I’m an expert in high-performance computing (HPC), advanced data processing, applied computational math and statistics. I help scientists, engineers, and researchers develop workflows and scientific software in a distributed context on Top500 supercomputers and in the cloud. I teach computing and code (mostly Python) and am an advocate of Better Scientific Software. I’m interested in all things computing and data. I’m learning Rust and enjoy coffee too much.
My background is in astrophysics. I have a bachelors in physics from Purdue University. I went on to graduate school at the University of Louisville and studied astrophysics with a masters dissertation on the local ISM. I continued with a Ph.D. at the University of Notre Dame for another two years doing research in large scale survey astronomy before leaving early to pursue a career in data science.
I helped build out the data analytics team at the New York Power Authority, the largest public power utility in the country. I worked on projects in optimization and automation for both business and operations use-cases (e.g., optimizing the flow of water at the Niagara Falls power project).
I returned to Purdue University in the Spring of 2018 on the Research Computing team. My background in research science, with my skills in technical computing and software, along with my experience in industry have made me uniquely qualified to help others solve problems and build solutions at a top research university.
High-performance computing (HPC) is an area of computing occupied by scientists, researchers, and industry professionals in all manner of subject area. National laboratories, major universities, and now public cloud providers, build and operate large scale interconnected compute and storage systems comprised of many discrete machines working together to solve problems. For me, this means building better scientific software and more exotic workflows that can be distributed across multiple machines at once and is distinct from common software engineering.
Data Science is notoriously difficult to define. In the academic context, it is the common thread of data and computational facility shared among 21st century scientists in every domain. More generally, and as it lives in an industrial context, data science is the intersection of applied computational math and statistics, basic software engineering, and business acumen. More recently, data science has become synonymous with machine learning (ML) and now deep learning.
My university training and research background is in astrophysics and large scale survey astronomy. My research interests are quite varied, including the history and evolution of galaxies as understood through both astronomical surveys involving some of the largest datasets ever collected (LSST) and massive particle simulations. I’m also interested in the search for life using exoplanetary spectroscopy.