About me

I was born in Parma, Italy in July 30th 2000. In 2014, with the start of high school, I chose to start studying computer science. In 2019 I started my university studies at "Università degli studi di Parma" in the course "Ingegneria Informatica, Elettronica e delle Telecomunicazioni". I got my Bachelor's Degree in October 2022 and currently i'm studying for my Master's Degree at "Politecnico di Milano" in the course "Computer Science and Engineering" in Milan, Italy .

My plan is to graduate within the next two years and then work for an IT company. I am currently focusing my studies in the area of artificial intelligence and data science. My intent is to specialize more and more in this area.

What i'm doing

  • ai icon

    Artificial intelligence

    I'm currently focusing on studying in the AI field.

  • Web development icon

    Software development

    As a computer scientist, I'm always developing code.

  • Engineering icon

    Engineering

    As engineering student i'm also studying engineering related subjects.

  • Projects icon

    Projects

    I'm always working on new IT projects.

Related institutions

Resume

Education

  1. Politecnico di Milano

    Sep 2022 — Now

    Master's degree student in Computer science and engineering.

  2. Università degli studi di Parma

    Sep 2019 — Oct 2022

    Bachelor's degree, Computer, Electronic and Telecommunications Engineering.
    Grade: 110 / 110

  3. ITIS Leonardo Da Vinci

    Sep 2014 — Jun 2019

    Technical institute diploma, Computer Science.
    Grade: 98 / 100

Experience

  1. IT Support Technician

    May 2019 — Sep 2019

    Self-employed IT support technician, ProntoPro platform.

  2. Network technician

    Jun 2018 — Sep 2018

    ITIS Leonardo Da Vinci, network technician internship.

  3. It Support

    Jun 2017 — Aug 2017

    Data Flow s.r.l, IT support internship.

Certifications

  1. First certificate in English (B2)

    Jun 2022

    Cambridge University Press & Assessment English.
    Score: 174

  2. Test Of English for Interational Communication

    May 2022

    ETS.
    Score: 855

My skills

  • Machine Learning
    70%
  • Deep Learning
    50%
  • NLP
    75%
  • SW development
    80%

Articles

Natural Language Processing and Deep Learning for Bankruptcy Prediction: An End-to-End Architecture

G. Lombardo, A. Bertogalli, S. Consoli, D. R. Recupero, IEEE Access, 2024

Machine and Deep Learning methods are widely adopted to predict corporate bankruptcy events for their effectiveness. Bankruptcy prediction is commonly modeled as a binary classification task over accounting data where the positive label is associated with companies with a high likelihood of bankruptcy and the negative label with a low risk of failure. Most of the models mainly focus on exploiting accounting, stock market data, and data augmentation to deal with the intrinsic unbalance of this task. More recently, financial reports such as the US SEC annual reports have been investigated for feature engineering to boost the accuracy of the classification task. However, these approaches only marginally leverage Natural Language Processing advanced techniques to improve the prediction, by usually only leveraging dictionary-based approaches and word frequencies for feature engineering.

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Document embedding with Transformer for bankruptcy prediction

Andrea Bertogalli, Bachelor's thesis, 2022

The aim of this thesis is to create and implement an end-to-end trainable model that surpasses current models in bankruptcy prediction. The proposed approach combines a multi-head Long Short-Term Memory (LSTM) network and a Transformer. The LSTM network handles financial data, while the Transformer processes 10-K filings from publicly listed U.S. companies on the NYSE and NASDAQ, covering the period from 2000 to 2018. This hybrid architecture is expected to improve performance by leveraging the strengths of both models in analyzing structured financial data and unstructured text.

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