Pietro Barbiero

Machine learning engineer - Computational scientist


My job consists in combining mathematics and computer science to analyze and study biological systems. My research activity focuses on machine learning, neural networks, and evolutionary algorithms applied to genomic data analysis.


curriculum vitae
Pietro Barbiero

Research

Stats

H-index 3 and 47 citations @scholar
Since 2017: 3 journals, 12 conference papers.

FOSS projects

COVID19 ICU - Forecasting Ultra-early Intensive Care Strain from COVID-19 in England @GitHub
DBGen - General purpose database for genetics PyPI
LazyGrid - Python package for scikit-learn pipelines’ memoisation and comparison PyPI
EvoFS - Multi-objective evolutionary algorithm for feature selection PyPI
EvoCore - Evolutionary-based algorithm to compress large data sets into few high-informative samples @GitHub
GH-EXIN - Novel neural networks for hierarchical and non-stationary clustering @GitHub

Education


2017 - 2019

M.Sc. Engineering Mathematics - Politecnico di Torino, Italy

2013 - 2017

B.Sc. Computer Engineering - Politecnico di Torino, Italy


Accomplishments


2019

Top Team Award at the European Innovation Academy 2019

Conference grant, DECON - International Conference on Decision Economics

2018

Conference grant, WIRN - 28th Italian Workshop on Neural Networks

2017

Conference grant, WIRN - 27th Italian Workshop on Neural Networks

Additive Manufacturing and Arduino Microcontroller, DIPFablab International Winter School, Verres, Italy

Contacts

@Scholar @GitHub @ResearchGate @Linkedin @Wikipedia @Societa' Digitale


Creative Commons Licence
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.