Explore Projects

<p>The project aims to analyze a satellite set of telemetry to understand links and dependencies among different subsystems. The project should be able to demonstrate an understanding of the links between the different behaviour changes of each telemetry within a satellite or within a set of external sources of information (mission plan, solar aspect angles, ephemerides, etc.) in order to rapidly characterize future debris events to support risk analysis, close approach analysis, collision avoidance maneuvering, forensic analysis and other decision making. Machine learning can be used to learn the different link models and storage of acquired knowledge should be stored in a graph (Bayesian network). The intermediate and final output should be represented as data interpretable by a visualization interfaces, preferably in JSON.</p>

<p>Cubesat are now widespread as affordable tools for teaching and researching for Universities and Research Centers. Although they are simple platforms, their complexity can be increased in a budget. One of the elements that increase the capabilities of cubesats is the incorporation of an attitude (and orbit) control system (AOCS). Simulation of the capability of these subsystems is the first step to assess the convenience to include such subsystems in a platform.</p>

<p>Xi-Mac, Xi Editor's official macOS frontend is still very barebones and thus making Xi Editor on Mac not yet fit to act as a developer's main editor. This project aims to implement new front end elements to accommodate upcoming features to Xi such as a language server plugin or improving the find and replace functions. Intended elements to implement for this project include a status bar, an autocomplete menu, a view to show definitions, a quick look navigation view, split views and various other improvements to the general feel and aesthetic of the front end to make Xi feel like a native macOS product.</p>

Implement conflict-free replicated data type (CRDT) synchronization for synchronous multi-author document drafting.

Optimize cosmetic filter rule evaluation routines using SIMD vector instructions for faster initial page load rendering.

<p><a href="https://docs.godotengine.org/en/stable/getting_started/scripting/gdscript/gdscript_basics.html" target="_blank">GDScript</a> is a high-level, dynamic typed python like language. It's one of the supported language of <a href="https://godotengine.org/" target="_blank">Godot Engine</a> developed by it's community.</p> <p>The goal of the project is to implement a documentation system to the language. It'll use comments added above any script member as the documentation. When the source is compiling a documentation for the code will be generated by extracting the comments and type information from the parse tree. The generated documentation could be viewed by the editor and saved as a makrup document.</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>Javis.jl is a graphical animation/visualization package for the Julia Language which fetches motivation from Grant Sanderson's Python based animation engine viz Manim. This proposal aims to bring a more organized experience for creators and developers via layers, powerful abstractions for improved syntactic sugar, aesthetically pleasing animation tools and the ability to livestream animations.</p>

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++.

QuantumClifford.jl is a package designed for simulating Quantum Clifford circuits, circuits that consist of Clifford gates. QuantumClifford.jl can be highly useful for designing Quantum error-correcting codes. Since applying Clifford gates to Stabilizer states is mathematically similar to Linear Algebraic operations, we can utilize GPU to parallelize the process and make it significantly faster. This Summer of Code project consists of implementing the aforementioned Clifford gate operations in GPU kernels, and then seamlessly integrating them into the rest of the QuantumClifford library.

<p>Model zoo is a great compilation of deep learning and reinforcement learning algorithms. Currently state of the art baselines in terms of reinforcement learning and generative models are lacking in this package, which are present in tensorflow and pytorch. This project aims to add state of the art reinforcement learning algorithms like Proximal Policy Optimization and Trust Region Policy Optimization along with multi-modal translation and image captioning networks. These models are complex to implement and thus most users resort to standard tensorflow/pytorch implementations. Adding these to model zoo would attract a lot of researchers.</p>

<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>Julia is a dynamic programming language that, over the past few years, gained interest in the open-source community, especially in the field of scientific computing. Julia programs are executed by a virtual machine that translates the source code, during run-time, to machine code based on the LLVM compiler framework. LLVM provides a variety of analyses and transformation capabilities that are leveraged to optimize programs and facilitate efficient execution. More recently, LLVM was enhanced by a new optimization framework, namely Polly, that supports automatic parallelization and data-locality optimizations based on the polyhedral model. Polly is able to speed up compute kernels significantly, especially in the context of dense linear algebra and iterative stencil computations. In the course of this project I plan to integrate Polly into Julia to enable polyhedral optimizations for Julia programs.</p>

