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andread3vita/README.md

๐Ÿš€ About Me

Hi there!!! My name is Andrea De Vita. Since I started high school, I have been interested in physics. What surprised me the most was the possibility of describing the universe through such tiny objects as particles. As I grew up, I delved into this passion, majored in physics and elaborated these concepts better. Now I am aware of the existence of fundamental interactions that govern the universe, and I'm chasing the dream of being able to investigate these topics firsthand.

During my academic path I have always thought that the best way to contribute in the world of experimental physics is to investigate alternative solutions to the state of the art instruments. For this reason I delved into ML technicques and their application to High Energy Physics problems. Moreover I am interested in the FCC proposal because I am aware of the limitations that are present in nowadays colliders. Indeed, I believe that it is possible to design a new generation of detectors addressing the requirement of possible physics discoveries.

๐Ÿ“จ Contacts

๐ŸŽ“ Education

PHD in Physics @ CERN & UniPd

I am a PHD candidate at CERN working on tracking reconstruction at FCC-ee.

M.Sc. in Physics of Data @ UniPd

I graduated in Physics of Data with a thesis about Hadron Identification with Highly-Granular Calorimeters.

B.Sc. in Physics @ UniPv:

I graduated in Physics with a thesis about a study of performance of a dual readout calorimeter for Future Circular Colliders.

๐Ÿ”† Hobbies

Sport

I used to play basketball when I was a child and I am currently member of the CERN Basketball Club.

Wikipedia

I am Wikipedia User since 2018 and in 2020 I was promoted to autopatrolled user.

Pinned Loading

  1. MuonG2_measurement MuonG2_measurement Public

    Forked from NicoloSalimbeni/advanced_lab_A

    Jupyter Notebook

  2. QuaxProject QuaxProject Public

    Jupyter Notebook 2

  3. SpikeNeuralNetwork_muonDetection SpikeNeuralNetwork_muonDetection Public

    Forked from enlupi/SNN-MUC

    Implementation, training and benchmarking of a Spiking Neural Network for CMS Muon Chambers denoising

    Jupyter Notebook