Interpretable Inference via Principled BNP Approaches in Biomedical Research and Beyond

(08 Jul 2024–02 Aug 2024)

Organizing Committee





  • Cheng Li (National University of Singapore)

Contact Information

General Enquiries: Ims-enquiry(AT)
Scientific Aspects Enquiries: pmueller(AT)


The program will explore new directions in nonparametric Bayesian (BNP) theory and applications, focusing on applications in biomedical research problems. Specific themes in theory are: (1) complex data structures, dependent BNP models and quantification of the induced dependence; (2) efficient and scalable computational algorithms for BNP implementation; (3) summarizing BNP inference as a decision problem. And in applications: (1) real world evidence in clinical studies; (2) single cell RNA-seq data; (3) spatial transcriptomics; (4) subgroup analysis; (5) microbiome data; (6) network- and tree-structured data.


Introductory lecture on Nonparametric Bayesian Data Analysis by Michele Guindani (UCLA)9 July 2024 (morning)N/A
Opening workshop9–12 July 2024N/A
Tutorial on Foundations of nonparametric Bayesian inference by Steven McEachern (Ohio State University) & Long Nguyen (University of Michigan)16 and 17 July 2024N/A
Research seminars15 and 19 July 2024N/A
Tutorial on BNP in biomedical research by Yanxun Xu (Johns Hopkins University) and Yang Ni (Texas A&M University)23 and 25 July 2024N/A
Research seminars22, 24 and 26 July 2024N/A
Closing workshop and ISBA BNP networking meeting (TBC)30 July–2 August 2024N/A


IMS Auditorium


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