The BDHSC would like to congratulate the 2022 R25 Recipients. Please read on to learn more about our recipients and their proposed research.
Assistant Professor, Computer Science and Engineering, AI Institute College of Engineering and Computing
Forest Agostinelli’s research involves designing new artificial intelligence algorithms and applying these algorithms to problems in the sciences. Simultaneously, his research draws upon the sciences to provide inspiration for new artificial intelligence algorithms. Dr. Agostinelli will work on “Quantifying Vascular Calcification and Predicting Patient Outcome with Synthetic Data, Deep Neural Networks, and Logic Programming” at this R25 award.
Clinical Assistant Professor of Medicine, UofSC SOM; Adjunct Faculty, UofSC College of Pharmacy
Pamela Bailey has interests in antibiotic stewardship and epidemiology, and her research interests include hand hygiene, personal protective equipment and quality improvement, particularly in antimicrobial stewardship. She will do research about “Antimicrobial use in the outpatient setting during COVID-19 pandemic” within this R25 award.
Assistant Professor, Epidemiology and Biostatistics Arnold School of Public Health
Mufaro Kanyangarara is an infectious disease epidemiologist with more than a decade of experience designing and implementing public health research projects internationally. Her research focuses on the epidemiology and control of infectious diseases and improving maternal, newborn and child health. Dr. Kanyangarara’s R25 proposal is titled Leveraging Big Data for the Prevention and Control of Sexually Transmitted Infections (STIs).
Assistant Professor, Health Services Policy & Management Arnold School of Public Health
Dr. Liang’s recent work involves heterogeneous health data integration, Electronic Health Records (EHR) based phenotyping and data mining, medical knowledge representation, EHR-based machine learning and predictive modeling, and clinical natural language processing. The title of Dr. Liang’s R25 proposal is Informatics Approach to Identification and Deep Phenotyping of PASC Cases.