The main task of this project is to implement a modern Bayesian optimization method TuRBO in Julia, extending BayesianOptimization.jl package. The implementation will be based on paper [1]. It will be well tested, documented and integrated into the above mentioned package. The Julia ecosystem and open source community will benefit from this project in various ways since solvers are a key technology used by many methods from the applied sciences. For instance, as mentioned in [1], Bayesian optimization can be used for hyperparameter tuning in machine learning, calibrating a simulator to real world data, chemical engineering and materials discovery and many others. [1] David Eriksson, Michael Pearce, Jacob Gardner, Ryan D Turner, and Matthias Poloczek. Scalable global optimization via local bayesian optimization, 2019

<p>Surrogate modeling has become a staple in large-scale scientific computing applications like aerospace and chemical engineering where full evaluations of the model are too expensive to be directly utilized within analysis contexts. However, with the recent advent of pervasive deep learning through differentiable programming, the ability to mix surrogate techniques with neural network code has become a practical issue. Surrogates.jl is a new surrogate modeling library hosted under the DiffEq umbrella which supports differentiable programming to allow for accelerated deep learning. It is compatible with forward and reverse mode automatic differentiation.</p> <p>My plan is to develop more surrogates methods, such as but not limited to: Compactly supported surrogates, Polynomial expansions, Regularized minimal-energy tensor-product splines, Variable fidelity modeling, Mixture of experts and DENSE.</p>

<p>In the last decades, Bayesian statistics has gained ground in the modelling of phenomena. Despite its advantages, to implement a Bayesian framework is still difficult and restricted because the available literature on Bayesian statistics generally focuses on mathematical formalism and requires greater statistical expertise than some other methods. For this reason, any tool or resource that facilitates the understanding of Bayesian statistics and its mathematical background will be very useful. Turing.jl is a high-performance probabilistic programming bayesian inference language inside Julia ecosystem. Inside Turing lang, MCMCChains.jl represents an implementation of Julia types for analyzing, storing and summarizing MCMC simulations and uses utility functions for diagnostics and results visualizations. This project comprises various tasks to improve different aspects of MCMCChains.jl such as plotting functionality and storage of MCMC sampling to achieve better heuristics.</p>

This project aims to develop a Julia module for simulating and optimizing Bell distillation circuits, addressing the challenge of selecting efficient entanglement purification strategies under varying noise and resource constraints. I will first construct a structured database of Bell distillation circuits with associated performance metrics. I will then design an optimization algorithm that selects the most suitable circuit based on inputs such as target fidelity, error rates, and resource availability. The project will include a simulation framework for evaluating circuit performance under configurable noise models. Deliverables include the circuit database, simulation tools, optimization algorithm, and a well-documented, user-friendly Julia product that integrates with existing quantum simulation workflows. This tool will enable researchers to systematically explore and deploy optimal distillation strategies, improving efficiency and accelerating experimentation in quantum computing.

<p>The <strong>Taylor Models</strong> are mathematical tools that enable the rigorous representation of functions by a polynomial expansion and a remainder that encloses all sources of errors. These kinds of models define it own arithmetic (and composition) of functions in a validated manner, this means that the represented function is guaranteed to be enclosed in some interval.</p> <p>Several methods can be benefited from these models, of particular interest are the interval optimization problems and the validated solution to ordinary differential equations.</p> <p>My project consists of improving the existent methods in <a href="https://github.com/JuliaIntervals/TaylorModels.jl" target="_blank">TaylorModels.jl</a>, a Julia package that implements these models. The improvements include:</p> <ul> <li>Algorithms to tightening the enclosure of the represented functions and its applications to interval optimization. </li> <li>A new approach to rigorously solving ordinary differential equations.</li> </ul>

TopOpt.jl currently supports only linear elastic type problems (meaning a limited set of physics), but, naturally, several other sorts of topology optimization problems are of great interest to practitioners in engineering and product design, namely thermal and fluid flow compliance. Beyond that even, multiphysics problems combining several of these effects are of interest as well. So, we would like to implement and include these sets of physics and the related topology optimization problems in the package. Specifically, we will - implement the necessary numerical backend features to support thermal compliance and rudimentary fluid flow problems, increasing the available feature set, - implement appropriate front-end features to make use thereof, following the package's existing methodology, - and, as a reach, I would like to work on combining some of the above and propose a method for introducing multiphysics capabilities in some capacity. Of course, this all will be documented and tested alongside existing examples from relevant surveys/ the seminal papers.

