Learning objectives

• Single-cell omics have advanced our understanding of cellular diversity, though challenges remain in data analysis
• Deep generative AI allows cost-effective generation of high-quality single-cell omics data for improving research accessibility
• Deep generative AI-driven integration of multi-modal single-cell data offers insights into cellular states and transitions for novel discoveries

Speaker biography

Dr. Jun Ding is a Tenure-track Assistant Professor at McGill University, an affiliated member of RI-MUHC and Mila – Quebec AI Institute, and a Junior 1 FRQS Scholar. His research focuses on developing deep generative neural networks to decode cellular dynamics from single-cell omics data, bridging AI and life sciences to uncover disease mechanisms and therapeutic strategies. Dr. Ding has published in leading journals, including Nature Communications, Genome Research, Cell Stem Cell, and Genome Biology. His work, supported by CIHR and NSERC grants, advances AI-driven solutions for diagnostics and therapeutics in complex diseases.