Michele Alessandro Bucci
About Michele Alessandro Bucci
Michele Alessandro Bucci is a scientific researcher with expertise in machine learning and fluid mechanics. He has worked at several prestigious institutions, including Inria and Arts et Métiers ParisTech, and specializes in dynamical system identification and manifold learning.
Current Position at Inria
Michele Alessandro Bucci has been working as a Scientific Researcher at Inria since 2019. His role involves engaging in advanced research projects that focus on the application of machine learning techniques to improve numerical simulations in fluid mechanics. Bucci's work contributes to the development of innovative methodologies that enhance the understanding and performance of dynamical systems.
Previous Experience at Arts et Métiers ParisTech - ENSAM
Before joining Inria, Michele Bucci completed a Ph.D. at Arts et Métiers ParisTech - ENSAM from 2014 to 2018. During this four-year period in the Paris Area, he focused on aerodynamics and aeroacoustics, laying a strong foundation for his future research endeavors. His academic experience at ENSAM significantly shaped his expertise in fluid dynamics.
Postdoctoral Research at LIMSI
Following his Ph.D., Michele Bucci worked as a Postdoctoral Researcher at LIMSI from 2018 to 2019. This one-year position in Orsay, France, allowed him to further develop his research skills and contribute to projects related to machine learning and dynamical systems, reinforcing his commitment to advancing the field.
Education and Specialization
Michele Bucci holds a Master's degree in Aerodynamics and Aeroacoustics from Arts et Métiers ParisTech, which he achieved in 2014. He also completed a Doctor of Philosophy - PhD in Mechanical Engineering - Fluid Dynamics at the same institution from 2014 to 2017. His educational background includes a Second level degree in Mechanical Engineering from Politecnico di Bari, completed in 2014, and a First level degree in the same field from 2009 to 2012. Bucci specializes in adversarial neural networks and optimal transport for manifold learning, focusing on their application in fluid mechanics.
Research Focus and Contributions
Michele Alessandro Bucci's research primarily revolves around dynamical system identification through machine learning. He aims to enhance the performance of numerical simulations in fluid mechanics by applying advanced machine learning techniques. His work is significant in the context of improving the accuracy and efficiency of simulations, which are critical in various engineering applications.