CQB Seminar- Maria Brbic, PhD- EPFL – Duke Center for Quantitative BioDesign

Join the CQB for our seminar series, featuring our first guest speaker of the semester: Dr. Maria Brbic.

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. Her methods have been used by global cell atlas consortia efforts aiming to create reference maps of all cell types with the potential to transform biomedicine, including the Human BioMolecular Atlas Program (HuBMAP) and Fly Cell Atlas consortium. Prior to joining the EPFL faculty in 2022, Maria was a postdoctoral fellow at Stanford University, Department of Computer Science, and was a member of the Chan Zuckerberg Biohub at Stanford. Maria received her Ph.D. from University of Zagreb in 2019 while also researching at Stanford University as a Fulbright Scholar 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.

Abstract:

We are witnessing an AI revolution. At the heart of this revolution are generative AI models that, powered by advanced architectures and large datasets, are transforming AI across a variety of disciplines. But how can AI facilitate and eventually enable groundbreaking discoveries in life sciences? How can it bring us closer to understanding biology – the functions of our cells, their alterations in diseases, and variations across species? In the first part of the talk, I will present AI methods that build upon protein foundation models and enable us to learn cell representations across single-cell RNA-seq datasets from different species. Next, I will demonstrate how generative AI can uncover spatial relationships between cells, enabling the reassembly of tissues from dissociated single cells. Finally, I will discuss the future of discovery in the era of generative AI and foundation models, highlighting the paradigm shift in machine learning required to revolutionize biology.