Marco Forgione
Senior Researcher at IDSIA USI-SUPSI
Lugano, Ticino, Switzerland
Summary
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Senior🎓
Top SchoolMarco Forgione is a Senior Researcher specializing in automatic control and machine learning at IDSIA, with eight years of experience bridging academic rigor and industrial R&D. He holds a PhD in Systems and Control from TU Delft and has applied data-driven control and observer techniques to pharmaceutical batch processes, RF electronics, and consumer appliance projects for Whirlpool. Marco contributes to open-source control tooling—improving Python Control’s frequency plotting and tests—demonstrating a blend of numerical analysis, software engineering, and practical control design. Based in Lugano, he combines strong academic publication-level research with hands-on embedded and algorithm development, from converter modeling to doneness estimation via humidity sensing.
8 years of coding experience
10 years of employment as a software developer
Master of Science (MSc), Computer Engineering, 110/110 cum laude, Master of Science (MSc), Computer Engineering, 110/110 cum laude at Università degli Studi di PaviaDoctor of Philosophy (Ph.D.), Systems and Control Engineering, Doctor of Philosophy (Ph.D.), Systems and Control Engineering at Delft University of Technology
English, Italian, French, Dutch
Stackoverflow
11reputation
94reached
0answers
1question
Github Skills (8)
control-systems10
python10
numpy10
matplotlib9
jupyter-notebook9
scipy8
testing7
pyspark6
Programming languages (5)
Github contributions (5)
python-control/python-control
Mar 2021 - Apr 2021
The Python Control Systems Library is a Python module that implements basic operations for analysis and design of feedback control systems.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:41 reviews, 10 commits, 1 PR in 21 days
Contributions summary:Marco's primary contribution involves the addition and modification of code related to the `singular_values_plot` function within the `control/freqplot.py` file. Their work includes adding the function, writing tests, and creating an example Jupyter notebook. The user also addressed documentation and default settings for the new function, while also fixing issues and reshaping the frequency response output.
pythonbasic-operationslibrary-systemspython-moduleelectrical-engineering
forgi86/RNN-adaptation
May 2021 - Feb 2022
Code accompanying the paper "On the adaptation of recurrent neural networks for system identification"
Contributions:58 commits, 1 PR, 7 pushes in 9 months
deep-learningdynamical-systemstransfer-learningrecurrent-neural-networksneural-networks
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