I am working in High Performance Computing, Data Science, Parallel and Distributed Simulation, Embedded Systems, and Point-of-Care medical devices. I have been working with Random and Approximate Methods to improve the performance of Topological Data Aalysis (TDA), specifically persistent homology. These studies have focused on partitioning and parallelism to decompose the problem into a collection of subparts that can be processed in parallel. Because the TDA algorithms have exponentional complexity in both time and space, decomposing and partitioning the data into regional subspaces has dramatic impact on the overall performance. In Big Data Clustering, I have been working to combine random projection hashing with locality sensitive hashing to implement high-performance, distributed privacy preserving data mining. The projection and hashing approach permits us to perform clustering on distributed data sets by exchanging only hash keys between the distributed nodes. The hash keys are produced by destructive transforms so that the original data in the distributed network remains fully protected. We are promoting these techniques to enable clustering and nearest neighbor search across HIPAA protected medical databases. In addition, I have been working extensively for many years to advance the field of Parallel and Distributed Simulation (PDES) using the Time Warp mechanism. Most recently we have been studying the design of solutions for the pending event set problem for high performance simulation on multi-core and many-core platforms. I have initiated studies to extract profile data from discrete event simulation models to obtain quantitative data that I plan to use to focus my algorithm development for parallel simulation. Finally, I am working with the local BioSensors group and College of Medicine to develop point-of-care medical devices to assist patient diagnosis, treatment, and monitoring.