Research
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