In this broadly defined project, we develop new machine-learning methods to infer or use phylogenies. The goal is to see if machine learning methods can beat existing methods in specific tasks such as phylogenetic placement or outlier detection. The focus is on finding innovative ways to update machine learning methods or adopt them to phylogenetics. We also work on incorporating phylogenies as prior data into machine learning methods for answering other questions (e.g., metagenomic classification).

In this broadly defined project, we develop novel methods for inferring phylogenetic trees from genome-wide datasets. The project involves modeling evolutionary processes, developing scalable algorithms, analyzing their statistical properties in theory, applying them to large simulated and biological data to evaluate them, and providing software to the community.

Extrachromosomal DNA formation is an important pathological condition found in nearly a third of cancers and all cancer subtypes. Our lab is developing computational tools to characterize their structural and functional properties of ecDNA and related focal amplifications. 

The interested students should have an interest in learning about,  designing and implementing graph algorithms, and should commit to taking my winter class CSE280A.

Current approaches profiling the transcriptome of cells in tissues offer a rich yet immobile snapshot of biology. In collaboration with the Kosuri Lab at the Salk Institute, we are developing approaches to infer cell kinematics and tissue infiltration timelines to obtain a finer temporal view of key processes that impact T cell differentiation and function during an anti-tumoral response. This project will rely on the development of both computational tools and wet lab protocols.