Fields of expertise
My 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.
Honors and Awards
2) Top 10% Paper Award, IEEE International Conference in Image Processing (ICIP), September 2015
3) Best Paper Award, Picture Coding Symposium (PCS), December 2016
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.
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.
Parametric dictionary learning for graph signals2013. IEEE Global Conference on Signal and Information Processing (GlobalSIP), Austin, Texas, December, 2013.
Progressive quantization in distributed average consensus2012. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, March, 2012.
Compressed classification of observation sets with linear subspace embeddings2011. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, May 22-27, 2011. p. 1353-1356.
Comparison of time and frequency domain interpolation implementations for MB-OFDM UWB transmitters2010. 2010 IEEE International Symposium on Circuits and Systems (ISCAS 2010), Paris, France, May 30 -June 2 2010.
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 streamsCN110024391 ; EP3549344 ; US2020228840 ; EP3549344 ; CN110024391 ; WO2018100503 ; IT201600122898 . 2018.
Biofuels vs food2008
Teaching & PhD
Electrical and Electronics Engineering