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The Julia GPU ecosystem allows users to write array programs that run seamlessly across NVIDIA, AMD, and Intel GPUs. This functional portability relies on GPUArrays.jl, which defines the common interface implemented by all vendor packages. However, several foundational operations currently lack shared implementations. Operations such as reverse, findall, accumulate!, and mapreduce either crash on backends lacking a vendor override or silently degrade to CPU execution by copying data back and forth. A direct source audit of GPUArrays.jl (v11.4.1), CUDA.jl, AMDGPU.jl, oneAPI.jl, and Metal.jl confirmed these gaps: reverse and findall are entirely absent from the shared fallback layer, accumulate! exists only as a scalar CPU fallback, and mapreducedim! is declared as an error("Not implemented") stub, forcing every backend to maintain isolated, vendor-specific implementations. This project resolves these portability gaps by establishing a cleaner, more robust architecture: moving critical GPU kernels upstream to AcceleratedKernels.jl (AK.jl) while refining GPUArrays.jl into a lightweight, pure delegation layer. Building upon the newly introduced KernelIntrinsics API, this approach implements highly optimized, vendor-neutral kernels directly within AK.jl. Because Julia’s multiple dispatch inherently prefers the most specific method, routing AnyGPUArray fallbacks to AK.jl means backends with existing optimized vendor methods remain untouched, while backends missing the functionality automatically receive high-performance GPU execution instead of a crash or silent slowdown.

<p>Escape analysis is a classic problem in compiler analysis. Julia compiler has an existing AbstractInterpreter framework for managing inter-procedural analysis of type through data-flow analysis. But the existing framework has very limited support for escape information.</p> <p>In this project, we are implementing escape analysis via extending the current framework or via implementing a new framework and integrating it into the compiler. With better escape information collected, we are able do more kinds of optimizations including converting heap objects to stack objects and scalar optimizations.</p>

The goal of this project is to improve the user experience of search functionality provided by Documenter.jl by building an intelligent search system that will enable users to easily and quickly find the information they need. The system will use various pre-processing techniques and search algorithms to provide accurate search results and suggestions for related content.

<p>Interactive data exploration is an increasingly important component of data science. Interact.jl and Escher.jl are good libraries that have the potential to be extremely powerful for these tasks, but currently Interact only supports IJulia and Escher suffers some stability issues. WebIO.jl looks to be a promising path forward for building web based components that are created by or interface with Julia. Rebuilding Interact.jl on top of WebIO.jl will bring Interact beyond IJulia, to the Atom/Juno IDE and to desktop apps with Electron/Blink.jl. Additionally it should enable Escher.jl to reuse much of Interact, which will likely reduce Escher’s size and complexity significantly, increasing its stability. Furthermore, by removing its dependence on Jupyter and IPywidgets, Interact will gain flexibility to produce more complex UIs which intermix displayed data such as plots, and UI controls. Finally, I plan to add some automated UI testing which hopefully will assist in improving the stability of not just Interact and Escher, but potentially IJulia, Juno, and Blink as well.</p>

This project adds a new package to JuliaStats, covering complex survey analysis. The package is inspired by the `survey` package in R. Specifically, a part of the R package is ported to Julia. The completed project will consist of added support for survey analysis, along with the associated documentation.

<p>De-novo sequence assembly is the process of constructing a contiguous long sequence out of shorter sub-sequences produced by sequencing platforms, without referring to a reference genome. It is an essential task in many biological studies today, including population and medical studies. The initial stages of de-novo assembly require the construction of a de-bruijn graph (DBG) from sequencing reads, the compression of a de-bruijn graph into a unitig graph, and the compression of multiple unitigs and nodes into contigs, supported by evidence from mapping paired-end reads. A coherent ecosystem of computational tools and packages allow researchers to quickly implement and test their ideas. For bioinformatics, Julia already offers such an ecosystem in the form of the BioJulia &amp; EcoJulia projects, and additional independent packages. This project will add sequence assembly tools to the BioJulia ecosystem, specifically: 1) DBG construction from reads, 2) UG construction from a DBG and 3) Constructing contigs using unitigs. These tools will allow researchers to quickly construct and analyze the contigs obtained from a set of reads.</p>

<p>DifferentialEquations.jl comprises of leading-edge methods for solving Differential Equations and an extensive benchmark suite written purely in Julia. Many methods have state-of-the-art performances, but still, some of them can be accelerated for high-performance returns. This project aims to maintain the same standards and improve benchmark performances of various solvers. In essence, the project will emphasize increasing the speed through iterative optimizations, bug fixes and improvements, reduce redundant memory allocation and improved benchmarking.</p>

