Clearbox AI
Luca Gilli has a diverse work experience, starting with their current role as Co-Founder at Clearbox AI Solutions since July 2019. Luca is on a mission to improve businesses and society through harnessing AI technologies, specifically working on a data-centric MLOps solution using generative models to generate synthetic data and insights for AI projects.
Prior to this, Luca worked as the Co-founder and Chief Engineer at Stocastica AI Consulting from January 2018 to March 2019.
Luca also served as an R&D Consultant at NRG from February 2013 to December 2017. In this role, they developed an optimization tool for fuel management in Boiling Water Reactors and conducted uncertainty quantification and sensitivity analysis of large scale multi-physics simulations.
Before that, Luca worked as a Junior Researcher at TU Delft from November 2008 to November 2012. During this time, they focused on research related to sensitivity analysis and uncertainty quantification techniques for multi-physics problems. Their work led to a successful PhD dissertation and a doctorate, where they developed a new uncertainty quantification method to reconstruct surrogate models for complex problems more efficiently.
Luca Gilli's education history begins with a Bachelor's degree in Energy Engineering from Politecnico di Torino, which they obtained from 2002 to 2006. Luca then pursued a Master's degree in Nuclear & Energy Engineering from the same institution, completing it from 2006 to 2008.
Following their Master's degree, Luca Gilli enrolled in Delft University of Technology, where they pursued a Doctorate in Applied Sciences. Luca successfully completed their PhD from 2008 to 2013.
In addition to their academic qualifications, Luca Gilli obtained a certification in Radiological health physics: level III expert from the Dutch national center for radiation protection in May 2009.
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Clearbox AI
Clearbox AI offers Synthetic Data generation solutions that automatically create new high-quality data points and reports on their quality and privacy profile to comply with regulations, like GDPR, CCPA,.... It is an effective privacy-enhancing solution to access and share sensitive data inside and outside organizations. Additionally, SyntheticData can be used to improve the size and quality of datasets, fix structural issues in the information, experiment with data and AI in safe environments, and even validate AI models by generating various scenarios.