<p>Probabilistic models, which more naturally quantify uncertainty when compared to their deterministic counterparts, are often difficult and tedious to implement. Probabilistic programming languages (PPLs) have greatly increased productivity of probabilistic modelers, allowing practitioners to focus on modeling, as opposed to the implementing algorithms for probabilistic (e.g. Bayesian) inference. Turing is a PPL developed entirely in Julia and is both expressive and fast due partly to Julia’s just-in-time (JIT) compiler being implemented in LLVM. Consequently, Turing has a more manageable code base and has the potential to be more extensible when compared to more established PPLs like STAN. One thing that may lead to the adoption of Turing is more benchmarks and feature comparisons of Turing to other mainstream PPLs. The aim of this project is to provide a more systematic approach to comparing execution times and features among several PPLs, including STAN, Pyro, nimble, and Tensorflow probability for a variety of Bayesian nonparametric (BNP) models, which are a class of models that provide a much modeling flexibility and often allow model complexity to increase with data size.</p>

<p>Differential equations are widely used in any scientific field. A new method of solving differential equations using deep learning methods has great prospects and have greater versatility over the grid numerical methods. Neural networks can be used as a method for efficiently solving difficult partial differential equations(PDEs). The library that provides the method for solving a general form of PDEs using deep learning will be a useful tool for many scientists and engineers, as well as students.</p> <p>This project aims to design of a general solver for different types of PDEs using deep learning approach base on the <a href="https://arxiv.org/abs/1907.04502" title="PINNs" target="_blank">PINNs</a> algorithm as part of <a href="https://github.com/SciML/NeuralPDE.jl" title="NeuralPDE" target="_blank">NeuralPDE.jl</a> library using the <a href="https://github.com/JuliaDiffEq/ModelingToolkit.jl" title="ModelingToolkit" target="_blank">ModelingToolkit.jl</a> PDE interface.</p>

JuliaBUGS is a modern Julia implementation of the classic Bayesian Inference using Gibbs Sampling — BUGS language for Bayesian modeling, combining BUGS’s intuitive model specification with Julia’s high-performance sampling methods like Hamiltonian Monte Carlo. This project proposes a browser-based graphical model editor for JuliaBUGS, enabling users to visually construct probabilistic graphical models by adding nodes and drawing arrows, instead of writing code. Built using React with TypeScript and React Flow, the web interface will support adding, connecting, grouping, and editing random variables and deterministic nodes via an interactive canvas. Users can specify probability distributions and hyperparameters through intuitive forms. The editor will export model structures to JSON and generate valid JuliaBUGS (or BUGS) code. The GSoC project will focus entirely on delivering a polished, user-friendly frontend with clear usage examples and tutorials. Inspired by tools like DoodleBUGS (for WinBUGS or MultiBUGS), shinystan, and the R Package causact, this editor aims to make model specification more accessible and streamline the Bayesian modeling workflow.

<p><a href="https://github.com/0x0f0f0f/Metatheory.jl/" target="_blank">Metatheory.jl</a> is a general purpose metaprogramming and algebraic computation library for the Julia programming language, supporting the novel technique of e-graph term rewriting through equality saturation, first introduced in <a href="https://egraphs-good.github.io/" target="_blank">egg</a>. Our contribution allows performing general purpose symbolic mathematics, manipulation, optimization, synthesis or analysis of syntactically valid Julia expressions and user defined types, with a clean and concise programming interface, both during compilation or execution of programs. We have been currently experimenting with optimizing mathematical code and equational theorem proving strategies. Some of the proposed tasks for this project include:</p> <ul> <li>Experimenting with <a href="https://github.com/JuliaSymbolics/Symbolics.jl" target="_blank">Symbolics.jl</a> integration and optimization for symbolic mathematics.</li> <li>Experiments in optimizing lowered Julia IRs (possibly by statically overlaying methods with <a href="https://github.com/femtomc/Mixtape.jl" target="_blank">Mixtape.jl</a>), allowing users to define concise rewrite systems that directly optimize Julia code.</li> <li>Experimenting in encoding category theory axioms to efficiently model string diagrammatic equational reasoning, and to produce human readable equational proofs. Possibly, integrate this rewrite system with <a href="https://github.com/AlgebraicJulia/Catlab.jl" target="_blank">Catlab.jl</a></li> </ul>