<p>Neural Networks (NN) is one of the most fundamental building blocks of modern Computer vision and it has found its relevance in multiple domains like image, text, audio, and many more. Various operations, convolution operation in analysing images to recurrent neural networks used to process text uses the principle of NN. Also, researchers have come up with novel deep learning architecture to analyse and find high-order patterns in domains like 3D structure.</p> <p>However, it is not easy to implement domains like 3D computer vision with essential utilities of NN library (which is primarily focused on classic computer vision). Implementation can generally become complicated and confusing. Also, it's no longer deemed necessary among deep learning enthusiasts to know the traditional computer vision algorithm from the implementation point of view, but they are beneficial from the understanding point of view. Therefore a framework which can unifies and ease the task of 3D vision will be useful to the community.</p>

My proposal is to bring in core changes to Javis' Object - Action mechanism for making animations . As of now Javis does not store drawing information as a data-structure but a function at compute time. The Object is drawn to the canvas by a function call at render time. The proposal has 3 parts. First - pre-compute the polygons/paths that an Object will place on the canvas, and have that information as a field in the Object. This part involves hacking into functions in Luxor.jl to store/return the information of drawn paths somewhere. Second - Bring in the ability to partially draw an object with the above paths at hand. Also implement functions to animate the "creation" of the object being shown. For example if the Object were a circle , the creation animation would draw an arc increasing its angle from 0 to 2Pi . Third - Bring in the ability to morph an Object into another. For example turn a square into a circle. The current implementation of Morphing in Javis has certain limitations, with the proposal implemented any arbitrary Object would be morph-able into another Object

This project aims to further enhance the performance and usability of tensor network tools in Julia. There are three main goals of the project: 1) extending the optimization methods in OMEinsumContractionOrders.jl with the state of art contraction order optimization algorithms; 2) implementing a hyper-graph visualization tool for tensor networks as a standalone package; 3) porting the contraction order optimization algorithms in OMEinsumContractionOrders.jl to the TensorOperations.jl package as a backend. Testing and documentation will also be done for all implemented methods, and the code will be open-sourced and published to the Julia community.

FlashFill is a program synthesis framework for generating string-processing programs from input-output examples. This project aims to implement FlashFill in Julia and integrate it with DataFrames.jl. Such a package would be a valuable tool for Julia users handling tabular data, providing an easy way for data-cleaning and data-wrangling. It would also lay the foundations for implementations of further open-source program synthesis frameworks in Julia.

<p>Documentation, essential for any software project, needs to be as easy as possible to write and publish. For Julia packages, you can use the <a href="https://github.com/JuliaDocs/Documenter.jl" target="_blank">Documenter package</a>, which automatically generates and publishes package manuals as a web page or a PDF.</p> <p>It is important for Documenter to be modern and flexible. This project aims to revitalize Documenter, by upgrading the generated HTML front end and making it easier for documentation authors to customize. This will make sure that Documenter will keep meeting the needs of the community for years to come.</p>

<p>This project aims to achieve a better ecosystem for <code>Images.jl</code>, an image-processing toolbox in Julia. Main contributions consist of user-friendly documentation on <code>Images.jl</code> ecosystem, developer manual, and more consistent, robust, and extensible APIs. Moreover, this project also serves as a subproject to bring <code>Images.jl</code> from pre-Julia-1.0 stage to post-Julia-1.0 stage, and eventually to <code>Images.jl v1.0</code> milestone.</p>

Graph Neural Networks (GNNs) rely on message passing, traditionally implemented via a gather/scatter mechanism. In GraphNeuralNetworks.jl, this approach materializes dense feature matrices on graph edges, leading to severe memory bottlenecks and reduced performance as graph sizes scale. This project addresses this limitation by redefining message passing as non-materializing Sparse Matrix-Matrix Multiplications (SpMM). By treating the graph as a sparse mathematical operator, this project will unlock significant computational efficiency and memory savings on CUDA GPUs. The core objectives include integrating COO storage, upstreaming patches to CUDA.jl, and refactoring the GNNlib API. To ensure continuous momentum during upstream review cycles, the project features a secondary track focused on upgrading the Automatic Differentiation (AD) pipeline by fully integrating Mooncake.jl and Enzyme.jl, alongside expanding the ecosystem with new datasets and new tutorials / improved documentation.

