Academic versus industrial research in Machine Learning
We face an increased trend of interest and competitiveness in machine learning, for both academic and industrial research. However, the aims are completely different: in industry, there is a constant pressure to augment the existing functionalities through smart services, while the academic research mainly targets development and study of specific building blocks. Practical examples will emphasize the gap between the two cultures. The talk touches the challenges of delivering the results in both academic and industrial environments. The issues exposed might provide potential hints for the academic curricula, aiming to speed up graduates’ involvement in academic and/or industrial R&D, with a special emphasis on machine learning.
About the lecturer
Lucian M. Sasu is associate professor at the Department of Mathematics and Informatics, Faculty of Mathematics and Informatics, Transilvania University of Brasov, Romania; he is teaching and doing academic research in this university since 2000, and he holds a Ph.D. degree in Computer Science since 2006. He also joined Siemens Corporate Technology Romania in 2012 in the R&D area, being involved in both FP7 and in internal industrial projects. His research focuses on machine learning – with an emphasis on incremental learning, pattern recognition and bio-informatics, and was published in peer-reviewed journals and conferences. He is a member of IEEE and Romanian Mathematical Society.