Teaming for Interdisciplinary Research Pre-Seed Program

The Systems Modeling and Data Analytics Core (SMDA) represents a group of faculty who were hired through (or with significant interests in) UGA’s Presidential Interdisciplinary Faculty Hiring Initiative in Data Science and Artificial Intelligence. Faculty within these areas face technical challenges which traditional academic silos are not well suited to address including:

• Student training: statistics, data-analytics, machine-learning, etc
• Data accessibility: licensing, pre-cleaned datasets
• Data-type expertise: genomics, electronic medical records, drug- screening, chemoinformatics, scientific literature, etc.
• Data–analysis workflows: standard methods code or licensing

This core is being founded to alleviate these pain-points by facilitating coordination between data-driven investigators at UGA (Fig 1). For example, investigators within the UGA colleges of: Arts and Sciences (FAS), Veterinary

Medicine (CVM), Pharmacy (COP) and Public Health (CPH), work with datasets on genomics, therapeutic response and toxicity. Better coordination of these data-analytics efforts has the potential to reduce duplicative labor, improve workforce training and enable new research that is clinically relevant and scientifically and statistically rigorous (Fig. 1).

Figure 1. “Rosetta Stone Opportunity”. Common datasets or data-types can serve as a bridge between academic silos at the University of Georgia.

The mission of the SMDA Core is to facilitate collaborative research through improved workforce training and communication in the domain of data-analytics and modeling. The SMDA will achieve its mission by building infrastructure at two different scales:

• Tactical: Alleviate pain-points for data-driven healthcare research
• Strategic: Build multi-PI working groups that bridge UGA’s discipline- and data-expertise

Plan to Build Core Foundation for Multi-PI research

Over the next year, we plan to build a collaborative network for data-analytics driven research at UGA using the conceptual plan outlined in Figure 2. First, we will “Map” data-type expertise at

UGA to identify tactical overlap across UGA. This first step is critical as currently, data-driven health research is spread across UGA and separated by many traditional silos (Fig 1). Second, we will establish datatype working groups (“Bridge”) to aid coordination of these investigators to reduce duplicative labor and share training resources. These working groups will establish hubs of data-expertise that will be foundational to any large-scale data-driven projects at UGA. The goal of these working groups will be to (1) identify pain-points (2) share resources and (3) coordinate group infrastructure that benefits all members. Third, we will establish Research Question based Working Groups (RQWG’s = “Build”) to link clinical/experimental investigators with data-expertise WG’s. Critical areas of focus will be improving communication and coordination between computational and non- computational investigators. The SMDA website will provide a critical hub for this research by providing tutorials and graphic interfaces for common datasets that are common touchstones across research communities. In addition, these WG’s will establish core training needs for laboratory and clinical researchers

Figure  2.  Current  Plan  to democratize access to data-tools (objective #1) and enable team- based research (objective #2)

who are increasingly becoming responsible for analysis of large datasets. For example, in the Fall of 2024, several SMDA faculty began developing a data-analytics course for experimental investigators that focused on building basic data-analytics skills using the R-programming language(PHRM8210). This work has proven foundational to early collaborative research publications and proposals which are being developed now.

Team Lead

Eugene Douglass
eugene.douglass@uga.edu
College of Pharmacy
Pharmaceutical and Biomedical Sciences

Team Members

Jonathan Mochel
jpmochel@uga.edu
College of Veterinary Medicine
Department of Pathology

Karin Allensphach
Karin.Allenspach@uga.edu
College of Veterinary Medicine
Department of Pathology

TaoTao Wu
taotao.wu@uga.edu
College of Engineering
Biomedical Engineering

Aditya Mishra
Aditya.Mishra@uga.edu
Franklin College of Arts and Sciences
Department of Statistics

Matthew Schmidt
matthew.schmidt@uga.edu
Mary Frances Early College of Education
Department of Workforce Education and Instructional Technology

Blake Billmyre
Blake.Billmyre@uga.edu
College of Pharmacy
Pharmaceutical and Biomedical Sciences

Andreas Handel
ahandel@uga.edu
College of Public Health
Epidemiology & Biostatistics

Lorenzo Zapata
Lorenzo.VillaZapata@uga.edu
College of Pharmacy
Clinical and Administrative Pharmacy

Niying Li
Niying.Li@uga.edu
College of Pharmacy
Clinical and Administrative Pharmacy

Tatum Mortimer
tatum.mortimer@uga.edu
College of Veterinary Medicine
Department of Population Health