The David-Skene algorithms are a family of computational methods for aggregating crowdsourced annotations of data in order to obtain the true labels or categories for that data, which is very useful in places where we do not have access to the "true" annotations for large datasets and therefore need to rely on fallible human judgment. The most traditional version of the David-Skene (DS) algorithm dates back to 1979 but more recently a more efficient (eightfold sppedup) although less accurate version, called Fast David-Skene (FDS) has been developed. The hybrid variant (HDS) sits in the middle in terms of accuracy and speed between DS and FDS. Since demands for the two differ according to situation, it makes sense to implement all thre versions (DS, FDS, and HDS) so that everyone interested can use the one most suitable to their situation. This is the purspose of this project. Additionally, the algorithms in question will be extended to be applied to multi-label problems as well.

<p>JuliaDiffEq is the most popular github organization for solving differential equations in Julia.Currently, it has an assorted collection of Ordinary Differential Equations (ODE), StochasticDifferential Equations (SDE) and Algebraic Differential Equations (DAE) solvers.</p> <p>But currently, there is no support for Partial Differential Equations (PDEs) which form an integral class of Differential equations found ubiquitously in natural sciences and engineering. PDEs are generally solved approximately by discretizing them to form ODEs which can then be solved separately.</p> <p>A toolbox to solve a large family of scientifically-interesting PDEs is thus sought. In light of this, I propose to create a toolbox of methods for discretizing PDEs in Julia.</p>

<p>The goal of this project would be to implement a fast open-addressed hash table inspired from Google’s <code>flat_hash_map</code>. In addition to this, I propose to work on getting an ordered version of hash tables, based on <code>OrderedDict</code>. All of these would be supplemented with proper benchmarks. Finally, I plan to work on getting several tree-based data-structures implemented and adding it to <code>DataStructures.jl</code>.</p>

<p>I propose to</p> <ol> <li><p>Add a package for image segmentation as a part of JuliaImages with the following algorithms</p> <pre><code> * Thresholding - Otsu’s method and Adaptive thresholding * K-means clustering * Mean shift segmentation * Watershed segmentation * Felzenszwalb's efficient region merging algorithm * Shi and Malik’s normalized graph-cut based segmentation </code></pre> </li> <li><p>Add Histogram of Oriented Gradient feature to ImageFeatures.jl</p> </li> </ol>

This project aims to significantly enhance the search experience within documentation generated by Documenter.jl. The current client-side search encounters difficulties accurately indexing and retrieving results for Julia-specific syntax, leading to suboptimal relevance. Furthermore, issues like unnecessarily large search indices and repeated listing of the same location hinder usability. This proposal outlines a plan to address these shortcomings by: 1) Implementing search performance benchmarks (relevance, precision/recall, index size) to measure and guide improvements. 2) Developing and integrating an improved, Julia-aware tokenizer within the search JavaScript. 3) Optimizing the index by investigating and removing redundant data, ensuring unique location entries are stored, and adding warnings for potentially problematic large indices. 4) Creating comprehensive developer documentation for the enhanced search system. The outcome will be a more robust, accurate, and efficient search capability for users navigating Julia package documentation.

The CxxWrap.jl package acts as a bridge between Julia and compiled C++ code, resulting in developers being able to use the best of both languages. While it already exposes a subset of the C++ standard library to Julia, there is a significant opportunity to expand its capabilities and enhance the interoperability further. The primary objective of this project is to improve the coverage of the C++ Standard Template Library within CxxWrap.jl. The STL is very widely used and provides a rich set of container types, algorithms, and utilities in C++.

<p>The main goal of the project is to port capability from​ sktime​ (Machine learning for time series) to MLJ universe &amp; develop data container to handle time series data in modular, composable way.</p>

QuantumClifford.jl enables efficient simulation of Clifford circuits acting on stabilizer states using the (de)stabilizer formalism, with complexity O(𝑛²), where 𝑛 is the number of qubits. For sparse stabilizer states—those with a qubit connectivity of O(log 𝑛)—an alternative algorithm based on graph states achieves improved complexity of O(𝑛 log 𝑛). This project aims to implement this graph-states-based simulator as an alternative backend in `QuantumClifford.jl`. Specifically, the project will: support the simulation of generator gates {𝐻, 𝑆, Λ𝑍}, enable the decomposition of arbitrary Clifford gates into these three generators, and implement single-qubit Pauli measurements on graph states. The goal is to provide a seamless transition to the graph-based simulator through compatibility with existing `apply!` and `project!` APIs in `QuantumClifford.jl`.

<p>Support parallelism in Bio.jl based on Dagger.jl</p>