AIMRC Data Science Core: Storage and Computational Servers and Related Workflow

The Arkansas Integrative Metabolic Research Center (AIMRC) will host Dr. Prateek Verma, manager of the AIMRC Data Science Core, at 12:55 p.m. on Wednesday, April 1, in CHEM 0144. Dr. Verma will highlight the unique complex-analysis and machine-learning analytical capabilities of the Data Science Core and how core staff can assist faculty to advance research.

Abstract: The Data Science Core of the Arkansas Integrative Metabolic Research Center (AIMRC) provides centralized computational infrastructure and expertise to support data-driven biomedical research across the U of A. This seminar will introduce the services and capabilities of the core and demonstrate how investigators can leverage its resources for data storage and sharing, computation and advanced data analysis workflows. The core provides high-performance storage designed to support large experimental datasets generated across AIMRC cores and collaborating laboratories. The storage infrastructure includes scalable, high-speed arrays with hundreds of terabytes of usable capacity and routine off-site backups, enabling secure, long-term management of imaging and experimental datasets. In addition to storage, the core provides access to powerful computational servers and GPU-accelerated resources for complex data analysis and machine learning workloads in partnership with the Arkansas High-Performance Computing Center (AHPCC), and provides tools such as Jupyter, RStudio and Python-based environments for reproducible research workflows. The Data Science Core actively collaborates with investigators to develop custom analysis pipelines and machine learning approaches for biological datasets. Recent collaborations in automated microscopy image analysis, cell segmentation and tracking, and various machine-learning-powered workflows illustrate how modern AI techniques can accelerate discovery in metabolic and biomedical research.

Biography: Verma is the Manager of the Data Science Core at the Arkansas Integrative Metabolic Research Center (AIMRC) at the U of A, where he supports researchers with machine learning, data science and computational infrastructure for biomedical research. His work focuses on developing AI and machine learning approaches for analyzing biological, medical imaging and multi-omic datasets. Prior to this role, he was a postdoctoral researcher at the U of A and at the Georgia Institute of Technology, where he developed machine learning methods for scientific imaging, molecular discovery and data-driven materials research. Earlier in his career, he worked in industry as a senior scientist, leading R&D on functional coatings. He earned his Ph.D. in materials science and engineering from the Georgia Institute of Technology.

This event is supported by NIGMS of the National Institutes of Health under award number P20GM139768. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Pizza and beverages will be served. Please contact Kimberley Fuller, fullerk@uark.edu, for more information.

For those unable to attend in person, this seminar will also be available via Zoom.