Machine learning for biomedical health applications

With researchers at Princeton, Stanford, and UNC, I am collaborating on several application areas related to the biomedical health sciences:

  • Experimental design for spatial genomics
  • Bayesian inverse reinforcement learning for health applications
  • Efficient online changepoint detection in time series


Optimizing the design of spatial genomics studies

In revision, 2023

PDF bioRxiv

Kernel density Bayesian inverse reinforcement learning

Submitted (preliminary version appeared in AABI), 2023

Multi-fidelity Bayesian experimental design using power posteriors

NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, 2022

PDF Workshop Link

Active multi-fidelity Bayesian online changepoint detection

Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021

PDF Code arXiv BibTeX

Diana Cai
Center for Computational Mathematics

I am broadly interested in machine learning and statistics, and in particular, developing robust and reliable methods for modeling and inference.