
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AI for Formal Methods
The use of machine learning to support reasoning, synthesis, and verification with formal guarantees.