Francis Bach
About Francis Bach
Francis Bach is a Research Faculty member at Ecole Normale Superieure and INRIA, where he has worked since 2007. He leads the SIERRA project-team and specializes in machine learning, statistics, and optimization, with a research focus on convex optimization and related fields.
Work at INRIA
Francis Bach has been a Research Faculty at INRIA since 2007. His role involves conducting advanced research in the field of computer science, with a focus on machine learning and optimization. Over the years, he has contributed to various projects and initiatives that align with INRIA's mission to advance knowledge and technology in computer science.
Work at Ecole Normale Superieure
Since 2007, Francis Bach has also served as a Research Faculty at Ecole Normale Superieure. He leads the SIERRA project-team within the Computer Science department, where he oversees research activities and collaborates with other faculty members and students on various projects related to his areas of expertise.
Education and Expertise
Francis Bach's educational background includes a PhD in Computer Science from the University of California, Berkeley, which he completed from 2000 to 2005. He also studied at Ecole nationale supérieure des Mines de Paris, earning a Master of Public Administration (MPA) from 1997 to 2000, and attended École Polytechnique from 1994 to 1997. His research focuses on convex optimization, graphical models, sparse methods, kernel-based learning, vision, and signal processing.
Previous Experience at Ecole des Mines de Paris
Prior to his current roles, Francis Bach worked as a Research Faculty at Ecole des Mines de Paris from 2005 to 2007. During his tenure, he engaged in research and academic activities that contributed to the institution's reputation in the field of engineering and applied sciences.
Research Focus and Specialization
Francis Bach specializes in machine learning, statistics, and optimization. His research encompasses various methodologies and applications, particularly in convex optimization and graphical models. He has developed expertise in sparse methods and kernel-based learning, contributing to advancements in vision and signal processing.