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gsoc.html
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<!DOCTYPE html>
<html lang="en">
<head>
<!-- Required meta tags -->
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1, shrink-to-fit=no"
/>
<!-- Bootstrap Imports -->
<link
href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.2/dist/css/bootstrap.min.css"
rel="stylesheet"
/>
<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.0.2/dist/js/bootstrap.bundle.min.js"></script>
<link rel="stylesheet" href="https://unpkg.com/aos@2.3.4/dist/aos.css" />
<link rel="stylesheet" href="./style.css" />
<link rel="stylesheet" href="./infostyle.css" />
<link rel="stylesheet" href="panelstyle.css" />
<link rel="icon" href="./logos/eq.ico" type="image/x-icon" />
<script src="svg-inject.min.js"></script>
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.6.4/jquery.min.js"></script>
<link
rel="stylesheet"
href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css"
/>
<meta property="og:site_name" content="equadratures" />
<meta property="og:type" content="website" />
<link rel="image_src" href="logos/figure.png" />
<meta property="og:image" content="logos/figure.png" />
<meta property="og:image:url" content="logos/figure.png" />
<meta property="og:image:secure_url" content="logos/figure.png" />
<meta property="twitter:image" content="logos/figure.png" />
<meta property="og:url" content="https://equadratures.org/" />
<meta
property="og:title"
content="Learn Continuous Structure | equadratures"
/>
<meta
property="og:description"
content="equadratures is an open-source python framework for fitting models to data. It may be used for polynomial regression, sparse polynomial approximation, subspace-based dimension reduction, polynomial chaos, and more recently for kernel-based learning. Underneath the hood it leverages numerical quadrature and orthogonal polynomials."
/>
<meta property="og:image" content="./icons/logo_new.png" />
<meta property="og:image:url" content="./icons/logo_new.png" />
<meta property="og:image:secure_url" content="./icons/logo_new.png" />
<meta name="twitter:card" content="summary_large_image" />
<meta
name="twitter:description"
content="equadratures is an open-source python framework for fitting models to data. It may be used for polynomial regression, sparse polynomial approximation, subspace-based dimension reduction, polynomial chaos, and more recently for kernel-based learning. Underneath the hood it leverages numerical quadrature and orthogonal polynomials."
/>
<meta name="twitter:site" content="@equadratures" />
<meta
name="twitter:title"
content="Learn Continuous Structure | equadratures"
/>
<meta name="twitter:creator" content="@equadratures" />
<meta name="twitter:url" content="https://twitter.com/equadratures" />
<title>Google summer of code | equadratures</title>
<meta
name="description"
content="equadratures is an open-source python framework for fitting models to data. It may be used for polynomial regression, sparse polynomial approximation, subspace-based dimension reduction, polynomial chaos, and more recently for kernel-based learning. Underneath the hood it leverages numerical quadrature and orthogonal polynomials."
/>
<meta
name="keywords"
content="data science, machine learning, polynomial chaos, uncertainty quantification, model auditing, Pranay Seshadri"
/>
</head>
<body class="body light-mode">
<div id="header"></div>
<main style="margin-top: 62px" data-aos="fade-up">
<div class="container">
<div class="row align-items-center panelcontainer mx-1 my-3">
<div class="panel col-12 mx-auto align-items-center">
<div class="card shadow border panelBackground">
<img
src="logos/gsoc.png"
class="card-img-top mx-auto"
style="max-width: 500px; padding: 20px"
/>
<div
class="card-body d-flex flex-column align-items-center light-mode"
>
<div
class="card-text"
style="font-family: DIN Pro; color: #2a3987"
>
Through the NumFOCUS organisation, equadratures participates
in the Google Summer of Code. This programme seeks to involve
undergraduate students in the development of open-source
tools. Individual equadratures’ projects are designed to
empower students with useful mathematical and statistical
machinery, while helping the broader user community. Students
are mentored by our development team and encouraged at each
step of the GSOC programme.
<br />
<br />
Our past students are listed below.
<ul style="font-family: DIN Pro; color: #2a3987">
<li>
2020 Barney Hill <br />
Polynomial regression trees with orthogonal polynomials.
</li>
<li>
2021 Simardeep Singh Sethi <br />
Developing an app for uncertainty quantification using
polynomial chaos methods.
</li>
</ul>
</div>
</div>
</div>
</div>
</div>
</div>
<div id="footer"></div>
</main>
<!-- partial -->
<script src="https://unpkg.com/aos@2.3.4/dist/aos.js"></script>
<script src="./script.js"></script>
<script>
loadHeader();
loadFooter();
</script>
</body>
</html>