Open-World Machine Learning for Biomedicine
Event details
| Date | 11.05.2022 |
| Hour | 13:00 › 14:00 |
| Speaker | Maria Brbic (Stanford University) |
| Location | Online |
| Category | Conferences - Seminars |
| Event Language | English |
SV Seminar - Maria Brbic (Stanford University)
Abstract
Biomedical data poses multiple hard challenges that break conventional machine learning assumptions. In this talk, I will highlight the need to transcend our prevalent machine learning paradigm and methods to enable them to become the driving force of novel scientific discoveries. I will focus on open-world deep learning methods that generalize to new scenarios never seen during training and demonstrate the impact they have in single-cell genomics. I will first present a method that transfers knowledge across heterogeneous datasets generated under different distributions, and then describe the paradigm and methods needed to discover previously unobserved phenomena. I will discuss the biological findings enabled by these methods and the conceptual shift they bring in annotating comprehensive single-cell atlas datasets. Altogether, my work demonstrates that generalization to never-before-seen scenarios is not only possible, but it is a necessary component in developing next-generation methods that can reveal new scientific insights.
Bio
Maria Brbic is a postdoctoral researcher in Computer Science at Stanford University. She develops new machine learning methods inspired by challenging problems in biomedicine and applies her methods to advance biomedical research. 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. Previously, she received her PhD degree from University of Zagreb while also researching at Stanford University and University of Tokyo. Her work was awarded with the Fulbright Scholarship, L’Oreal UNESCO for Women in Science Scholarship, Branimir Jernej award for outstanding publication in biology and biomedicine, and the Silver Plaque Josip Loncar for the best doctoral dissertation. She has been named a Rising Star in EECS by MIT. She is a member of the Chan Zuckerberg Biohub at Stanford.
Abstract
Biomedical data poses multiple hard challenges that break conventional machine learning assumptions. In this talk, I will highlight the need to transcend our prevalent machine learning paradigm and methods to enable them to become the driving force of novel scientific discoveries. I will focus on open-world deep learning methods that generalize to new scenarios never seen during training and demonstrate the impact they have in single-cell genomics. I will first present a method that transfers knowledge across heterogeneous datasets generated under different distributions, and then describe the paradigm and methods needed to discover previously unobserved phenomena. I will discuss the biological findings enabled by these methods and the conceptual shift they bring in annotating comprehensive single-cell atlas datasets. Altogether, my work demonstrates that generalization to never-before-seen scenarios is not only possible, but it is a necessary component in developing next-generation methods that can reveal new scientific insights.
Bio
Maria Brbic is a postdoctoral researcher in Computer Science at Stanford University. She develops new machine learning methods inspired by challenging problems in biomedicine and applies her methods to advance biomedical research. 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. Previously, she received her PhD degree from University of Zagreb while also researching at Stanford University and University of Tokyo. Her work was awarded with the Fulbright Scholarship, L’Oreal UNESCO for Women in Science Scholarship, Branimir Jernej award for outstanding publication in biology and biomedicine, and the Silver Plaque Josip Loncar for the best doctoral dissertation. She has been named a Rising Star in EECS by MIT. She is a member of the Chan Zuckerberg Biohub at Stanford.
Practical information
- Informed public
- Free
Organizer
- Prof. Andrew OATES, Dean SV
Contact
- Geneviève PETER