Explore Projects

<p>The ns-3 App Store project aims to support modularization of the ns-3 codebase and federation of development activities. Once this project is complete, ns-3 users will be able to download modules depending on their requirements. Developers can make use of the app store to upload new modules as well. The app store will support dependency management, patch management, bake integration, module grouping, regression testing and also a rating system.</p>

<p>The problem of population genetics can be viewed as a stochastic process. The aim of the project is to understand the reverse transition dynamics of the system conditioned on the end position. In order to understand the feasibility of using reinforcement learning to the problem, agents are tested against stochastic processes that resemble population genetics. Practical evidence is validated to provide a sanity check on the feasibility of deployment of such a method in practice in large-scale problems of population genetics.</p>

<p>GenPipes is an important tool by C3G. GenPipes contains a suite of different software. The software contained in the suite is continuously updated but there is no method as of now to automatically update the software catalog which is available on the C3G Website. The goal of the project is to develop a pipeline to automate the updating of the software stack, while also adding some more metadata which would be relevant to the software.</p>

<p>The objective of the problem arises from the reconstruction of gene genealogies problem - from a given genotypes of sequence data from contemporary individuals and an extended pedigree of genealogical relationships among them we have to decide and be very clear about what we should consider to be nodes information in genealogy tree according to kinds of dependencies among these nodes which should be considered to be edges of the tree. This project will focus on the development of novel algorithms for advanced reconstruction of the gene genealogy by performing inference on graphical models, specifically, implementing the greedy approach and loopy belief propagation algorithm on the succinct tree sequences. The implemented algorithms would undoubtedly be of use to integrate into the existing tskit and msprime libraries.</p>

<p>The goal of the summer project is to revise the current implementation so that it can compute large frequency spectra efficiently. As a result, various inference can be carried on huge sample sizes at a reasonable computation cost. The idea is to track a small subset of entries in the full frequency spectrum and interpolate to recover the full AFS to proceed with integration for larger sample sizes. The key problem is how to recover the frequency spectrum accurately, so several experiments would be carried out to tune parameters and implementations. Also, a framework is expected to be developed for balancing the trade off between computational complexity and data recovery accuracy, that is, deciding which kind of data should be computed with approximation and which should be computed directly.</p>

<p>This project will expand the public accessibility of SegAnnDB as well as work on providing integrations with the UCSC Genome Browser and Galaxy.</p>

This project proposes a complete rewrite of BRL-CAD's existing OGV, which currently uses Meteor.js, an older framework no longer actively maintained. I suggest converting it to a more stable and maintainable framework. I plan to use Next.js, a modern React-based framework, for the frontend. SCSS/SASS will be used for styling, Nest.js will be used as the backend framework for better efficiency and type-safety, and Three.js will be used for modelling 3-D graphics on the web. Additionally, the frontend will be redesigned following industry-standard UI/UX best practices.. Also, it will be made sure to use libraries that have LTS support and are correctly maintained.

Currently tremor supports composition through composing pipelines together, through function composition and through allowing references to query operator definitions and constants in externalizable modules that can be loaded via a module path. It would be excellent if the modularity in tremor extended fully to the query language so that distinct subgraphs could be modularized and consumed by multiple queries to optimise for reuse of flow oriented logic in tremor. This would require extending module support in the tremor query language to support sub-graph definitions with parameters that can be declared and used as part of a higher level query. Modules in tremor-query in their current state: <https://docs.tremor.rs/tremor-query/modules/> This project idea involves designing the sub-graph module syntax and semantics and implementing changes to the lexer, grammar, optimizers and runtime. It is most suited to candidates who are interested in programming language evolution and design.

This project is an extension of the Translation Tracker from the GSoC 2025 to support community translation of the p5.js reference documentation. I'm solving the problem and the gap in the current translation tracker infrastructure. P5.js documentation currently has five languages (English, Spanish, Simplified Chinese, Korean, and Hindi). P5.js is growing, and we need an infrastructure to support the translations. The goal is to improve/add GitHub Actions and workflows that assist human translation by volunteer translators, not to support the AI agents' translation.

