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<section class="cosmic-home" data-cosmic-home data-default-preset="cosmicWeb">
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    <p class="cosmic-home__eyebrow">Astrophysics x Graph Machine Learning</p>
    <h1>Dakshesh Kololgi</h1>
    <p class="cosmic-home__subtitle">
      PhD researcher at UCL building graph-based machine learning methods to connect galaxies with their dark matter cosmic web environments.
    </p>
    <div class="cosmic-home__actions">
      <a class="cosmic-btn cosmic-btn--primary" href="https://arxiv.org/abs/2512.05909" target="_blank" rel="noopener noreferrer">Read Latest Paper</a>
      <a class="cosmic-btn" href="{{ '/about-me/' | relative_url }}">Research Interests</a>
      <a class="cosmic-btn" href="{{ '/blog/' | relative_url }}">Blog</a>
      <a class="cosmic-btn" href="{{ '/publications/' | relative_url }}">Publications</a>
      <a class="cosmic-btn" href="{{ '/assets/pdf/cv.pdf' | relative_url }}" target="_blank" rel="noopener noreferrer">CV</a>
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<h2>Research Interests</h2>

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  <article class="research-card">
    <h3>Graph ML for Cosmic Structure</h3>
    <p>Learning physically meaningful graph representations of galaxy environments using geometry-aware metrics.</p>
  </article>
  <article class="research-card">
    <h3>Galaxy-Environment Connection</h3>
    <p>Studying how galaxy properties evolve across voids, walls, filaments, and clusters in the cosmic web.</p>
  </article>
  <article class="research-card">
    <h3>Simulation to Survey Transfer</h3>
    <p>Bridging simulation-grounded models toward observational datasets including DESI-era analysis.</p>
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    <h3>Interpretable Scientific AI</h3>
    <p>Designing ML pipelines that remain transparent, testable, and anchored in astrophysical understanding.</p>
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<h2>Latest Paper</h2>

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  <h3>Learning the Cosmic Web: Graph-based Classification of Simulated Galaxies by their Dark Matter Environments</h3>
  <p>
    We introduce a three-stage simulation framework: T-Web environment labels, graph features from Delaunay triangulation,
    and a graph attention network (GAT) classifier.
  </p>
  <ul>
    <li>Task: classify each galaxy as void, wall, filament, or cluster from local structure.</li>
    <li>Result: 85% accuracy for galaxies with stellar mass above 10^9 M_sun.</li>
    <li>Impact: graph-based representations improve environment inference and support future DESI-focused studies.</li>
  </ul>
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    <a class="cosmic-btn cosmic-btn--primary" href="https://arxiv.org/abs/2512.05909" target="_blank" rel="noopener noreferrer">View on arXiv</a>
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