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This project aims to build a Julia package for probabilistic modeling based on static directed graphs. Specifically, we want to enable users to execute programs previously written in BUGS(Bayesian inference Using Gibbs Sampling) directly in Julia.

<h3>Parallel Graph development</h3> <p>I am interested in working on parallel graph algorithms and an efficient data structure to represent Graphs, which take advantage of Julia’s parallel environment. The data structure would be based on LightGraphs and all algorithms that I intend to code would be an addition to LightGraphs.jl . The algorithms would be Implementation of various research papers published and data structure would be such that it minimises overheads in parallel computations .</p>

<p>This proposal consists of the design of an API for Iterative methods in Julia and the set up of benchmarks and a regression tracker.</p>

<p>Virtually all properties of interest arise from electronic interactions between the atoms of a crystal.</p> <p>The work I propose aims to develop a physics-informed graph convolutional neural network architecture, inspired by the <a href="https://en.wikipedia.org/wiki/Tight_binding" target="_blank">Tight-Binding model</a> formalism. This would help us initialise weights with information from the Slater-Koster tables made available as a priori, which would theoretically help derive a more realistic, accurate and data-efficient representation.</p> <p>In addition to this, I also aim to help design and develop essential parts of <a href="https://github.com/Chemellia" target="_blank">Chemellia</a>; primarily <a href="https://github.com/Chemellia/ChemistryFeaturization.jl" target="_blank">ChemistryFeaturization.jl</a>.</p>

<p>This project aims to get the GraphBLAS API working from Julia by connecting it to the SuiteSparse GraphBLAS implementation and creating a graph type backed by the methods provided in the library.</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.

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`.

The objective of this project is to implement a class imbalance package in Julia with algorithms to correct for class imbalance. The package is expected to integrate well with other packages such as MLJ and to provide any useful resampling algorithms to handle the imbalance issue.

<p>Bridge the gap between Python community and the Julia community for the state of the art natural language processing models.</p>

<p>Density functional theory (DFT) is a key driving force in modern quantum chemistry, with applications ranging from materials science to drug discovery. From a numerics perspective it amounts to solving a coupled system of non-linear partial differential equations. This is the setting of the density functional toolkit DFTK.jl, a Julia package for solving DFT problems.</p> <p>Can we make a full DFT solver differentiable via automatic differentiation (AD)? There are at least three reasons why this is an interesting problem:</p> <ol> <li>It will enable faster model design iterations for chemists, providing useful model derivatives automatically.</li> <li>It will enable research into novel combinations of machine learning and DFT.</li> <li>It is technically challenging from an AD perspective. We expect to raise rough edges and collaborate on smoothing them out. We believe it has the potential to become a showcase of the Julia ecosystem and AD compositionality on a large scale.</li> </ol>

<p>Environments are a crucial component of <a href="https://en.wikipedia.org/wiki/Reinforcement_learning" target="_blank">reinforcement learning (RL)</a>. Grid worlds are a broad class of tile-based games that are easy to create, and are quite popular for testing and comparing various RL algorithms. They are highly versatile and provide immense flexibility in terms of creating custom environments that can test for specific properties like safe exploration, for example. <a href="https://github.com/JuliaReinforcementLearning/GridWorlds.jl" target="_blank">GridWorlds.jl</a> is a <a href="https://julialang.org/" target="_blank">Julia</a> package that provides a suite of grid world environments along with useful primitives that aid in creating custom environments.</p> <p>This project redesigns GridWorlds.jl in a way that makes it simpler, easy to understand, and more performant (in some cases) than the previous version (v0.4.0). The new version (v0.5.0) also decouples itself significantly from the API of <a href="https://github.com/JuliaReinforcementLearning/ReinforcementLearning.jl" target="_blank">ReinforcementLearning.jl</a>, which makes it easy to create entry points from other reinforcement learning frameworks. It also becomes possible to programmatically record, replay, and analyze an agent’s behavior at any point during training, which proves handy while performing reinforcement learning experiments. Moreover, one can directly play the games from GridWorlds.jl inside the Julia REPL.</p>

