Dorina Thanou

EPFL STI IEM LTS4
ELE 239 (Bâtiment ELE)
Station 11
1015 Lausanne

EPFL STI IEM LTS4
ELE 239 (Bâtiment ELE)
Station 11
1015 Lausanne

EPFL STI IEM LTS4
ELE 239 (Bâtiment ELE)
Station 11
1015 Lausanne

EPFL STI IEM LTS4
ELE 239 (Bâtiment ELE)
Station 11
1015 Lausanne

Expertise

Data science, Machine learning, Signal processing, AI for medicine and biology
Dorina Thanou is a senior researcher and lecturer at EPFL, leading strategic research initiatives on AI for Health and Science under the EPFL AI Center. Her research interests include graph-based signal processing for data representation and analysis, as well as machine learning, with a particular focus on the design of interpretable and robust models for biomedicine. She is currently leading many interdisciplinary collaborations that aim at fostering the adoption of AI in medicine and health.
Dorina got her M.Sc. (August 2010) and Ph.D. (February 2016) in Communication Systems and Electrical Engineering respectively, both from EPFL, Switzerland, and her Diploma in Electrical and Computer Engineering (July 2008) from the University of Patras, Greece. The topic of her PhD was on representation learning algorithms for graph structured data. In summer 2014, she was a research intern with Microsoft Research, Redmond, USA, working on the compression of 3D point clouds. Between 2016 and 2020, Dorina has been with the Swiss Data Science Center, where she mainly worked on many interdisciplinary research collaborations, in academia and industry. Between 2020-2024, she was with the Center for Intelligent Systems at EPFL, leading the Intelligent Systems for Medicine and Health research pillar.
Among other technical activities, Dorina is currently one of the co-organizers of the Data Science on Graphs webinar series, initiated by the Data Science Initiative of the IEEE Signal Processing Society. She has been the Finance chair for the IEEE Data Science Workshop in 2018, the Finance chair and the invited speakers co-chair for the Graph Signal Processing workshop in 2018. She has served as an Associate Editor of the IEEE Transactions on Image Processing (2021-2022), IEEE Transactions on Signal and Information Processing over Networks (2022-), an area chair for the Learning on Graphs conference, and a reviewer for many international conferences and journals in signal processing and machine learning, such as IEEE Transactions on Signal Processing, IEEE ICASSP, NeurIPS, ICLR among others. She is currently a member of the EURASIP technical area committee on Biomedical Images & Signal Analytics (BISA), and a steering committee member of the Data Science Initiative of the IEEE Signal Processing Society.
Dorina received the Best Student Paper Award at ICASSP 2015, and the Best Paper Award at PCS 2016. In October 2021, she has been an elected an ELLIS Scholar.
She is an IEEE senior member.

Awards

ELLIS Scholar in Geometric Deep Learning

2021

Best Student Paper Award, IEEE International Conference on Speech and Signal Processing (ICASSP).

2015

Top 10% Paper Award, IEEE International Conference in Image Processing (ICIP) .

2015

Best Paper Award, Picture Coding Symposium (PCS)

2016

IEEE Senior Member

2023

Publications

Teaching & PhD

PhD Students

Sevda Ögüt, Tuna Alikasifoglu, Manuel Madeira, Vasiliki Rizou, Jérémy Jean Philippe Baffou

Courses

ELLIS Summer School on AI for Health

EE-740

The EPFL AI Center and the ELLIS EPFL unit are organizing the AI for Health Summer School, taking place on the EPFL campus from 7th to 11th July, 2025. This intensive week will delve into how AI is transforming biomedicine, with a focus on the intersection of AI, life sciences, and medicine.

Graph representations for biology and medicine

EE-626

Systems of interacting entities, modeled as graphs, are pervasive in biology and medicine. The class will cover advanced topics in signal processing and machine learning on graphs and networks, and will showcase applications of the tools in biomedicine.

Network machine learning

EE-452

Fundamentals, methods, algorithms and applications of network machine learning and graph neural networks