Francesco D'angelo
About Francesco D'angelo
Francesco D'Angelo is a Research Intern at Motional in Singapore, where he has worked since 2021. He has a background in physics and neural systems, with previous research experience at ETH Zürich.
Work at Motional
Francesco D'angelo has been serving as a Research Intern at Motional since 2021. His role is based in Singapore, where he has been involved in synthesizing critical driving scenarios using generative models. This position allows him to apply his academic knowledge in a practical setting, contributing to the development of autonomous vehicle technologies.
Education and Expertise
Francesco D'angelo holds a Master of Science in Neural Systems and Computation from Eidgenössische Technische Hochschule Zürich, completed between 2018 and 2020. Prior to this, he studied Fisica at Università degli Studi di Padova, where he earned a Laurea L from 2014 to 2018. His educational background provides a solid foundation in both theoretical and applied aspects of neural networks and computational systems.
Background
Francesco D'angelo's academic journey began at Liceo Marconi, where he studied from 2009 to 2014. He then progressed to Università degli Studi di Padova and later to ETH Zürich, where he engaged in various research projects. His international experience includes working in Zurich, Switzerland, and Singapore, reflecting his adaptability and global perspective in research.
Research Experience at ETH Zürich
At ETH Zürich, Francesco D'angelo held multiple research roles, including a Research Assistant and positions focused on specific projects such as Bayesian Neural Networks and Generative Neural Networks. His work included a Master Thesis and participation in projects that explored advanced computational models. This experience has equipped him with valuable skills in machine learning and neural computation.
Research Projects and Contributions
Francesco D'angelo contributed to significant research projects at ETH Zürich, including the development of Bayesian Neural Networks with Normalizing Flow and the Learning of the Ising Model with Generative Neural Networks. These projects highlight his involvement in cutting-edge research and his ability to work on complex problems in the field of neural computation.