Problem: Installing Julia, opening a terminal, running Julia REPL, then opening the package manager, adding Pluto package, running Pluto, and then going to the browser to run it. How many steps was it? 7. Seems quite uninviting. Now think of a person who has never touched any terminal in his life. Seems like an even more daunting task. A person who is only interested in running a Pluto Notebook and running Julia on it may or may not know how to do any of these steps, and since these can be automated, why keep this task to the end user? My Plan to solve: This is where this app comes in, bundles all these steps, runs this in the background, and then only shows the user their notebook running inside the app itself, no browser required. Deliverable: An Electron app that can perform the following tasks: 1. Install Julia and pluto on the first run. 2. Show the pluto view inside the app. 3. Allow the view to interact with the server. 4. Support the native file system features of Pluto. 5. Support running more than one instance of Pluto views. 6. Support light and dark modes. 7. Be responsive (allow different screen sizes). 8. Show the Julia Package Page inside the app itself to refer to some documentation when and if required. 9. It should also have proper comments and documentation for future reference.

Survey.jl is a Julia package for survey analysis inspired by the R package 'survey'. It aims to address the unique challenges associated with the analysis of large survey datasets, such as weighting, clustering, and stratification. I will be completing the implementation of generalized linear models, adding support for more complex survey types, creating helper functions as needed, and helping to refactor parts of the codebase.

This project extends the capabilities of the MedEye3D framework by developing an advanced visualization tool for supervoxel-based comparison across multiple medical imaging modalities (e.g., CT and MRI). The tool will enable users to interactively explore supervoxels on synchronized image views, with automatic highlighting of corresponding regions to aid in non-rigid registration and comparative analysis. Features include side-by-side image displays, interactive correction of supervoxel associations, and annotation of anatomical control points to support image alignment. Built using Julia, ModernGL.jl, and ITK/SimpleITK, this tool will enhance clinical workflows and research by improving registration accuracy and facilitating reliable visualization in complex imaging scenarios. The project contributes to digital twin applications in healthcare by enabling robust, user-guided multi-modality analysis.

<p>This is a proposal for the project <em>"Native Julia solvers for ordinary differential equations and algebraic differential equations"</em>. There is no solver for ordinary differential equation (ODE) boundary value problem (BVP) yet in the <em>JuliaDiffEq</em> organization. I am going to implement native Julia solvers for ODE BVP problem. There are a number for advantages of Julia native solvers over calling C or Fortran's existing libraries via Julia's FFI, including the ability to use a wide variety of types in Julia like <em>BigFloat</em> for arbitrary precision, easier for people to contribute and better integrate with other packages in Julia ecosystem. The goals for this project are to implement BVP related data structures, a shooting method and a collocation method. Together, this contribution will help make Julia a viable environment for researchers and scientists to simulate models from boundary value problems.</p>

<p><a href="https://github.com/JuliaText" target="_blank">JuliaText</a> is the JuliaLang organization that provides with packages to work with text. It currently lacks support for basic problems like Named Entity Recognition, Part-of-Speech Tagging, Dependency Parsing etc. which help serves as the basis for various language processing problems and analysing text.</p> <p>I propose to implement practical models for Named Entity Recognition and Part-of-Speech Tagging in Julia and extensively test and validate them. Robust and well-tested APIs for these two tasks will be written.</p>

Molly.jl is a package in pure Julia for simulating molecules and physical systems in general using method known as Molecular Dynamics (MD). This project implemented a class of molecular dynamics simulations known as replica exchange molecular dynamics (REMD) to this library. REMD methods are very important in the study of complex biomolecules such as proteins, due to their ability to better explore the configurational space of such molecular systems by running many parallel copies of MD simulations with different conditions and sampling states from all these copies. These states are then periodically exchanged which helps in evading local minima. During the complete project phase, variants of REMD known as temperature-REMD and Hamiltonian-REMD were implemented. Along with these a general interface to define custom REMD simulations, improvements to the parallelization, more potentials and many improvements to the whole library in general were introduced.

