At the heart of healthcare innovation lies the strategic integration of Privacy Enhancing Technologies (PETs) into health insurance models. The presentation will explore the transformative potential of PETs in crafting new, dynamic insurance models that leverage qualitative, privacy-sensitive patient data. By focusing on the alignment of PETs with VBHC principles, we will uncover the pathways through which these technologies facilitate personalized, outcome-based patient care while ensuring data privacy and security. Attendees will gain insights into implementation, challenges overcome, and the possible impacts on both the quality of patient care and the sustainability of health insurance operations.

Los Alamos National Laboratory's (LANL) has a diverse set of High Performance Computing codes. Analysis of many of these codes indicate they are heavily memory bound with sparse memory accesses. High Bandwidth Memory (HBM) has proven a significant advancement in improving the performance of these codes but the roadmap for major (step function) improvements in memory technologies is unclear. Addressing this challenge will require a renewed focus on high performance memory and processor technologies that take a more aggressive and holistic view of advancements in ISA, microarchitecture, and memory controller technologies. Beyond scientific simulations, advancements in performance of sparse memory accesses will benefit graph analysis, DLRM inference, and database workloads.