Each compression algorithm used by Zarr is assigned an identifier by the Numcodecs library. To make these identifiers useful in other programming...

The National Ecological Observatory Network (NEON) collects and provides long-term, open-access ecological data. The NEON Data API provides access to...

qutip-qip currently does not perform optimisation and scheduling at gate-level in quantum circuits. Allowing circuits to be imported from qiskit will...

Projection predictive inference is a decision theoretic Bayesian approach that decouples model estimation from decision making. Given a reference...

CVXPY is a Python-embedded modeling language for convex optimization problems. In this project, I aim to add support for new advancements in...

This project aims to develop interfaces for the SimpleChains.jl library for use with SciML packages, so that the former’s non-allocating and high...

The study of urban morphology relies on clever abstractions of the built environment into computationally tractable models. One such model, the...

The project aims to fill the current gap in the creation of a baseline urban grid that allows for network analysis of the morphology of street...

Gaussian Processes (GPs) are a flexible class of probabilistic models which can automatically fit quite complex functions. In addition, GPs provide a...

Electromagnetic devices have become ubiquitous in our lives due to their limitless applications. For designing and optimizing such devices,...

Time Series models constitute an integral part of the machine learning stack. Time series tasks can span a wide range of tasks such as...

Quantum systems are very costly to simulate on a classical computer which can result in exceptionally long computation times. This projects aims to...

Several multivariate distributions in PyMC, such as MvNormal, MvStudentT, and others, are constrained to working with 2D inputs and do not function...

For simulating a system that contains many variables and/or equations, traditionally computational engineers tend to focus on utilizing an...

This project focuses on porting Plots recipes for SciML to Makie and adding to QuantumNLDiffEq.jl. Completion of the former would allow users to...

ArviZ's purpose is to provide backend-agnostic tools for exploratory analysis of Bayesian models. To do so, we transform data from probabilistic...

The purpose of this project is to improve Surrogates.jl by adding two new surrogate models - GEKPLS and GENN - and to make fixes and updates to...

As the project title describes the main goal is to create a model class that builds, saves, loads, predict and fit user created models. This can be...

Facility location modeling, a set of geoanalytic methods, is a critical problem solving tool leveraged in public and private sector decision making...

This project aims to improve the existing deep learning for computer vision ecosystem in Julia, especially by adding more models that users can use...

For many users of statistical software, the most important aspect of a statistical software package is convenience. Therefore, it is important to...

The aim of this project is to make the currently running forecasting system capable of parallelization. This is needed because the forecasting system...

Graph Isomorphism is an exciting topic regarding networks, but also a fairly complex problem to solve. The goal of the project is the full...

The project would involve creating a pipeline from data acquisition to simulation to overlaying the visualisation in an extended reality (XR)...

This project aims to improve and populate the existing benchmark framework of PyBaMM. This will be achieved by - 1. Adding new benchmarks for...

Implement the “Modular Primitives for High-Performance Differentiable Rendering” paper in Julia, making use of JuliaGPU. Allowing us to compare the...

The aim is to build the textual portions of the FastAI.jl package, inspired by the fastai Python library, which will provide high-level components...

Currently, exact inference for Gaussian processes within PyMC makes heavy use of Cholesky decompositions that have a cubic asymptotic time...

Multi-output Gaussian processes (MOGPs), a type of Bayesian nonparametric models, have recently gained strong attention from researchers, and become...

QuTiP uses Jupyter Notebooks to demonstrate its functionality in the field of quantum physics and offers guidance to new users. While QuTiP is...

Arviz, which is designed to be a backend-agnostic tool for diagnostics and visualization of Bayesian inference in Python, has encountered a number of...

A small benchmark suite already exists for CVXPY. The suite measures both the time to compile a problem into the solver standard form, and the time...

This project aims to make the model which would already be trained for the classification of species and detection of alive and dead, trees or birds...

This project is to add a layer of data abstraction on top of taskflow's original pipeline, so that users can either choose to prepare the data buffer...

In Bayesian statistics, one way to circumvent positing parametric assumptions is to use Dirichlet Processes (DP) as priors for probability...

This project aims to implement the sequence space jacobian methods of Auclert et al. (2021) into the Heterogeneous Agents Resources and toolKit...

The project aims to enhance the coverage of SciPy functions in CuPy. The stretch goal is to introduce interpolate module in CuPy. I'm excited about...