Full Name
Anna Blenda
Position
Professor of Biomedical Science
Institution
University of South Carolina School of Medicine Greenville
Bio
Dr. Blenda joined Department of Biomedical Sciences at University of South Carolina School of Medicine Greenville in December 2016. She earned a combined BS/MS degree (Specialist Diploma) with honors (summa cum laude) in biology and English with minor in German from the Bohdan Khmelnytsky Melitopol State Pedagogical University (Melitopol, Ukraine), a PhD in biotechnology from the Institute of Agroecology and Biotechnology (Kyiv, Ukraine), and a PhD in genetics from Clemson University (Clemson, USA). Immediately following completion of her second PhD in 2003, she was a postdoctoral researcher at the Clemson University Genomics Institute for four years and then a research assistant professor in the Department of Genetics and Biochemistry at Clemson University. During that period she was involved in multidisciplinary research projects developing genomic resources for several model organisms. Dr. Blenda’s next appointments were as an assistant professor and an associate professor with tenure at Erskine College, where she taught genetics, molecular biology, cell biology, and medical biotechnology, as well as conducted genetic and genomic research. In the fall of 2012 she participated in the development and teaching of the inaugural course, Molecular and Cellular Foundations of Medicine, at the University of South Carolina School of Medicine Greenville. In 2015-2016 Dr. Blenda spent nine months on a research sabbatical studying antimicrobial properties of human galectin proteins. During that time she conducted research as a visiting associate professor in the Department of Pathology and Laboratory Medicine at Emory University School of Medicine in the laboratory of Dr. Sean Stowell, who is now affiliated with Harvard Medical School. This sabbatical served as a launching pad for her current projects in translational research, including galectin and glycomic profiling of cancers, as well as genomic and bioinformatic analysis of cancer patients' molecular and clinical data using AI and Machine Learning.
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