<p>Markdown provides a great ability to format messages and write structured documents using plain text format.</p> <p>Zulip uses two different markdown processors: for the backend, it is <a href="https://github.com/Python-Markdown/markdown" target="_blank">Python-Markdown</a> and for the frontend, it is <a href="https://github.com/markedjs/marked" target="_blank">marked.js</a>.</p> <p>While the backend markdown processor is highly extensible but the current frontend markdown processor doesn’t allow the same. Moreover, it is not upgradeable because of the Zulip customisations.</p> <p>The goals of this project are:</p> <ol> <li>Refactor the frontend-markdown processor with CommonMark parser.</li> <li>Make the same extensible for Zulip’s custom features like @-mentions.</li> </ol> <p>I’ll take the reference from <a href="https://github.com/commonmark/commonmark.js" target="_blank">commonmark.js</a> to write the CommonMark parser for our frontend-markdown processor. And to make it extensible, I'll use the upstream <a href="https://github.com/markedjs/marked" target="_blank">marked.js</a>.</p> <p>In parallel to this, I’ll focus on works in the markdown area. Integrating them with the Zulip will help to increase the overall users’ experience. The list of them are:</p> <ol> <li><a href="https://github.com/zulip/zulip/issues/17971" target="_blank">Add support for link shortener</a></li> <li><a href="https://github.com/zulip/zulip/issues/2401" target="_blank">Re-enable backslash escape inline pattern</a></li> <li><a href="https://github.com/zulip/zulip/issues/2183" target="_blank">Add support for online mentions</a></li> <li><a href="https://github.com/zulip/zulip/issues/13746" target="_blank">Add support for importing avatar from GitHub on account creation</a></li> </ol>

Markdown provides a great ability to format messages and write structured documents using plain text format. Zulip uses two different markdown processors: for the backend, it is Python-Markdown and for the frontend, it is marked.js. While the backend markdown processor is highly extensible but the current frontend markdown processor doesn’t allow the same. Moreover, it is not upgradeable because of the Zulip customisations. The goals of this project are: Refactor the frontend-markdown processor with CommonMark parser. Make the same extensible for Zulip’s custom features like @-mentions. I’ll take the reference from commonmark.js to write the CommonMark parser for our frontend-markdown processor. And to make it extensible, I'll use the upstream marked.js. In parallel to this, I’ll focus on works in the markdown area. Integrating them with the Zulip will help to increase the overall users’ experience. The list of them are: Add support for link shortener Re-enable backslash escape inline pattern Add support for online mentions Add support for importing avatar from GitHub on account creation

<p>The OpenMRS platform can be extended via two kinds of add-ons: (1) Modules, and (2) Open Web Apps.</p> <p>Originally we built an "OpenMRS Module Repository" where people could upload their modules, but now days there are lots of other repositories where people can publish the code they build, and we want to support a distributed ecosystem that allows people in the community to publish their OpenMRS add-ons wherever they want, as GitHub Releases, to Bintray, to Maven, etc. So the Module Repository has been replaced with an "Add-On Index."</p> <p>The goal of this project is to make various enhancements to OpenMRS Addons, making it easier for end-users to sign up for updates, show module and tag stats, and add support for Github releases.</p>

The website lacks visual consistency, accessibility features, and a dark mode—critical elements for enhancing the experience of developers and engaging contributing organizations. To address this, I will follow a two-phase approach: first, planning and designing UI components and layouts.; second, implementing these designs using React.js, Next.js, TypeScript, and Tailwind CSS v4.0. Deliverables include a responsive, accessible interface, reusable component system, robust dark mode support, and performance-optimized code.

Gemma Facet is a comprehensive platform that provides an end-to-end solution for fine-tuning Gemma language models through a microservices architecture. The platform implements four core services: dataset preprocessing with support for local and Hugging Face datasets, automated training jobs using Unsloth and Transformers libraries, model inference capabilities, and flexible export functionality supporting multiple formats (adapters, merged models, and GGUF). The backend leverages Google Cloud Run services for scalable compute, Firestore for database operations, and Google Cloud Storage for artifact management. The frontend is built with Next.js, Tailwind CSS, and Shadcn UI, providing an intuitive dashboard interface. Infrastructure is managed through Terraform with containerized services using Docker. The system handles complex workflows including dataset splitting configurations, asynchronous training job management, and multi-format model export pipelines. Key technical implementations include Cloud Run Job integration for resource-intensive operations, comprehensive API design with full documentation, and optimized data processing pipelines for efficient model fine-tuning workflows.

