Statistical Bioinformatics Seminar: Dr Haotian Cui, University of Toronto

Hosted by Sydney Precision Data Science Centre 

Speaker: Dr Haotian Cui, University of Toronto 

Abstract: This presentation will discuss recent advances in foundation models for
single-cell omics and therapeutic discovery, focusing on the development of scGPT, a
generative transformer model trained on over 33 million single-cell profiles.  It will
cover key design principles—such as generative pretraining and multi-task
alignment—that enable broad applications including cell type annotation, perturbation
response prediction, and reference mapping.  The talk will also highlight emerging
multimodal extensions such as scGPT-spatial and MethylGPT, and emphasize the importance
of perturbational training for modeling cellular dynamics.  Finally, it will introduce
LUMI-lab, a closed-loop self-driving platform developed by Dr.  Cui and collaborators,
which integrates model-guided design, synthesis, and validation to accelerate mRNA
delivery—demonstrating a scalable framework for virtual cell modeling and active
learning in biology.  

About the speaker: Dr Haotian Cui is a researcher specializing in machine learning,
genomics, and drug discovery.  He recently completed his Ph.D.  in Computer Science at
the University of Toronto, advised by Prof Bo Wang.  His research focuses on developing
large-scale self-supervised and generative foundation models for single-cell omics and
molecular biology, integrating AI with experimental pipelines to accelerate therapeutic
discovery.  He is the lead author of scGPT—one of the first generative foundation
models for single-cell multi-omics.  He also led the development of LUMI-lab, an
autonomous AI-driven platform for mRNA therapeutics.  He has published numerous papers
in leading journals and conferences, including Nature, Nature Methods, Nature
Communications, ACL, EMNLP, ICML.  

This event will be held online.  

Zoom: https://uni-sydney.zoom.us/j/85114748391