BEGIN:VCALENDAR
VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
X-WR-CALNAME:AIMRC Data Science Core: Storage and Computational Servers and
  Related Workflow
X-WR-TIMEZONE:Central Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20261017T123936Z
UID:tag:localist.com\,2008:EventInstance_52365585502810
DTSTART:20260401T175500Z
DTEND:20260401T184500Z
DESCRIPTION:The Arkansas Integrative Metabolic Research Center (AIMRC) will
  host Dr. Prateek Verma\, Manager of the AIMRC Data Science Core\, at 12:5
 5 p.m. on Wednesday\, April 1\, in CHEM 0144. Dr. Verma will highlight the
  unique complex-analysis and machine-learning analytical capabilities of t
 he Data Science Core and how core staff can assist faculty to advance rese
 arch. \n\n \n\nAbstract: The Data Science Core of the Arkansas Integrative
  Metabolic Research Center (AIMRC) provides centralized computational infr
 astructure and expertise to support data-driven biomedical research across
  the University of Arkansas. This seminar will introduce the services and 
 capabilities of the core and demonstrate how investigators can leverage it
 s resources for data storage and sharing\, computation\, and advanced data
  analysis workflows. The core provides high-performance storage designed t
 o support large experimental datasets generated across AIMRC cores and col
 laborating laboratories. The storage infrastructure includes scalable\, hi
 gh-speed arrays with hundreds of terabytes of usable capacity and routine 
 off-site backups\, enabling secure\, long-term management of imaging and e
 xperimental 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 Arkan
 sas High-Performance Computing Center (AHPCC)\, and provides tools such as
  Jupyter\, RStudio\, and Python-based environments for reproducible resear
 ch workflows. The Data Science Core actively collaborates with investigato
 rs to develop custom analysis pipelines and machine learning approaches fo
 r 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 disco
 very in metabolic and biomedical research.\n\n \n\nBiography: Prateek Verm
 a is the Manager of the Data Science Core at the Arkansas Integrative Meta
 bolic Research Center (AIMRC) at the University of Arkansas\, where he sup
 ports 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 imagin
 g\, and multi-omic datasets. Prior to this role\, he was a postdoctoral re
 searcher at the University of Arkansas and at the Georgia Institute of Tec
 hnology\, where he developed machine learning methods for scientific imagi
 ng\, 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 PhD in Materials Science and Engineerin
 g from the Georgia Institute of Technology.\n\nThis 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 nece
 ssarily represent the official views of the National Institutes of Health.
 \n\n \n\nPizza and beverages will be served. Please contact Kimberley Full
 er\, fullerk@uark.edu\, for more information. \n\n \n\nFor those unable to
  attend in person\, this seminar will also be available via Zoom.
GEO:36.067978;-94.172898
LOCATION:(CHEM) Chemistry\, 0144
SUMMARY:AIMRC Data Science Core: Storage and Computational Servers and Rela
 ted Workflow
URL;VALUE=URI:https://calendars.uark.edu/event/aimrc-data-science-core-stor
 age-and-computational-servers-and-related-workflow
CATEGORIES:Lectures
END:VEVENT
END:VCALENDAR