Laurent Morin
About Laurent Morin
Laurent Morin is a Senior Research Engineer at Inria, specializing in big-data framework extensions for Hadoop and Spark. He has a background in research roles at DESY and CAPS entreprise, and he is involved in the Chistera DIVIDEND Project funded by the French National Research Agency.
Work at Inria
Laurent Morin has been employed at Inria as a Senior Research Engineer since 2015. His work is based in the Rennes Area of France. At Inria, he focuses on advancing research in big data frameworks, specifically developing extensions that utilize custom accelerators for Hadoop and Spark. This research aims to enhance energy efficiency in data centers, addressing a critical concern in the management of large-scale data processing.
Previous Experience at DESY
Before joining Inria, Laurent Morin worked as a Research Associate at DESY from 2005 to 2007. During his two-year tenure, he contributed to various research initiatives, gaining valuable experience in a collaborative research environment. This position laid the groundwork for his future roles in research and project management.
Experience at CAPS entreprise
Laurent Morin served as a Research Fellow and Collaborative Research Project Manager at CAPS entreprise from 2007 to 2014. Over the course of seven years, he managed collaborative research projects, which involved coordinating efforts among various stakeholders. This role enhanced his expertise in project management and collaborative research methodologies.
Involvement in Chistera DIVIDEND Project
Laurent Morin was involved in the Chistera DIVIDEND Project, which received funding from the French National Research Agency (ANR). This project focused on innovative approaches in research and development, contributing to advancements in the field of data processing and energy efficiency.
Research Focus on Big Data Frameworks
Laurent Morin specializes in the development of big data framework extensions, particularly for Hadoop and Spark. His research emphasizes the integration of custom accelerators to optimize performance and reduce energy consumption in data centers. This focus aligns with the growing demand for sustainable practices in data management and processing.