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💫 About Me:

  👋 Hello there! I'm a passionate research software engineer with a strong background in physics, holding a PhD in the field. My expertise lies at the intersection of physics and machine learning technology, where I specialize in developing machine learning algorithms to tackle complex systems and intricate problems. Currently based at the Netherlands eScience Center, I am dedicated to advancing scientific research through the application of robust software engineering principles and innovative solutions. Join me on this exciting journey as we explore the limitless possibilities that emerge when physics meets machine learning! 🚀🚀🚀 #Research #MachineLearning #Physics #SoftwareEngineering

🌐 Socials: LinkedIn

💻 Tech Stack:

Julia Python C C++ Matplotlib NumPy Pandas Plotly PyTorch scikit-learn Scipy

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sciarella's Projects

autogluon icon autogluon

AutoGluon: AutoML for Image, Text, and Tabular Data

gomea icon gomea

Library for optimization with the model-based evolutionary algorithm GOMEA (Gene-pool Optimal Mixing Evolutionary Algorithm)

hoomd-blue icon hoomd-blue

Molecular dynamics and Monte Carlo soft matter simulation on GPUs.

jax icon jax

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

jax-md icon jax-md

Differentiable, Hardware Accelerated, Molecular Dynamics

pydeep icon pydeep

PyDeep is a machine learning / deep learning library with focus on unsupervised learning. The library has a modular design, is well documented and purely written in Python/Numpy. This allows you to understand, use, modify, and debug the code easily. Furthermore, its extensive use of unittests assures a high level of reliability and correctness.

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