I am a Principal Member of Technical Staff at Sandia National Laboratories - California. I primarily work on research and development for HPC Software Technology. This HPC Software Technology enables Scientific Software relevant to the U.S. Department of Energy to run efficiently on supercomputing platforms having nodes with heterogenous devices, including GPUs. I am also interested in AI/ML for automated performance tuning and automated testing in HPC (including modern generative AI techniques), AI/ML-guided Scientific Software and hybrid supercomputer-cloud platforms. Additionally, my work can be used to optimize AI/ML workloads on modern data center platforms having GPUs. Key topics in my work are Kokkos, C++ Parallel STL, C++ Executors, OpenMP, MPI, loop scheduling and loop transformations, LLVM, adaptive runtime systems, tools for profiling and debugging, and performance modeling. I completed my PhD in Computer Science in May 2015 from the University of Illinois at Urbana-Champaign. During this time, I also worked with and held positions with the U.S. Department of Energy laboratories, particularly at Lawrence Livermore National Laboratory and Argonne National Laboratory. Since earning my doctorate, I continued research on combining work on loop scheduling and inter-node load balancing, making loop scheduling and load balancing work synergistically together to improve performance of scientific and engineering simulations run on supercomputers. A highlight of my work is the development of multi-GPU programming capabilities in LLVM's OpenMP, including mechanisms for maintaining data locality when scheduling computation and data to multiple GPUs. This is increasingly important for scaling HPC applications involving AI/ML, bioinformatics, drug discovery on multi-GPU platforms such as NVIDIA's DGX.