In the pursuit of cultivating technologies that can have a positive impact on science and society, Bethel is an innovator, a visionary, an entrepreneur, a mentor, a data and computer scientist, a technologist, and a leader. His technical background in computer and data science spans several fields, which lie at the nexus of computing and data: high performance computing, scientific/information visualization, graphics, scientific data/image analysis, and machine learning. Over the course of his 30+ years in the field, he has been instrumental in advancing the field of high performance scientific visualization through the R&D of new methods and software tools, and applying them with success to many fields of science. He is passionate about enabling scientific knowledge discovery through the convergence of data and computing, a pursuit that requires ongoing evolution and adaptation to a changing technological and scientific landscape.
At Berkeley Lab, he built a research group from three persons and no research funding into a vibrant program consisting of over 10 full-time staff, multiple student assistants and post-doctoral researchers, and multiple joint faculty appointments. He has mentored/supervised over 60 scientists, engineers, and students. The team produces an average of 30-35 publications per year, and has a diverse funding portfolio in the range $5-6M/yr, and has earned an international reputation for high quality work in scientific visualization, data analysis, computer vision, machine learning, and data science. He conceived and implemented a large, multi-institutional project that was DOE's largest ever open-science, scientific visualization program, the SciDAC Visualization and Analytics Center for Enabling Technology (VACET). That program successfully made production-quality, petascale-capable visualization a reality for the DOE computational science community. He has worked closely with program management in the Energy Department's Office of Advanced Scientific Computing Research, where he architected, with input from the community, the visualization and analytics research agenda for the Exascale Computing Plan (now the Exascale Computing Project), and is presently an architect for a data roadmap that focuses on DOE experimental and observational science programs.