This is a rolling list of my research projects. I’m broadly interested in computational approaches to understanding molecular biology and mutational processes in cancer. My work draws from unsupervised learning, multi-study and ensemble learning, deep learning, and Bayesian modeling and computation. Many of my current projects explore various aspects of mutational signatures analysis, which models mutational processes in cancer genomes using latent factors.
Some of these are tagged as in-progress, and this list will be updated regularly. Please reach out to me if our research interests align and you would like to chat!
A Comparative Analysis of Bayesian NMF Models
IN PROGRESS
bayesian computation
mutational signatures
genomics
While many Bayesian non-negative matrix factorization (NMF) models have been proposed, there has…
Bayesian Causal Inference for Cancer Mutational Signatures
IN PROGRESS
mutational signatures
causal inference
bayesian computation
genomics
Mutational signatures analysis is a quickly growing field to model…
Deep Unfolding Bayesian Non-Negative Matrix Factorization
IN PROGRESS
deep learning
mutational signatures
genomics
Bayesian non-negative matrix factorization is a method used across fields including genomics, audio and signal processing, and neuroscience. The complexity of the posterior…
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