Abstract: Given the fast development of technology and automation systems, researchers are generating tons of data from scientific experiments to facilitate discoveries, especially in the areas of biology and medicine. However, large-scale biomedical data make it challenging for biologists or medical researchers to more effectively utilize these data with robust and powerful methods for novel and reproducible discoveries. by transferring prior knowledge for new biological discoveries with an efficient Human-Aware AI+Biomedicine system. First, I will show the importance of having a fair evaluation framework to rank the contributions of foundation models (FMs) for biological data analysis and demonstrate the importance of prior knowledge. Second, I will show the strength of leveraging prior knowledge to gain new insights in analyzing multi-modal data for clinical and biomedical applications by building Expert AI models. Finally, I will discuss our progresses of building AI agents for accelerating scientific discoveries which facilitate generation tasks in multiple areas and enclose the loop.
Bio: Tianyu is a PhD candidate from Yale University working on artificial intelligence (AI) and machine learning (ML) methods for scientific research. Previously, Tianyu obtained bachelor’s degrees with highest honors from the joint program between Zhejiang University and UIUC. Having good understanding for both machine learning concepts and biological knowledge, Tianyu has leaded multiple research projects based on multimodal AI Agents and large reasoning models for computational biology research. He has published several papers in the high-impacted journals (Nature and Cell series) and ML conferences (NeurIPS, ICML). He also severed as reviewers and organizers for key conferences in this field. His research is supported by fundings from NIH, NSF, Google, and OpenAI.