I am currently a software engineer at NVIDIA, where I work on the RAPIDS team to develop GPU-accelerated tools for data science and machine learning. I primarily work on the cuDF library for high-performance data processing, but I have also contributed extensively to other parts of the RAPIDS stack, including cuML and cuGraph. I am an experienced Python, C, and C++ and CUDA developer, with additional experience working with a range of other programming languages, including Java and Rust. I also have extensive experience with build systems in various programming languages, with a special focus on CMake and various build backends for Python.

Prior to joining the RAPIDS team I completed my Ph.D. in Chemical Engineering and Scientific Computing in Sharon Glotzer's research group at the University of Michigan, where I worked on self-assembly problems in soft matter physics. The primary focus of my research was expanding the range of applicability of the highly coarse-grained models that have been used to successfully predict the behavior of colloidal systems over the past decade. My research has been featured in numerous journals, including Nature Chemistry, Computer Physics Communications, and the Journal of Molecular Modeling. As part of my research, I also became extensively involved with the development of open-source scientific software, which you can read more about on my software page.