<p><a href="http://zxcalculus.com" target="_blank">ZX-calculus</a> is a graphical language that can characterize quantum circuits. It is a powerful tool that is usually used for quantum circuit simplification. <code>ZXCalculus.jl</code> will implement quantum circuit simplification algorithms based on ZX-calculus in pure Julia. Also, it will provide interfaces to import and export quantum circuits to the form of YaoIR, an intermediate representation for quantum programs in the <a href="https://github.com/QuantumBFS/Yao.jl" target="_blank"><code>Yao.jl</code></a>. So that one can get quantum circuits with higher performance automatically when designing quantum programs with <code>Yao.jl</code>.</p>

<p>Machine learning models are being used to assess loan and job applications, in bail, sentencing and parole decisions, and in an increasing number of impactful decisions. Unfortunately, due to both bias in the training data and training methods, machine learning models can unfairly discriminate against individuals or groups. While there are many statistical methods to alleviate unfairness, there’s a growing awareness that any account of fairness must take causality into account. This project aims to implement causality-based tools for checking and forcing fairness into the Julia ecosystem of machine learning tools. Specifically, we intend to extend the packages Fairness.jl and MLJ.jl to include methods that implement counterfactual fairness, a recent definition of fairness. We will do so building upon principles of causal graphical models and causal probabilistic programming, as implemented in languages such as Omega.jl and CausalInference.jl.</p>

Classical force fields in molecular dynamics are fast but cannot capture bond breaking, charge transfer, or complex many-body chemistry. Machine learning potentials (MLPs) close this gap — offering near-quantum accuracy at a fraction of ab initio cost. Molly.jl currently supports MLPs only through a Python bridge (ASECalculator), which requires a Python runtime and bypasses Julia's differentiable programming stack entirely. This project delivers native Julia support for four widely-used MLP families: ANI (neural network potential for organic molecules), ACE (linear body-ordered basis expansion), NequIP, and Allegro (E(3)-equivariant graph neural networks). ANI is implemented from scratch in pure Julia using Lux.jl, with pre-trained ANI-1x/ANI-2x weights loaded from HDF5. ACE is integrated via the mature ACEpotentials.jl package. NequIP and Allegro are wrapped from TorchScript-exported models via PythonCall, following the existing ASECalculator pattern. All four potentials plug into Molly.jl's general_inters dispatch loop with zero changes to core simulation code. Pure-Julia models (ANI, ACE) are fully differentiable via Enzyme reverse-mode AD and GPU-accelerated via KernelAbstractions. The result: researchers can run differentiable, ML-accelerated molecular dynamics entirely within Julia — no Python, no overhead, composable with the full SciML ecosystem.

Currently, Symbolics.jl has a Groebner basis implementation which is able to convert a myriad of symbolic expressions into multi-variate polynomials. This can be incredibly useful for solving functions for their roots, but to do so, a root finder for these generated polynomials must be implemented. Moreover, multiple users of Symbolics.jl have requested a symbolic nonlinear solver. As such, the project aims to complete the following: 1- Solve single variable polynomials (even high degree ones n ≥ 4) and multi-variable polynomials (over 2 dimensions or more) 2- Solve transcendental / other types of non-linear equations (highly requested: issues #961, #468, and #866) 3- Ensure that the implementation is well tested and well documented to make it more user-friendly and help out future contributors of the project.

<p>The goal of this project is to implement the IntervalLinearAlgebra.jl package within the JuliaIntervals organisation and have the first release at the end of the programme. JuliaIntervals develops packages related to interval arithmetic, a mathematical framework to perform rigorous computations, treating all quantities as intervals and producing as output of computations an interval guaranteed to contain the true result. Traditional linear algebra routines, such as solving systems of polynomial equations or even just computing the cube of a matrix have been proved to be computationally challenging and designing efficient algorithms for interval matrix computations has been an active research question for a few decades. During this project, state-of-the-art algorithms for interval linear algebra will be reviewed and implemented. As linear algebra is central in practically all numerical applications, the successful completion of the project will allow to expand interval arithmetic methods also to other domains, such as reachability analysis, optimisation and differential equations solvers.</p>

This project aims to implement the necessary infrastructure for the Discontinuous Galerkin method in the finite element toolbox Ferrite.jl. This will include writing examples, documentation, and tests. DG method is a FEM like the continuous Galerkin (CG) method where the test functions are chosen to be the same as the trial functions. However, DG elements do not share degrees of freedom (DoF) at common nodes, introducing extra DoF and adding the process of choosing coupling between elements through numerical fluxes. The main deliverables are: • Methods to integrate on inner faces. • Extend the methods to integrate over subfaces. • Sparsity pattern methods for DG. • Implement infrastructure for handling boundary conditions for DG approximations. • 1-D, 2-D, and 3-D steady heat equation using DG example. • Testing and documentation for all implemented methods.