Professor Yajun Yan received his Bachelor's and Master's degrees in Biochemical Engineering from Beijing University of Chemical Technology in 1999 and 2002 respectively. He then obtained his PhD in Chemical and Biological Engineering from the State University of New York at Buffalo in 2008. From 2008 to 2010, he worked as a postdoctoral researcher in Synthetic Biology and Metabolic Engineering at the University of California, Los Angeles. Since 2010, he has been a faculty member at the University of Georgia, where he was promoted to tenured Associate Professor in 2015 and to full Professor in 2019.

Professor Yajun Yan has a broad academic background in Chemical and Biological Engineering. Over the past 20 years, his research has focused mainly on Synthetic Biology and Metabolic Engineering. He is dedicated to developing microbial and enzymatic methods for efficient production of biofuels, bulk and fine chemicals, natural products, and drug-active molecules. His research projects cover multiple disciplines, including Biochemistry, Microbiology, Molecular Biology, Protein Engineering, Metabolic Engineering, and the principles and techniques of Synthetic Biology.

His AI-related research interests include applying AI techniques to design and engineer biological systems. These systems can be enzymes, metabolic pathways, metabolic regulations, genetic circuits and even whole organisms. In addition, he is interested in utilizing AI to identify potential drug candidates, optimize production pathways, and improve the yields of bio-based products.

To date, Professor Yajun Yan has published over 110 academic papers in prestigious journals in his field, such as Nature Biotechnology, Nature Communications, PNAS, Trends in Biotechnology, Biotechnology Advances, Metabolic Engineering, Green Chemistry, ACS Synthetic Biology, etc. He has also co-authored three academic books and holds 16 patents in multiple countries including the US, China, and Canada.

The Kannan Lab is interested in developing and applying deep learning and machine learning models for bioinformatics applications. Ongoing funded projects are focused on applying large language models for protein function prediction, classification, and text mining. The lab is also interested in representation learning on knowledge graphs, contrastive learning, and generative diffusion models for protein design and dynamics. 

If you are interested in any of these areas, please contact Dr. Kannan (nkannan@uga.edu) with a statement of research interest and a CV.

Dr. Xi previously practiced as a surgeon before pursuing graduate studies for a PhD in cell biology. Currently, his research programs, supported by multiple NIH R01 grants, focus on cancer drug development, specifically targeting metastatic breast cancer and colorectal cancer. Dr. Xi possess extensive teaching and mentoring experience across various levels, including undergraduate students, graduate students, medical students, postdoctoral fellows, and junior faculty members. Many of his trainees have successfully established independent careers. Dr. Xi’s current research interest lies in the intersection of AI and biomedical research, particularly AI's role in cancer drug development. Since joining UGA as the Department Head of Pharmaceutical and Biomedical Sciences at the College of Pharmacy in August 2023, he is keen on expanding his focus to AI and biomedical research.

Professor Y. George Zheng is a Full Professor in the Department of Pharmaceutical and Biomedical Sciences at University of Georgia. Dr. Zheng’s research interests are mainly focused on developing innovative pharmacological molecules for the treatment of cancer and metabolic diseases. Prof. Zheng’s laboratory has the expertise in the area of in silico drug screening, molecular docking, structure-based drug design, and medicinal chemistry. He has published more than 100 peer-reviewed papers in the leading journals such as Nature, Nature Communications, Nucleic Acid Research, Angewandte Chemie, Journal of American Chemical Society, Journal of Medicinal Chemistry, etc. He has been supervising undergraduate students, Masters and doctoral graduate students, and postdoctoral trainees. The group welcomes students who have molecular science foundation with sincere enthusiasm to create next blockbuster drugs for curing human diseases.

Matthew Schmidt, Ph.D., is Associate Professor at the University of Georgia (UGA) in the Learning, Design, and Technology department. His primary research interest includes design and development of innovative educational courseware and computer software with a particular focus on individuals with disabilities, their families, and their providers. His secondary research interests include learning in extended reality (inclusive of virtual reality, augmented reality, and mixed reality) and Learning Experience Design.

Dr. Hall is a computational astrophysicist who studies exoplanet formation. Her approach combines machine learning techniques with hydrodynamical simulations of protoplanetary accretion discs to uncover forming exoplanets hidden in telescope data. Dr. Hall leads a team focused on refining these techniques to increase our fundamental understanding of the planet formation process.