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This talk will cover methods used to learn multimodal and multiscale representations of human tissue samples from histology images, bulk sequencing, and spatial transcriptomics data derived from cancer biopsies. In particular, the increasing availability and resolution of spatially resolved sequencing on human tissue samples, such as Spatial Transcriptomics (ST), provides rich and spatially resolved molecular information to diagnose and analyse tumours beyond the morphological information routinely available to pathologists through Whole Slide Images. Complex morphological and molecular spatial information becoming available at scale requires building robust multimodal AI architectures that take advantage of such high-dimensional information. We will cover some of the methods we developed for this problem, such as efficiently learning from multi-resolution microscopy, some general multimodal architectures, and SSL for spatial transcriptomics.
Bio:
I grew up in the beautiful city of Hamburg in Germany and moved to the UK after high school for my undergraduate degree at the London School of Economics. About a year into my time at LSE, I realised that I was more interested in mathematics & statistics than the economics aspects of my degree, which eventually led me to self-educate myself in Computer Science on the side. I then did a Master’s in Computer Science at Imperial College London, where I focussed on various disciplines ranging from Cybersecurity to Natural Language Processing. I really enjoyed Natural Language Processing and Machine Learning more generally and started working as a Senior Data Scientist in BCG’s pharmaceutical practice. During my PhD at the University of Cambridge, I focused on representation learning for high-dimensional biomedical data and worked on projects with Flagship Pioneering and Microsoft Research.
