Tommaso Lazzari

Tommaso Lazzari

@TommasoLazzari

Statistics and DataScience student

Padova, Italy
1
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8
Public Repos
0
Private Repos

Language Breakdown

Lines of code distribution across 8 owned repositories

1.8M Total LOC
Jupyter Notebook
1,371,452 lines
77.7%
N/A
Python
284,978 lines
16.1%
N/A
R
109,382 lines
6.2%
N/A
I

I-Shaped Developer

I-shaped

Specialist — deep expertise in Jupyter Notebook

Jupyter Notebook
Python
R

Collaboration Network

Global Impact visualization

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Tommaso Lazzari
0 active collaborators

Repos

8

PRs

0

Growth

+18%

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Coding Streak

Contribution activity over the past year

1 day
80
Contributions
72
Commits
0
Pull Requests
Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun
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Top Repositories

DMAGNet

A convolutional neural network designed for galaxy morphology classification with a strong emphasis on model interpretability.

1 1
Python
TommasoLazzari
0 0
PortfolioOptimization_EfficiencyTesting

Optimization of portfolios using statistical and financial models on real market data.

0 0
R
Gradient_and_CoordinateDescent_Methods

Comparative analysis of gradient-based optimization methods (fixed step, Armijo rule, block coordinate descent, coordinate minimization) on synthetic and real data.

0 0
Jupyter Notebook
GasPrices_TimeSeries

Time series analysis of residential natural gas price dynamics in Italy and Turkey using explanatory econometric approach.

0 0
R
Garch_Analysis

Analysis and forecasting of financial market volatility using GARCH-family models. The study compares different model specifications (GARCH, EGARCH, APARCH) and innovation distributions (normal, Student-t), with performance validated through Diebold-Mariano tests and rolling correlations.

0 0
R
FrankWolfe_PortfolioOptimization

Comparative analysis of the performances of the Projected Gradient Method and the Frank-Wolfe Algorithm (and variants) on the Markowitz Portfolio Optimization Problem.

0 0
Jupyter Notebook
Lab_Statistica

Anomaly detection on Italian public procurement data from ANAC using a custom heterogeneous Graph Neural Network autoencoder. It includes data preprocessing, heterogeneous graph construction, anomaly detection on heterogneous graph, interpretability tools, and interactive Dash dashboards for graph exploration.

0 0
Python

Open Source Impact

Contributions to external projects

0 merged PRs

No external contributions found.