Maria Brbic
EPFL IC IINFCOM MLBIO
INJ 331 (Bâtiment INJ)
Station 14
1015 Lausanne
Web site: Web site: https://bioengineering.epfl.ch/
EPFL IC IINFCOM MLBIO
INJ 331 (Bâtiment INJ)
Station 14
1015 Lausanne
Web site: Web site: https://sin.epfl.ch
EPFL IC IINFCOM MLBIO
INJ 331 (Bâtiment INJ)
Station 14
1015 Lausanne
Web site: Web site: https://ssc.epfl.ch
Fields of expertise
Machine learning, computational biology
Biography
Maria Brbic (https://brbiclab.epfl.ch/) is is an Assistant Professor of Computer Science and, by courtesy, of Life Sciences at EPFL. She is affiliated with the EPFL Center for Intelligent Systems (CIS) and EPFL Institute of Bioengineering (IBI). Prior to joining EPFL, Maria was a postdoctoral researcher in Computer Science at Stanford University working with Jure Leskovec and was a member of the Chan Zuckerberg Biohub at Stanford. She received her PhD degree from University of Zagreb in 2019, while also researching at Stanford University and University of Tokyo. Her research 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 Josip Loncar Silver Plaque award for the best doctoral dissertation. She has been named a Rising Star in EECS by MIT in 2021. Her research is focused on developing new machine learning methods and applying her methods to advance biomedical research.Teaching & PhD
Teaching
Computer Science
Communication Systems
PhD Programs
- Doctoral program in computer and communication sciences
- Doctoral program in computational and quantitative biology
CS-625: Transfer learning and meta-learning
Deep learning in biomedicine
Courses
Deep learning in biomedicine
Deep learning offers potential to transform biomedical research. In this course, we will cover recent deep learning methods and learn how to apply these methods to problems in biomedical domain.
Transfer learning and meta-learning
This seminar course covers principles and recent advancements in machine learning methods that have the ability to solve multiple tasks and generalize to new domains in which training and test distributions are different.