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FrancescoCeron/README.md

Francesco Ceron

MSc student in Data Science at the University of Padova with a background in Statistics for Economics and Business.

My interests mainly focus on:

  • Machine Learning
  • Optimisation for Data Science
  • Forecasting & Econometrics
  • Statistical Modelling
  • Computer Vision
  • Quantitative Data Analysis

I enjoy working on applied quantitative projects involving optimisation, forecasting, machine learning and complex data analysis across different domains.

Selected Projects

Electric Vehicle Demand Forecasting

Time series forecasting of electric and hybrid vehicle registrations in the UK using ARIMA, GAM, ARIMAX and exponential smoothing models.

Gradient Methods for Semi-Supervised Learning

Implementation and comparison of gradient-based and coordinate descent optimisation methods for graph-based semi-supervised learning.

Portfolio Optimization with Frank-Wolfe Variants

Projection-free optimisation methods for constrained mean-variance portfolio optimisation.

Deep Belief Networks on Fashion-MNIST

Computer vision project based on Deep Belief Networks and Restricted Boltzmann Machines implemented in PyTorch.

Technologies

  • Python
  • R
  • SQL
  • PyTorch
  • NumPy
  • SciPy
  • Pandas
  • Matplotlib
  • Jupyter Notebook
  • Git & GitHub

Interests

Optimisation • Machine Learning • Forecasting • Econometrics • Statistical Learning • Computer Vision

Pinned Loading

  1. seeg-network-analysis-seizure-prediction seeg-network-analysis-seizure-prediction Public

    SEEG network analysis and seizure prediction using pairwise and higher-order brain connectivity, clustering and machine learning techniques.

    Jupyter Notebook

  2. deep-belief-networks-fashion-mnist deep-belief-networks-fashion-mnist Public

    Deep Belief Network implementation for Fashion-MNIST image classification using Restricted Boltzmann Machines and PyTorch.

    Jupyter Notebook

  3. electric-vehicle-demand-forecasting electric-vehicle-demand-forecasting Public

    Time series forecasting of electric and hybrid vehicle registrations in the UK using ARIMA, GAM, ARIMAX and exponential smoothing models.

    HTML

  4. gradient-methods-semi-supervised-learning gradient-methods-semi-supervised-learning Public

    Implementation and comparison of gradient-based and Gauss-Southwell optimization methods for semi-supervised learning.

    Jupyter Notebook

  5. portfolio-optimization-frank-wolfe portfolio-optimization-frank-wolfe Public

    Projection-free optimization methods for constrained mean-variance portfolio optimization using Frank-Wolfe variants.