Fields of expertise
BiographyDorina Thanou is a senior researcher and lecturer at EPFL, leading the Intelligent Systems for Medicine and Health research pillar, under the Center for Intelligent Systems, since November 2020. 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 healthcare. She is currently leading many interdisciplinary collaborations that aim at fostering the adoption of AI in medicine and health.
She 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.
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 is an Associate Editor of the IEEE Transactions on Image Processing (2021-), 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.
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.
ELLIS Scholar in Geometric Deep Learning
Best Student Paper Award, IEEE International Conference on Speech and Signal Processing (ICASSP).
Top 10% Paper Award, IEEE International Conference in Image Processing (ICIP) .
Best Paper Award, Picture Coding Symposium (PCS)
Reconstruction of Time-Varying Graph Signals via Sobolev SmoothnessIeee Transactions On Signal And Information Processing Over Networks. 2022-01-01. DOI : 10.1109/TSIPN.2022.3156886.
Combining anatomical and functional networks for neuropathology identification: A case study on autism spectrum disorderMedical Image Analysis. 2021-04-01. DOI : 10.1016/j.media.2021.101986.
node2coords: Graph Representation Learning with Wasserstein BarycentersIeee Transactions On Signal And Information Processing Over Networks. 2021-01-01. DOI : 10.1109/TSIPN.2020.3041940.
Graph Signal Processing for Machine Learning: A Review and New PerspectivesIeee Signal Processing Magazine. 2020-11-01. DOI : 10.1109/MSP.2020.3014591.
Mask Combination of Multi-Layer Graphs for Global Structure InferenceIeee Transactions On Signal And Information Processing Over Networks. 2020-01-01. DOI : 10.1109/TSIPN.2020.2995515.
Learning of robust spectral graph dictionaries for distributed processingEurasip Journal On Advances In Signal Processing. 2018-10-24. DOI : 10.1186/s13634-018-0584-2.
Learning Laplacian Matrix in Smooth Graph Signal RepresentationsIEEE Transactions on Signal Processing. 2016. DOI : 10.1109/TSP.2016.2602809.
Distributed average consensus with quantization refinementIEEE Transactions on Signal Processing. 2013. DOI : 10.1109/Tsp.2012.2223692.
Predicting future myocardial infarction from angiographies with deep learning2021. Medical Imaging meets NeurIPS 2021, [Online only], December 14, 2021.
Interpretable Stability Bounds for Spectral Graph Filters2021-01-01. International Conference on Machine Learning (ICML), ELECTR NETWORK, Jul 18-24, 2021.
A Graph Signal Processing Framework for the Classification of Temporal Brain Data2020-01-01. 28th European Signal Processing Conference (EUSIPCO), ELECTR NETWORK, Jan 18-22, 2021. p. 1180-1184. DOI : 10.23919/Eusipco47968.2020.9287486.
Height And Weight Estimation From Unconstrained Images2020-01-01. IEEE International Conference on Acoustics, Speech, and Signal Processing, Barcelona, SPAIN, May 04-08, 2020. p. 2298-2302. DOI : 10.1109/ICASSP40776.2020.9053363.
Learning sparse models of diffusive graph signals2017. ESANN, April.
Learning time varying graphs2017. International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA. p. 2826-2830. DOI : 10.1109/ICASSP.2017.7952672.
Graph Transform Learning for Image Compression2016. Picture Coding Symposium (PCS), Nuremberg, Germany.
Distributed Signal Processing with Graph Spectral Dictionaries2015. Allerton Conference on Communication, Control, and Computing.
Graph-based motion estimation and compression for dynamic 3D point cloud compression2015. IEEE International Conference on Image Processing (ICIP), Quebec city, Canada, September, 2015. p. 3235-3239. DOI : 10.1109/ICIP.2015.7351401.
Polynomial Filter Design for Quantized Consensus2010. European Signal Processing Conference (EUSIPCO), Aalborg, Denmark, August 23-27, 2010.
Functionalized nanofiber-enhanced filter media for fine particles and heavy metals removal in flue gases2015
Progressive quantization in distributed average consensus2011
Methods and apparatuses for encoding and decoding digital images or video streamsUS11122298 ; CN110024391 ; EP3549344 ; US2020228840 ; EP3549344 ; CN110024391 ; WO2018100503 ; IT201600122898 . 2018.
Biofuels vs food2008
Teaching & PhD
Electrical and Electronics Engineering