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<!DOCTYPE HTML>
<!--
Strata by HTML5 UP
html5up.net | @ajlkn
Free for personal and commercial use under the CCA 3.0 license (html5up.net/license)
-->
<html>
<head>
<title>Home page</title>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no" />
<link rel="stylesheet" href="assets/css/main.css" />
<meta name="google-site-verification" content="UaHqwqLzAq794d7LVmDi7cPioSdjJV8dwqEUoTDHQtU" />
</head>
<body class="is-preload">
<!-- Header -->
<header id="header">
<div class="inner">
<a href="#" class="image avatar"><img src="profile_photo.jpg" alt="" /></a>
<h1><strong>My name is Kimia Shayestehfard</strong>, and I am a machine learning scientist.</h1>
</div>
</header>
</body>
<!-- Main -->
<div id="main">
<!-- One -->
<section id="one">
<header class="major">
<h2>About Me</h2>
</header>
<p>I recently completed my PhD in Electrical and Computer Engineering at Northeastern University, Boston, MA. Under the supervision of Prof. Stratis Ioannidis and Prof. Dana Brooks, my doctoral research focused on "Permutation Invariant Graph Learning". <br />
My research interests lie in the fields of machine learning, graph mining, and optimization. I'm passionate about developing innovative algorithms and models that can improve the efficiency and effectiveness of real-world systems. <br />
During my PhD studies, I designed and implemented five impactful projects utilizing Python, PyTorch, and TensorFlow. These projects have resulted in the publication of four papers in prestigious data mining conferences and journals. Additionally, I've recently submitted another project to a prominent data mining conference. <br />
<h3>Interests:</h3>
- Machine Learning
- Data Mining
- Graph Neural Networks
- Deep Generative Models (VAE, RNN, GAN)
</p>
<ul class="actions">
<li><a href="https://drive.google.com/file/d/1tZpSGwkDOOizHC6OofKG5ihTevHoWyK_/view?usp=drive_link"
class="button">My resume</a></li>
</ul>
</section>
<section id="dobulecolumn-section">
<div class="column">
<h2>Academic Background</h2>
<ul class="no-spacing">
<li>
<strong>PhD in Electrical and Computer Engineering (2018 - 2023)</strong></br>
Northeastern University (Boston, MA)
</li>
<li>
<strong>MSc in Electrical and Computer Engineering (2016 - 2018)</strong></br>
Northeastern University (Boston, MA)
</li>
<li>
<strong>BSc in Electrical and Computer Engineering (2009 - 2013)</strong></br>
Shiraz University (Shiraz - Iran)
</li>
<h2>Awards</h2>
<li>
<strong>Graduate Research Assistantship award (Sep 2016 - June 2023)</strong></br>
Northeastern University (Boston, MA)
</li>
<li>
<strong>SDM’23 Doctoral Forum travel award (April 2023)</strong></br>
</li>
</div>
<div class="column">
<h2>Most Recent Work Experience</h2>
<ul class="no-spacing">
<li>
<strong> Scalable Graph Transfer Learning (Fall 2022- Spring 2023, Northeastern University, Boston, MA)</strong></br>
Designed and implemented a Python and PyTorch-based framework for graph transfer learning operating on large graphs with sparse node labels.
</li>
<li>
<strong> AlignGraph: A Group of Generative Models for Graphs (Spring 2019 - Fall 2022, Northeastern University, Boston, MA)</strong></br>
Designed and implemented a group of generative models that combine fast and efficient graph alignment methods with a family of deep generative models (e.g., VAE, RNN, GRAN) that are invariant to node permutations using Python, Tensorflow and Pytorch <a href="https://epubs.siam.org/doi/pdf/10.1137/1.9781611977653.ch31">[paper]</a> <a href="https://github.com/shayestehfard/AlignGraph">[code]</a>.
</li>
<li>
<strong> Fast and Efficient n-Metrics for Multiple Graphs (Spring 2022, Northeastern University, Boston, MA)</strong></br>
Designed and implemented a framework to accelerate computation of multi-graph distances with graph coarsening using Python and NetworkX <a href="http://www.mlgworkshop.org/2022/papers/MLG22_paper_8073.pdf">[paper]</a> <a href="https://github.com/shayestehfard/AlignGraph">[code]</a>.
</li>
<li>
<strong> Graph Transfer Learning (Spring 2021 - Summer 2021, Northeastern University, Boston, MA)</strong></br>
Designed and co-developed a novel methodology for solving the graph transfer learning problem in a non-combinatorial fashion, using Python, Tensorflow and Keras <a href="https://par.nsf.gov/servlets/purl/10313472">[paper]</a> <a href="https://github.com/shayestehfard/GraphTransferLearning-NEU">[code]</a>.
</li>
</div>
</section>
<!-- Two
<section id="two">
<h2>Get In Touch</h2>
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lorem semper nunc nisi lorem vulputate lorem neque lorem ipsum dolor.</p>
<div class="row">
<div class="col-8 col-12-small">
<form method="post" action="#">
<div class="row gtr-uniform gtr-50">
<div class="col-6 col-12-xsmall"><input type="text" name="name" id="name"
placeholder="Name" /></div>
<div class="col-6 col-12-xsmall"><input type="email" name="email" id="email"
placeholder="Email" /></div>
<div class="col-12"><textarea name="message" id="message" placeholder="Message"
rows="4"></textarea></div>
</div>
</form>
<ul class="actions">
<li><input type="submit" value="Send Message" /></li>
</ul>
</div>
<div class="col-4 col-12-small">
<ul class="labeled-icons">
<li>
<h3 class="icon solid fa-home"><span class="label">Address</span></h3>
1234 Somewhere Rd.<br />
Nashville, TN 00000<br />
United States
</li>
<li>
<h3 class="icon solid fa-mobile-alt"><span class="label">Phone</span></h3>
000-000-0000
</li>
<li>
<h3 class="icon solid fa-envelope"><span class="label">Email</span></h3>
<a href="#">[email protected]</a>
</li>
</ul>
</div>
</div>
</section> -->
<!-- Footer -->
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<li><a href="https://twitter.com/KimiaShayesteh1"
class="icon brands fa-twitter"><span class="label">Twitter</span></a></li>
<li><a href="github.com/shayestehfard" class="icon brands fa-github"><span
class="label">Github</span></a></li>
<li><a href="https://www.linkedin.com/in/kimia-shayestehfard-5814a775/" class="icon brands fa-linkedin"><span
class="label">Linkedin</span></a></li>
<li><a href="https://scholar.google.com/citations?user=KDDHIEIAAAAJ&hl=en"
class="icon brands fa-google scholar"><span class="label">Google scholar</span></a></li>
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</body>
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