Dr Bubacarr Bah
Dr Bubacarr Bah is the German Research Chair of Mathematics with specialization in Data Science at AIMS South Africa
He received a BSc degree (with sum cum laude) in mathematics and physics from the University of The Gambia, where he worked as a Graduate Assistant; the MSc degree in mathematical modeling and scientific computing from the University of Oxford, Wolfson College; and the PhD in applied and computational mathematics at the University of Edinburgh, Scotland, under the supervision of Professor Jared Tanner. His PhD research was on compressed sensing, in particular on random matrix theory questions arising from compressed sensing and signal processing in general. For some of his PhD work he has received the 2010 SIAM Best Student Paper Prize for his paper Improved bounds on restricted isometry constants for Gaussian matrices published in the SIAM Journal of Matrix Analysis.
His first post-doc was at the Laboratory for Information and Inference Systems (LIONS) in Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland, working on sampling strategies and designing algorithms for compressed sensing and sparse approximation. He also worked on machine learning, in particular learning linear embedding of high-dimensional data. His second post-doc was at the Department of Mathematics and the Institute of Computational and Engineering Sciences (ICES) at the University of Texas at Austin. While here, Dr Bah’s research continued in the areas of signal processing, machine learning and randomized numerical linear algebra. He was at the same time a lecturer at the Department of Mathematics teaching undergraduate Differential Equations and Applied Linear Algebra courses.
More precisely, Bubacarr is an applied and computational mathematician and his research interest is at the intersection of Applied Mathematics and Computer Science. He worked in the general area of signal and data processing and machine learning. In particular, his work was on the design and analysis of sampling strategies and algorithms for compressed sensing and learning linear embeddings of high-dimensional data sets. Some of his works relates to random matrix theory and numerical linear algebra; while some relates to scientific computing and the use of high performance computing (HPC) resources.
His research will now focus on data science, as the new German Research Chair of Mathematics with specialization in Data Science. He plans to develop a vibrant research group on data science: conducting research in foundational mathematical and computational methods and techniques (modeling, sampling, algorithms, etc) for data science problems from biomedical sciences, engineering, social sciences, etc. In particular, there would be a significant emphasis on associated the scientific computing issues, which would include the utilization of HPC resources to address scalability challenges arising from the Big Data nature of these problems. His Chair on data science cannot come at a better time for Africa. There are a wide range of data science problems in Africa from finance, biomedical sciences, manufacturing, power systems, transportation, and so on, that are in need of data science research and technologies. A lot of developed economies like the USA and Germany has integrated data science research and technologies in almost every sector of their industries to improve efficiency and effectiveness of systems like engineering, business, education, government services, etc. The research output from this Chair is hope to have a significant impact in industries in South Africa, and Africa in general and Germany via the collaboration with German researchers, which is a key component of this Chair. Furthermore, the Chair gives Bubcarr the opportunity to engage in other academic activities that will contribute to the development of mathematics research and education in Africa.
For more information please visit https://sites.google.com/aims.ac.za/bubacarr