Learning objectives

• Introduce generative AI methods designed to uncover and model the complex relationships between different molecular layers (omics) in biology.
• Demonstrate specific applications, such as digitally reassembling tissues from single cells and linking cellular morphology to gene expression data.
• Conclude with a methodology for building effective multi-modal AI models even when paired training data is scarce

Speaker biography

Maria Brbic (https://brbiclab.epfl.ch/) is an Assistant Professor of Computer Science and of Life Sciences at the Swiss Federal Institute of Technology, Lausanne (EPFL). She develops new machine learning methods and applies her methods to advance biology and biomedicine. Prior to joining the EPFL faculty in 2022, Maria was a postdoctoral fellow at Stanford University. Maria received her Ph.D. from University of Zagreb in 2019 while also researching at Stanford University and University of Tokyo. Among other awards and recognitions, she was named a Rising Star in EECS by MIT in 2021, she received the Early Career Bioinformatics Award by SIB in 2023 and she was awarded with the SNSF Starting Grant in 2024. Maria is a CIFAR Fellow in the Multiscale Human Program.