Background
Austin D. Swafford, PhD is a scientist whose expertise spans a wide range of fields critical to the advancement of science, technology and healthcare. He has advised groups and business units in both academia and industry and led multiple private-public partnerships focused on automation, artificial intelligence, drug discovery, immunology, genetics, microbiomics, and nanotechnology, as well as curriculum, business, and software development.
Dr. Swafford has previously led and conducted research on a wide range of topics in biomedical research and biotechnology. His most recent work has examined the impact of microbes, including SARS-CoV-2, on the health of humans, animals, and the environment as well as developing state-of-the-art methods, technologies, software, and bioinformatics approaches to characterizing the microbiome.
Biography
Dr. Swafford earned his doctoral degree in Medical Genetics from the joint National Institutes of Health/University of Cambridge graduate program supported by a National Science Foundation Graduate Fellowship in Bioengineering to study the etiology and early diagnosis of type 1 diabetes.
He then completed a postdoctoral fellowship in beta cell regeneration under the Director of Genetics at the Genomics Institute of the Novartis Research Foundation and then spent several years in industry developing and implementing high-throughput workflows using automation platforms at leading pharmaceutical and biotech companies.
As the first Director of Research for the Center of Microbiome Innovation at UC San Diego, he helped design, recruit, manage, and lead numerous public-private partnerships.
Dr. Swafford has also co-authored peer-reviewed articles and patent applications on a wide range of subjects including basic and applied biology as well as novel sample processing and data analysis tools and methods to conduct large-scale microbiome studies. His work has been published in the Proceedings of the National Academy of Sciences, Nature Biotechnology, and Nature Methods, among other venues, including the application of machine learning for the detection of cancer using microbial profiles in tissue and blood.