<p>Current Red Hen Lab’s Audio Pipeline can be extended to support speech recognition. This project proposes the development of a deep neural-net speech to text module for the pipeline, based on the paper Deep Speech. The aim is to use both audio and visual modalities for achieving speech recognition.</p> <p>The initial goal is to extend current Deep Speech model (audio only) to Red Hen lab's TV news videos datasets. The next goal is to develop a multi-modal Speech to Text system (AVSR) by extracting visual modalities and concatenating them to the previous inputs.</p> <h5>Project Outline</h5> <p>I plan to develop four versions of Speech to Text during 12 weeks time.</p> <ul> <li>Version 1: Rewriting the Deep Speech model to support audio inputs from Red Hen Lab datasets. </li> <li>Version 2: Improving results using either N-gram model or a spell check system</li> <li>Version 3: Extracting visual features, concatenate them with Audio features and modify Deep Speech’s Input</li> <li>Version 4: Improving results using the same approach as in 2, tracking actual speaker’s lips, etc.</li> </ul>

This project evaluates large language models on frontend coding and UI generation tasks by extending and integrating ideas from open-source coding benchmarks such as Multi-SWE-bench and WebDev Arena. The evaluation framework assesses coding and UI generation capabilities across various dimensions, with particular emphasis on multimodal features for frontend development. It leverages the models’ ability to generate, render, and iteratively refine solutions using visual feedback from rendered UIs, and supports multiple frontend frameworks (React, Vue, Angular, Svelte, Next.js) alongside single-file HTML tasks. The solution involves building a modular pipeline with typed configuration, provider adapters (Ollama, vLLM, OpenRouter), and a model manager; implementing multimodal evaluation loops that incorporate screenshots and structured judging; supporting iterative evaluation (allowing models to refine solutions over multiple attempts); expanding the task taxonomy with increasingly complex frontend challenges (calculators, dashboards, interactive apps, framework-based projects); and automating the entire evaluation pipeline with reproducible runs and detailed artifact logging. Deliverables include the enhanced evaluation framework with new benchmark tasks, structured outputs and reports (JSON, screenshots, summaries), automation scripts for reproducible runs, and thorough documentation. The framework provides a transparent and extensible way to compare advanced AI coding capabilities in frontend development without relying on a leaderboard, focusing instead on rigorous, reproducible benchmarking.

Problem Statement: The current MyUoM app for Greek universities is a central hub for students and faculty, but it's missing some key features. Right now, it lacks a solid login system, a scalable backend, and real-time updates from official university sources. Plus, there’s no easy way for other universities to customize or integrate the platform to fit their needs. This makes the app less flexible and limits its potential to be a truly unified solution for higher education in Greece. Proposed Solution: I’m proposing MyUni—a unified, open-source platform that fixes these headaches. Built with a modern MERN stack (Next.js, Express.js, MySQL), MyUni will offer a scalable backend, real-time data syncing via APIs, and a flexible CMS for announcements and updates. It’ll support multiple universities with customizable branding and layouts, all wrapped in a user-friendly interface. Using TypeScript, microservices, and tools like Nginx and Redis, I’ll ensure it’s fast, secure, and ready to grow. Deliverables: By the end of GSoC 2025, I’ll deliver: - Web application that provide good user experience(UX) and has good User Interface(UI). - Admin dashboard where myuni admin can able to handle CMS and more. - Student dashboard where student can able to take-care of their profile, notifications of academics, and more. - Backend Architecture which serves as solution to all project requirements. - REST APIs with at least 95% test coverage. - REST APIs with modern design patterns. - Docker container image of entire app.

<p>The proposal mainly focuses on Convolutional Neural Network based object detection module to be integrated with the openDetection library. The library that will be used for this is caffe. The targets that will be accomplished are:1) Re-designing the library for CPU and GPU compilation modes. 2) Implement a way to invoke Caffe open source library from the OpenDetection module with a user-friendly code based way ( this will include a tinge of GUI support for instant access). 3) Implement open source guidance and codes for state-of-the art object localization problems(hypothesis generation) specifically based on selective-search and convolutional neural network (CNN) approaches. 4) Adding a ground-truth annotation tool to the module with a graphical-user-interface support. Implementing short, but effective modules like mixed-pooling, recurrent networks to the Convolutional Neural Networks Training dependent on the invoked caffe library. 5) Adding context based learning CNNs. 6) Adding user-interface to train and test CNN based classifiers and object detectors. 7)Adding documentation for the above</p>

