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

Submitted, 2023

PDF bioRxiv

Multi-fidelity Bayesian experimental design using power posteriors

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

PDF Workshop Link

Kernel density Bayesian inverse reinforcement learning

In preparation (preliminary version appeared in AABI), 2022

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

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