Through in-depth interviews, each "Community Corner" feature explores:
What are your current research interests?
I am a data steward at the Minnesota Supercomputing Institute where I assist UMN researchers with their data management needs, particularly as it relates to data access and controls. In this role, I help to ensure that protected data requirements are established. Recently, I have been helping with the development of the onboarding protocol and workflows for Blackwell, MSI’s newest HIPAA compliant HPC cluster. Blackwell would allow projects such as the Genomic Data Commons, a repository of genomic data, to store, manage and analyze their private highly restricted data.
How do you define Data Science?
I define Data Science as an interdisciplinary field that applies scientific methods and techniques to obtain insights from data, that ultimately supports informed decision-making. It draws on various domains such as computer science, mathematics, statistics, and more. For me, Data Science is fundamentally about using a rigorous, scientific approach to extract, analyze and interpret data.
Can you share an interesting or surprising result you’ve found in your data?
One of the projects I’m involved in is with the Center for Mesoscale Connectomics, a collaborative consortium of national and international universities. The project aims to estimate fronto-parietal connectivity at the mesoscale in both human and macaque brains. What’s particularly exciting is the team’s use of multiple high-resolution imaging modalities—including diffusion MRI (dMRI), tract-tracing, and polarization-sensitive optical coherence tomography (PS-OCT)—to visualize neural networks at a very fine scale. These techniques are producing incredibly detailed images that are helping us uncover previously unseen patterns of connectivity, offering new insights into how different regions of the brain communicate.
Are there any interesting new tools or libraries you or your students have been using?
Yes! I’ve been working on a new initiative called the UMN Genomic Data Commons (GDC), led by Dr. Saonli Basu. This tool is designed to significantly enhance genomic research at the University of Minnesota by serving as a centralized hub for genomic data sharing, management, and analysis.The GDC provides UMN researchers with access to harmonized genomic datasets through a user-friendly web portal. This interface allows users to view basic summary information and submit data analysis requests as well as provides an opportunity for PIs to share their data with the GDC. In addition, the GDC employs a suite of analytic pipelines to perform various types of genomic analyses using its integrated datasets.
What are you most excited about in the field of data science in the next 5 years?
I'm most excited to see a growing emphasis on data management, especially as the scale and complexity of research data continues to increase. As datasets become larger and more intricate, effective management practices will be essential for ensuring data quality, accessibility, and reproducibility. I'm also eager to see how artificial intelligence will continue to evolve and be applied in this space. I'm particularly interested in the development of best practices and regulations that ensure AI is used ethically and responsibly, especially research environments.
