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<!DOCTYPE html>
<html lang="en">
<head>
<!-- Title -->
<title>About StellarDNN | StellarDNN research Lab</title>
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<meta charset="utf-8">
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<meta name="description" content="StellarDNN is a Harvard research lab focusing on machine learning applications in astronomy and physics. Led by Pavlos Protopapas, we develop neural networks and transformers for astronomical analysis.">
<meta name="keywords" content="StellarDNN, Machine Learning, Astronomy, Physics, Neural Networks, Harvard, Research Lab, Pavlos Protopapas, NeuroDiffEQ, Astromer, NNEHT">
<meta name="author" content="StellarDNN Research Lab">
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<meta property="og:description" content="Harvard research lab applying machine learning to astronomy and physics, developing innovative solutions for astronomical analysis and physical simulations.">
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<h1 class="display-4" itemprop="name">StellarDNN</h1>
<p class="lead" itemprop="description">
StellarDNN is a research lab at the School of Engineering and Applied Sciences (SEAS),
led by Pavlos Protopapas, that focuses on using machine learning
techniques, particularly neural networks and transformers, to study and analyze astronomical
and physical phenomena. We use these techniques to classify celestial objects,
solve differential equations, and extract important features from data such as galaxy images
or time series. By exploring the intersection of astronomy, physics, machine learning, and statistics,
the group aims to gain a deeper understanding of these phenomena
and how they can be characterized and studied.
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<p class="lead">If you are seeking information on current research projects,
please view the list of open projects <a href="active_projects.html">here</a>.</p>
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<span class="d-block display-4 font-size-md-down-4 text-dark text-uppercase mb-0">76+</span>
<span>students</span>
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<span class="d-block display-4 font-size-md-down-4 text-dark text-uppercase mb-0">400+</span>
<span>publications</span>
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<!-- End Stats -->
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<!-- Stats -->
<div class="text-center">
<span class="d-block display-4 font-size-md-down-4 text-dark text-uppercase mb-0">25 </span>
<span>people</span>
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<!-- End Stats -->
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<h2>Latest Publications</h2>
<p><b> <a href="https://arxiv.org/pdf/2403.14763"> Gravitational duals from equations of state</b> </a>
<br> Bea, Y., Jiménez, R., Mateos, D., Liu, S., Protopapas, P., Tarancón-Álvarez, P., Tejerina-Pérez, P.</p>
<p><b> <a href="https://www.cs.drexel.edu/~mancors/papers/HICSS1-2023.pdf"> Behavioral Malware Detection using a Language Model Classifier Trained on sys2vec Embeddings</b> </a>
<br> John Carter, Spiros Mancoridis, Pavlos Protopapas, Erick Galinkin</p>
<p><b> <a href="https://arxiv.org/pdf/2311.15955"> Faster Bayesian inference with neural network bundles and new results for ΛCDM models</b> </a>
<br> A.T. Chantada, S.J. Landau, P. Protopapas, C.G. Scóccola</p>
<p><b> <a href="https://arxiv.org/pdf/2211.00214.pdf"> Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows</b> </a>
<br>R Pellegrin, B Bullwinkel, M Mattheakis, P Protopapas </p>
<p><b><a href="https://arxiv.org/pdf/2212.06965.pdf" > Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networks</b> </a>
<br> O Graf, P Flores, P Protopapas, K Pichara</p>
<p> </p>
</div>
</div>
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