Software engineer and educator. Spent a decade in the public research space at the intersection of high-performance computing (HPC) and big data analytics. I enjoy thinking about computational linear algebra, GPGPU, HPC and cloud technologies, and open source software development.

I teach a course on the computer science of data science. Sometimes I post my thoughts to the fml blog. I am one of the main developers of the pbdR project for integrating the R language in HPC environments. I also built and maintain the HPCRAN, which is an R package repository for HPC packages.

The best way to contact me is wrathematics at gmail.

• Matrix Computations in Constrained Memory Environments
• Matrix Factorizations for Data Analysis
• Comparing Symmetric Eigenvalue Performance
• Floating Point Arithmetic Is Hilarious

• Schmidt, D., 2020, November. A Survey of Singular Value Decomposition Methods for Distributed Tall/Skinny Data. In 2020 IEEE/ACM 11th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA) (pp. 27-34). IEEE.
• Hasan, S.S., Schmidt, D., Kannan, R. and Imam, N., 2019, December. A Scalable Graph Analytics Framework for Programming with Big Data in R (pbdR). In 2019 IEEE International Conference on Big Data (Big Data) (pp. 4783-4792). IEEE.
• Vazhkudai, S.S., de Supinski, B.R., Bland, A.S., Geist, A., Sexton, J., Kahle, J., Zimmer, C.J., Atchley, S., Oral, S., Maxwell, D.E. and Larrea, V.G.V., 2018, November. The design, deployment, and evaluation of the CORAL pre-exascale systems. In SC18: International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 661-672). IEEE.
• Schmidt, D., Chen, W.C., Matheson, M.A. and Ostrouchov, G., 2017. Programming with BIG data in R: Scaling analytics from one to thousands of nodes. Big Data Research, 8, pp.1-11.