Professional Experience
I bring a broad range of practical and research-based experience in data management. On the practical side, I’ve been developing data readiness workflows for the past four years using Python with libraries such as Pandas and Polars. I have experience working with varied data types including geospatial data, genomic data, and microscopy imagery. I’m comfortable working with local and cloud files, managing data in DBMS systems like Postgres, or working with data in distributed filesystems on HPC platforms like Frontier.
My hands-on experience is grounded in a strong research background, including work on Java Virtual Machine extensions to support object-oriented databases and performance optimization of MPI-IO for the Lustre file system. I also have extensive research experience with ADIOS and familiarity with other self-describing file formats such as NetCDF. I have also worked on domain-specific code generation techniques to support both I/O performance studies and data-readiness workflows. Most recently my research interests have been focused on data readiness and FAIR workflows.
Together, these experiences give me a strong foundation in both the practical and theoretical aspects of data management across diverse environments.