Machine learning for biomedical health applications

Dec 10, 2022

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

applications

Publications

Optimizing the design of spatial genomics studies
Andrew Jones, Diana Cai, Didong Li, Barbara E. Engelhardt
In revision, 2023

PDF bioRxiv

Kernel density Bayesian inverse reinforcement learning
Aishwarya Mandyam, Didong Li, Diana Cai, Andrew Jones, Barbara E. Engelhardt
Submitted (preliminary version appeared in AABI), 2023

Multi-fidelity Bayesian experimental design using power posteriors
Andrew Jones*, Diana Cai*, Barbara E. Engelhardt
NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, 2022

PDF Workshop Link

Active multi-fidelity Bayesian online changepoint detection
Gregory G. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams
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.