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+---
+title: Google Summer of Code 2026
+description: |
+ We are excited to announce our project ideas for the Google Summer of Code 2026! Below is a list of projects the Turing team would love to work on with contributors this summer. This information is also cross-posted at Julia's Turing project page.
+categories:
+ - Blog
+ - GSoC
+author:
+ - name: The TuringLang team
+ url: /team/
+date: 02/23/2026
+toc: true
+---
+
+The TuringLang ecosystem has made strong progress recently: more AD backends, a maturing JuliaBUGS, and clearer inference tooling.
+
+For GSoC 2026 we offer projects from core AD work to scalable inference and user-facing tools.
+
+If a project interests you, contact the mentors listed below or open a discussion on the relevant GitHub repo.
+Please drop a short introduction message in the [`#turing`](https://julialang.slack.com/archives/CCYDC34A0) channel on the [Julia Slack](https://julialang.org/slack/) and feel free to ping [Shravan Goswami](https://github.com/shravanngoswamii) there. Mentors will be happy to discuss project details, scope, and expectations, and you can find mentor contacts at [team page](/team/).
+
+Also cross-posted on [Julia's GSoC page](https://julialang.org/jsoc/gsoc/turing/).
+
+## Mentor Contacts on Julia Slack
+
+| Mentor | Slack Contact |
+|--------|---------------|
+| Hong Ge | [Slack](https://julialang.slack.com/team/UCRDHV7PB) |
+| Xianda Sun | [Slack](https://julialang.slack.com/team/U03AV5JMJ8N) |
+
+## New AbstractMCMC-based Gibbs Sampler for Turing.jl and JuliaBUGS.jl
+
+**Mentors**: [Hong Ge](https://github.com/yebai) and [Xianda Sun](https://github.com/sunxd3)
+
+**Project difficulty**: Medium
+
+**Project length**: 350 hrs
+
+Gibbs sampling is one of the most widely used inference strategies in Bayesian computation, but Turing.jl's current Gibbs implementation is tightly coupled to its internals and difficult to extend.
+
+This project is about designing a clean, composable Gibbs sampler built on top of the [AbstractMCMC.jl](https://github.com/TuringLang/AbstractMCMC.jl) interface so that it works perfectly across both Turing.jl and [JuliaBUGS.jl](https://github.com/TuringLang/JuliaBUGS.jl).
+Work will include:
+
+- Agreeing on a minimal AbstractMCMC-compatible interface for conditional samplers.
+- Implementing the new Gibbs combinator and verifying correctness on standard models.
+- Migrating existing Turing.jl Gibbs usage to the new interface.
+- Ensuring JuliaBUGS.jl can plug in its own conditional samplers without modification.
+
+## Stateful Hand-Written Rules and Thread Support in Mooncake.jl
+
+**Mentors**: [Hong Ge](https://github.com/yebai) and [Xianda Sun](https://github.com/sunxd3)
+
+**Project difficulty**: Medium to Hard
+
+**Project length**: 350 hrs
+
+[Mooncake.jl](https://github.com/chalk-lab/Mooncake.jl) is a source-to-source reverse-mode AD package for Julia.
+Two open issues currently limit its performance and applicability in real-world workloads.
+
+The first is that every hand-written `rrule!!` must allocate scratch memory on every call ([issue #403](https://github.com/chalk-lab/Mooncake.jl/issues/403)).
+Derived rules already avoid this by carrying persistent state between calls, but hand-written rules have no such mechanism.
+The fix is a `StatefulRRule` struct that holds a `Stack` of saved state, constructed via a `build_primitive_rrule` function.
+Rule authors implement `stateful_rrule!!`, which receives the current state (or `nothing` on the first call) and returns updated state alongside the usual outputs -- stack push/pop is handled automatically.
+The work is to (1) add a test that fails when a primal is allocation-free but its `rrule!!` is not, (2) audit all existing hand-written rules with that test, and (3) convert the offenders.
+
+The second is that Mooncake currently errors on any code using `Threads.@threads` ([issue #570](https://github.com/chalk-lab/Mooncake.jl/issues/570)).
+Even a race-condition-free primal can produce race conditions on the reverse pass -- two threads may concurrently increment the same tangent element, so increments must be atomic.
+Additionally, rule caches (the stacks inside `OpaqueClosure`s) must be Task-specific; sharing them across Tasks causes pushes and pops to interleave incorrectly.
+The work involves writing rules for the `ccall`s that enter and exit threaded regions, ensuring atomic tangent updates, and making rule caches Task-local without relying on `threadid()`.
+
+## Pigeons.jl Integration with Turing and JuliaBUGS via AbstractMCMC
+
+**Mentors**: [Xianda Sun](https://github.com/sunxd3)
+
+**Project difficulty**: Medium to Hard
+
+**Project length**: 350 hrs
+
+[Pigeons.jl](https://github.com/Julia-Tempering/Pigeons.jl) implements parallel tempering and related algorithms that are particularly effective for multimodal and high-dimensional posteriors.
+This project has two related goals that together make Pigeons a first-class citizen of the TuringLang ecosystem.
+
+The first part is documentation and examples.
+Turing.jl models can already be used as targets for Pigeons, but the combination is under-documented.
+The contributor will produce reproducible tutorials that walk through common use cases -- multimodal posteriors, hierarchical models, models with difficult geometry -- and compare Pigeons against HMC/NUTS on the same problems.
+These will be published on the Turing website and any integration rough edges found along the way will be fixed.
+
+The second part is implementing the efficient Gibbs sampler from [arXiv:2410.03630](https://arxiv.org/abs/2410.03630) in [JuliaBUGS.jl](https://github.com/TuringLang/JuliaBUGS.jl).
+The paper shows that by exploiting the structure of the compute graph (rather than the graphical model), the time per sweep of a full-scan Gibbs sampler on GLMs can be reduced from $O(d^2)$ to $O(d)$ in the number of parameters $d$ -- making high-dimensional GLMs feasible where traditional Gibbs is not, and outperforming HMC on effective samples per unit time in many regimes.
+JuliaBUGS already exposes the graph structure this approach relies on.
+The implementation must be correct and performant, validated against standard benchmarks with comparisons to both traditional Gibbs and HMC.
+
+## Contributor-proposed Project
+
+**Mentors**: Community
+
+If you have an idea not listed here, propose it. Submit a short proposal with motivation, a concise plan, expected deliverables, and a timeline. Maintainers and mentors will review and help turn it into a plan. Be prepared to discuss scope with mentors early.
+
+To discuss proposals and next steps, contact [Shravan Goswami](https://julialang.slack.com/team/U04UZB5U740) on the [Julia Slack](https://julialang.org/slack/).