Dorina Thanou

Dorina Thanou

Sr. Data Scientist
Alumni
(Alumni)

Dorina is a Senior Data Scientist at the Swiss Data Science Centre since December 2016. Prior to that, she was a postdoctoral researcher at the Signal Processing Laboratory (LTS4) of EPFL, Switzerland. She got her M.Sc. and Ph.D. in Communication Systems and Electrical Engineering respectively, both from EPFL, and her Diploma in Electrical and Computer Engineering from the University of Patras, Greece. In summer 2014, she was a research intern with Microsoft Research, Redmond, USA, working on the compression of 3D point clouds. 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 models for personalized medicine. After four years at the Swiss Data Science Center, Dorina has joined the pole of the Center for Intelligent Systems (CIS) at EPFL on Medicine and Healthcare as an AI Research Scientist.

Projects

COVID-19

Completed
Epidemic Forecasting
Biomedical Data Science
Big Science Data
Digital Administration

HMP

Completed
The Human Measurement Project
Biomedical Data Science
Digital Administration

Publications

Sherratt, K.; Gruson, H.; Grah, R.; Johnson, H.; Niehus, R.; Prasse, B.; Sandmann, F.; Deuschel, J.; Wolffram, D.; Abbott, S.; et al. "Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations" eLife 12 e81916 2023 View publication
Cramer, E. Y.; Huang, Y.; Wang, Y.; Ray, E. L.; Cornell, M.; Bracher, J.; Brennen, A.; Rivadeneira, A. J. C.; Gerding, A.; House, K.; et al. "The United States COVID-19 Forecast Hub dataset" Scientific Data 9 1 462 2022 View publication
Krymova, E.; Béjar, B.; Thanou, D.; Sun, T.; Manetti, E.; Lee, G.; Namigai, K.; Choirat, C.; Flahault, A.; Obozinski, G. "Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide" Proceedings of the National Academy of Sciences 119 32.0 e2112656119 2022 View publication
Dong, X.; Thanou, D.; Rabbat, M.; Frossard, P. "Learning Graphs From Data: A Signal Representation Perspective" IEEE Signal Processing Magazine 36 3.0 44-63 2019 View publication
Thanou, D.; Frossard, P. "Learning of robust spectral graph dictionaries for distributed processing" EURASIP Journal on Advances in Signal Processing 2018 1.0 67 2018 View publication
Thanou, D.; Dong, X.; Kressner, D.; Frossard, P. "Learning Heat Diffusion Graphs" IEEE Transactions on Signal and Information Processing over Networks 3 3.0 484-499 2017 View publication

Mentioned in

June 29, 2017

Open and reproducible environmental science: from theory to equations and algorithms

Open and reproducible environmental science: from theory to equations and algorithms

We need complex models that accurately represent the feedbacks between different processes and compartments to inform us how a perturbation in one component may affect other components of the coupled climate-earth surface system that are relevant to us.

Case Studies

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