Research areas of the group focus on the modeling of complex systems, algorithm design and their analysis in computational data science with an emphasis on big-data. Following hardware/software co-design principle, topics of interest include the design of parallel and scalable algorithms for solving large-scale numerical systems and graph analytics as well as scalable and applied machine learning with a focus on high-performance computing, performance modeling, simulation and optimization on massively parallel architectures, accelerators (e.g, GPUs) and forthcoming exascale computing systems. 

Research areas of interest are:

  • Graph Analytics and Graph Learning Algorithms
  • High Performance Computing in Computational Data Science
  • Numerical Linear Algebra
  • Numerical methods for Machine Learning
  • Parallel Algorithm Design
  • Parallel and GPU Computing
  • Performance Modeling
  • Scalable Machine Learning
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