Giuliano Cornacchia
Mentee at Istituto di scienza e tecnologie dell'informazione "A. Faedo" (Cnr-Isti)
Pisa, Tuscany, Italy
Summary
👤
Senior🎓
Top SchoolGiuliano Cornacchia is a data scientist and PhD in Computer Science based in Pisa with eight years of experience applying routing algorithms, traffic simulation, and mobility analysis to sustainability and urban planning. He developed emissions-aware routing models and the TraffiCO2 SUMO framework at CNR-ISTI to quantify environmental impacts of mobility policies and designed algorithms to reduce CO2 from navigation systems. At Booking.com he built scalable demand-forecasting pipelines, and at the World Bank he now engineers country-scale pipelines to process billions of GPS points for high-frequency urban indicators. An active open-source contributor, he implemented generative mobility models (GeoSim, STS-EPR) and reproducible diary generators in the widely used scikit-mobility library. His work blends rigorous academic research—including a visiting stint at UC Berkeley on route diversity—with production-ready systems that bridge policy, industry, and research. Notably, he pairs deep algorithmic expertise with hands-on data engineering to turn large, noisy mobility datasets into actionable urban insights.
8 years of coding experience
2 years of employment as a software developer
Laurea Magistrale LM in scienze e tecnologie informatiche, Informatica; Data & Knowledge: science and technologies, Laurea Magistrale LM in scienze e tecnologie informatiche, Informatica; Data & Knowledge: science and technologies at Università di Pisa
Github Skills (7)
pandas10
machine-learning10
python10
data-science10
numpy10
testing9
geopandas9
Programming languages (2)
Github contributions (5)
scikit-mobility/scikit-mobility
Jan 2021 - Jan 2023
scikit-mobility: mobility analysis in Python
Role in this project:
Data Scientist
Data ScientistContributions:35 commits, 7 PRs, 15 pushes in 2 years
Contributions summary:Giuliano implemented generative models, specifically GeoSim and STS-EPR, for human mobility analysis. These models incorporate spatial, temporal, and social dimensions. The commits include tests to verify the correctness of the models and their behavior with different inputs. Additionally, the user worked on adding a random state parameter to the Markov Diary Generator to enable reproducibility.
complex-systemsdata-analysismobility-analysisstatisticspython
kdd-lab/2019_Cornacchia
Apr 2020 - Feb 2021
Master Thesis of Giuliano Cornacchia
Contributions:23 commits, 6 PRs, 19 pushes in 9 months
master-thesismasterthesis
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