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Etienne Gnimpieba, Ph.D.



  • Assistant Professor, Computer Science/Bioinformatics
  • University of South Dakota


  • Ph.D., Biotechnology/Bioinformatics, University of Technology of Compiegne, France
  • MS, Computer Engineering and Mathematics for Integrative Biology, University of Evry Val d'Esonne, France
  • MS, Artificial Intelligence and Human Machine Interface, University of Yaounde, Cameroon
  • BA, Mathematics/Computer Science, University of Dschang, Cameroon

Current Research: My research uses Systems Biology modeling and Data Mining approaches to aid in providing a better understanding of gene-disease interaction. Each of these two domains are limited in computing resources for a common research computing environment. The emergence of High Performance computing (HPC) infrastructures allow better handling of that dilemma. The long term goal is to develop a new decision support knowledge base for gene-disease study. This uses integrative approaches (algorithm, process, tools) for life science multi-scale systems integration and analysis using a combination of big data mining, machine learning and Systems Biology approaches. This includes 1) a novel reusable multi-scale data mining model for knowledge discovery based on adaptive machine learning and artificial intelligence; 2) new algorithms for data transformation and integration to handle the heterogeneity among the integrated data sources and Systems Biology data (images, text, relational data, etc.); 3) a flexible implementation easy to use tools on a HPC and big data infrastructure using R Bioconductor, Matlab, Java J2EE; 4) several Biological use case studies development.