Xinhao Liu will present his FPO "Computational Tools for Alignment and Integration of Spatial Multimodality Data" on Wednesday, May 6, 2026 at 1:30p in COS 402. The members of Xinhao’s committee are as follows: Examiners: Ben Raphael (Adviser), Yuri Pritykin, Ellen Zhong Readers: Olga Troyanskaya, Li Ding (Washington University in St. Louis) A copy of his thesis is available upon request. Please email gradinfo@cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend the talk. Abstract follows below: Recent advances in spatial sequencing technologies, such as spatial transcriptomics, proteomics, and epigenomics, enable the in situ measurement of molecular features across intact tissue sections, providing new insights into the spatial organization of biological processes. As datasets grow in complexity, spanning multiple tissue slices, developmental timepoints, and molecular modalities, there is an increasing need for computational methods that can align and integrate spatial omics data into coherent, high-resolution representations of tissues and organs. Accurate alignment across slices is essential not only for reconstructing tissue architecture but also for comparing molecular programs across spatial scales, developmental stages, and experimental modalities. This thesis presents four computational methods for aligning and integrating spatial omics data across developmental timepoints and experimental modalities. First, we introduce PASTE2, a method for alignment and three-dimensional (3D) reconstruction of multislice spatial transcriptomics data that allows slices to overlap only partially. Next, we present DeST-OT, which aligns spatial transcriptomics slices collected at different developmental timepoints to capture tissue dynamics over time. We then introduce MOSAICField, a framework for aligning spatial slices across arbitrary combinations of experimental modalities, such as spatial transcriptomics, spatial proteomics, and histology images. Finally, we introduce OTVI, a machine learning framework that integrates multiple spatial modalities from related tissue slices into a common coordinate framework while learning biologically meaningful representations for each spatial location.