Dr. Jiada Li of the Wadsworth Center’s Bioinformatics Core attended the 90th Cold Spring Harbor Laboratory Symposium on Quantitative Biology, AI in Biology, held in Cold Spring Harbor, New York, from May 26-31, 2026. During the symposium, Dr. Li presented a poster titled “Leveraging a Fine-Tuned Vision Foundation Model for Precise Mycobacterial Cell Segmentation and Fluorescence Event Classification in Multi-Channel Time-Lapse Microscopy Images.” The work, co-authored with Tayler Farrington, Dr. Spencer Bruce, and Dr. Todd Gray, demonstrates the application of advanced artificial intelligence methods to improve the analysis of microscopy images used in infectious disease research.
The research describes a powerful AI-based computational tool that accurately identifies and segments densely clustered mycobacterial cells while automatically tracking cellular interactions and fluorescence events over time in multi-channel time-lapse microscopy images. By automating these complex tasks, the approach enables more precise and efficient quantification of bacterial behavior, providing researchers with new insights into microbial growth dynamics and host-pathogen interactions.
The symposium featured nine oral sessions and three poster sessions highlighting cutting-edge developments at the intersection of artificial intelligence and the biological sciences. Topics included regulatory genomics, sequence-to-function modeling, virtual cell systems, precision oncology, neuroAI, multimodal foundation models, scientific AI agents, and protein structure discovery. Participation in the symposium provided an outstanding opportunity to engage with the latest advances in AI-enabled biological research and to exchange ideas with scientists from academia, industry, and government from around the world. The meeting fostered new collaborations and showcased how artificial intelligence is transforming biological discovery and research.
Dr. Li’s presentation highlights the Wadsworth Center’s growing expertise in applying artificial intelligence and computational biology to public health research, advancing innovative approaches that enhance scientific discovery and strengthen laboratory capabilities for infectious disease surveillance and biomedical investigation.