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LorenzoVigorelli/README.md

Lorenzo Vigorelli

  • 🔭 Master Degree in Physics of Data student Unipd
  • 🎓 Bachelor Physics graduated Unife. Thesis: "Statistical analysis of binary neutron star mass distribution", analysis of the possibility of inferring the mass distribution of neutron stars from the chirp mass and mass ratios extracted from gravitational wave signals in the mergers of two neutron stars,
  • 👯 Mostly interested in: maching learning, complex systems, networks, computational physics
  • 💬 About me: Basketball fan, referee and player, passionate about technology. I adore mountains and beautiful landscapes
  • 📫 How to reach me: [email protected]

Mine GitHub stats Top Langs

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  1. SBI_RedBloodCells SBI_RedBloodCells Public

    Forked from jacopocarotenuto/SBI_RedBloodCells

    The project aims to explore the Simulation Based Inference to study three stochastic models: Ornstein-Uhlenbeck, Run&Tumble, and a dynamical model of the Blood Cells motion.

    Jupyter Notebook

  2. Distributed-K-Means-Clustering Distributed-K-Means-Clustering Public

    Adaptation of the K-Means clustering algorithm to MapReduce-like architectures, exploiting the parallelization capabilities offered by distributed systems. Final project for the ‘Management and Ana…

    Jupyter Notebook 1

  3. 8-Bit-Cpu-Emulator 8-Bit-Cpu-Emulator Public

    8-bit CPU emulator for running simple ASSEMBLY code, final project for the 'Management and Analysis of Physics Dataset (MOD. A)' course.

    Python

  4. HeartRateEstimation HeartRateEstimation Public

    Forked from jacopocarotenuto/HeartRateEstimation

    Heart Rate Estimation project for Laboratory of Computational Physics at University of Padua

    Jupyter Notebook

  5. BayesianNetworks BayesianNetworks Public

    Bayesian Networks can investigate conditional dependencies from the data: implementation of the K2 algorithm and comparison with standard models.

    Jupyter Notebook

  6. NetworkReconstruction NetworkReconstruction Public

    Forked from paololapo/NetworkReconstruction

    Two approaches to reconstruct a partially corrupted/observed adjacency matrix: bayesian inference and graph neural networks

    Jupyter Notebook