Maria Brbic

Edit profile

maria.brbic@epfl.ch +41 21 693 43 42 https://brbiclab.epfl.ch/

Google Scholar ID

EPFL IC IINFCOM MLBIO
INJ 330 (Bâtiment INJ)
Station 14
1015 Lausanne

+41 21 693 43 42
Office: INJ 330
EPFL > IC > IINFCOM > MLBIO

+41 21 693 43 42
Office: INJ 330
EPFL > SV > IBI-SV > IBI-SV-GE

Web site: Web site: https://bioengineering.epfl.ch/

+41 21 693 43 42
Office: INJ 330
EPFL > IC > IC-SIN > SIN-ENS

Web site: Web site: https://sin.epfl.ch

+41 21 693 43 42
Office: INJ 330
EPFL > IC > IC-SSC > SSC-ENS

Web site: Web site: https://ssc.epfl.ch

+41 21 693 43 42
Office: INJ 331
EPFL > SV > IBI-SV > UPBRBIC

+41 21 693 43 42
Office: INJ 331
EPFL > VPA-AVP-DLE > AVP-DLE-EDOC > EDIC-ENS

vCard
Administrative data

Fields of expertise

Machine learning, computational biology

Biography

Maria Brbic (https://brbiclab.epfl.ch/) is is an Assistant Professor of Computer Science and Life Sciences at EPFL. She is also affiliated with the EPFL AI Center 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 and she received an Early Career Award by SIB in 2023. Her research is focused on developing new machine learning methods and applying her methods to advance biomedical research. Her contributions to the field have earned her prestigious funding, including the SNSF Starting Grant.

Teaching & PhD

Teaching

Computer Science

Communication Systems

PhD Students

Fan Shuyang, Gadetskii Artem, Jiang Yulun, Wen Shuo, Yu Tingyang,

PhD Programs

Doctoral program in computer and communication sciences
Doctoral program in computational and quantitative biology

Teaching
CS-502: Deep learning in biomedicine
CS401: Applied Data Analysis
CS-625: Transfer learning and meta-learning

Courses

Applied data analysis

This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.)

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.