The data space approach aims at creating a data federation environment without the overhead of “enterprise” data integration. The concept of data spaces is gaining more and more importance, as this approach offers an opportunity for different stakeholders (data owners, including public bodies and municipalities, service providers offering AI-as-a-service algorithms , service consumers, regulatory bodies, etc.) to collaborate. There are several domains from eHealth to education where this kind of data/service federation is expected to have an important role in the near future. Blockchain is a natural candidate to support several aspects and steps of such collaborations. However, current initiatives do not really seem to fully exploit the possibilities of HFL (and its support for designing privacy-preserving applications with channels and Private Data Collection). The Eclipse Dataspace Connector, maintained by a coalition of European organizations, implements the International Dataspace Standard and also serves as a focal point of the Gaia-X. It is designed to connect multi-cloud environments and has an extension mechanism towards different cloud storage technologies. However, currently these extensions only support storage over designated cloud-based services, but do not offer consortial blockchain connection. The aim of the mentorship would be to develop an extension which support storing data over Hyperledger Fabric as data pane, relying on the Private Data Collection.

CampWiz NXT is the jury management platform used during Wiki Loves contests to evaluate media submissions, images, audio, and video, uploaded to Wikimedia Commons. It provides a structured environment for jurors and organisers to assess, score, and process contest entries at scale. The current implementation uses a dual-server architecture: a Next.js frontend and a Golang backend. While functional, this setup introduces real costs. There is server component complexity, a persistent Node.js process required for deployment, and an interface that places more cognitive burden on jurors than the work itself demands. These are solvable problems, and solving them is what this project is about. The goal is to migrate the frontend from Next.js to a pure React application built as a static output with Vite. This is not a surface-level reskin. The migration removes the Node.js server dependency entirely, allowing the frontend to be served over standard HTTP, making deployment simpler, cheaper, and more maintainable. The Golang backend remains unchanged; only the frontend layer is being replaced. Alongside the technical migration, the interface will be redesigned with a deliberate focus on clarity and minimalism. Jurors should be able to open the tool and immediately focus on reviewing submissions, not on navigating a cluttered interface. Non-essential actions will be removed from the juror viewport, decision controls will be streamlined, and keyboard accessibility will be enforced throughout. The organiser dashboard will receive the same treatment: clean workflows for contest setup, juror assignment, and results management, without requiring backend access for routine configuration. The result will be a frontend that is easier to deploy, easier to maintain, and noticeably better to use.

<p>Nginx is the second most popular web server and the most popular among high-traffic websites. OpenResty is a high performance web platform extended from core Nginx and supports integration of Lua modules. Though still there is a lack of any specialised solution for authorization based on access control models. This real need can be addressed by Casbin perfectly since it supports the 3 major access control models (ACL, RBAC, ABAC). This project aims to implement Casbin’s functionality entirely in Lua and support it as an OpenResty module.</p>

<p>Casbin is an authorization library. Its support for diverse programming languages, different access control models and the powerful adapters gains it the greatest popularity in the open source community. However, there are still lots of difficulties in utilizing Casbin at the frontend, especially applying it on the management of users’ accessibility to specific UX widgets. In the GSoC 2020, I wish to implement a friendly JS library (aka Casbin.js) and other supported facilities to extend Casbin to the frontend development. This project mainly includes a plug-and-play Javascript library that supports for seamless connection with server, auto page controlling and the corresponding components and plugins for modern frontend frameworks.</p>

<p>This project aims at making certain changes to the existing OpenMRS Add Ons infrastructure thereby making it fit as a complete replacement for the existing OpenMRS modulus. The main motive behind this project is to be able to completely retire OpenMRS modulus whose codebase has become tough to maintain. OpenMRS Add Ons also supports OWA’s which is a vital feature that Modulus lacked. The developer is now free to host his module in any of the supported hosting sites while Add Ons does the job of adding it to the OpenMRS module index . Add Ons also gives the freedom of choice of hosting location in hands of the module developer . Add Ons is built on a light framework and hence is hopefully easier to maintain as compared to Modulus.</p>