Heike Jagode is a Research Assistant Professor in the Innovative Computing Laboratory (ICL), and an Adjunct Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS), in the Tickle College of Engineering, at the University of Tennessee - Knoxville (UTK). She specializes in High Performance Computing (HPC) and the efficient use of advanced computer architectures; focusing primarily on developing methods and tools for performance analysis, tuning, and efficient energy use of parallel scientific applications. Heike's Ph.D. research focused on a multi-disciplinary effort to develop Dataflow Programming Paradigms for Computational Chemistry Methods to make these methods compatible with next-generation task scheduling systems like StarPU or PaRSEC. In her research, she converted the state-of-the-art NWChem Coupled Cluster methods into different dataflow versions and demonstrated the benefits of dataflow-based task execution over coarse grain parallelism in terms of scalability, resource utilization, and programmability. Education: Doctor of Philosophy (Ph.D.) in Computer Science, University of Tennessee, Knoxville, USA Master of Science (M.Sc.) in High Performance Computing, University of Edinburgh, EPCC, Scotland, UK Master of Science (M.Sc.) in Applied Techno-Mathematics, University of Applied Sciences Mittweida, Germany Bachelor of Science (B.Sc.) in Applied Mathematics, University of Applied Sciences Mittweida, Germany  Heike Jagode is a Research Assistant Professor in the Innovative Computing Laboratory (ICL), and an Adjunct Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS), in the Tickle College of Engineering, at the University of Tennessee - Knoxville (UTK). She specializes in High Performance Computing (HPC) and the efficient use of advanced computer architectures; focusing primarily on developing methods and tools for performance analysis, tuning, and efficient energy use of parallel scientific applications. Heike's Ph.D. research focused on a multi-disciplinary effort to develop Dataflow Programming Paradigms for Computational Chemistry Methods to make these methods compatible with next-generation task scheduling systems like StarPU or PaRSEC. In her research, she converted the state-of-the-art NWChem Coupled Cluster methods into different dataflow versions and demonstrated the benefits of dataflow-based task execution over coarse grain parallelism in terms of scalability, resource utilization, and programmability. Education: Doctor of Philosophy (Ph.D.) in Computer Science, University of Tennessee, Knoxville, USA Master of Science (M.Sc.) in High Performance Computing, University of Edinburgh, EPCC, Scotland, UK Master of Science (M.Sc.) in Applied Techno-Mathematics, University of Applied Sciences Mittweida, Germany Bachelor of Science (B.Sc.) in Applied Mathematics, University of Applied Sciences Mittweida, Germany