Dr. Marco Signoretto | OPTEC - K.U.Leuven CoE Optimization in Engineering

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Dr. Marco Signoretto (ESAT)

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Data-driven modelling (WG2)

My research interests deal with kernel methods especially in connection to tensors, modeling based on (convex) optimization, structure-inducing estimators and sparsity, multivariate time-series analysis, manifold learning, networks and complex systems.

you can find my updated CV here

Biography

Marco Signoretto graduated in Electronic Engineering (“Laurea Magistralis”) from the University of Padova in 2005. He obtained a M.Sc. degree in “Methods for Management of Complex Systems” from the University of Pavia and a Ph.D. degree in Electrical Engineering from the Katholieke Universiteit Leuven in 2006 and 2011, respectively. His current research goals are to further develop and analyze methodologies that combine tensor-based methods with kernels and convex optimization. Dr. Signoretto served as a co-organizer of the NIPS 2010 Workshop on Tensors, Kernels and Machine Learning.

IT (Articles in internationally reviewed academic journals)

Signoretto, M., Olivetti, E., De Lathauwer, L., Suykens, J. (2012). Classification of multichannel signals with cumulant-based kernels. IEEE Transactions on Signal Processing, 60 (5), 2304-2314.

Signoretto, M., De Lathauwer, L., Suykens, J. (2011). A kernel-based framework to tensorial data analysis. Neural Networks, 24 (8), 861-874.

Signoretto, M., Van de Plas, R., De Moor, B., Suykens, J. (2011). Tensor versus matrix completion : a comparison with application to spectral data. IEEE Signal Processing Letters, 18 (7), 403-406.

Daemen, A., Signoretto, M., Gevaert, O., Suykens, J., De Moor, B. (2010). Improved microarray-based decision support with graph encoded interactome data. PLoS One, 5 (4), 1-16.

IC (Papers at international scientific conferences and symposia, published in full in proceedings)

Signoretto, M., Suykens, J. (2012). Convex estimation of cointegrated VAR models by a nuclear norm penalty. Proc. of the 16th IFAC Symposium on System Identification (SYSID 2012). 16th IFAC Symposium on System Identification (SYSID 2012). Brussels, Belgium, 2012.

Geebelen, D., Batselier, K., Dreesen, P., Signoretto, M., Suykens, J., De Moor, B., Vandewalle, J. (2012). Joint regression and linear combination of time series for optimal prediction. Proc. of ESANN 2012. ESANN 2012. Brugge, Belgium, April, 2012.

Hunyadi, B., De Vos, M., Signoretto, M., Suykens, J., Van Paesschen, W., Van Huffel, S. (2011). Automatic seizure detection incorporating structural information. Proc.of the 21st International Conference on Artificial Neural Networks (LNCS 6791). ICANN 2011. Espoo, Finalnd, Jun. 2011 (pp. 233-240).

Ojeda, F., Signoretto, M., Van de Plas, R., Waelkens, E., De Moor, B., Suykens, J. (2010). Semi-supervised learning of sparse linear models in mass spectral imaging. Pattern Recognition in Bioinformatics, 5th IAPR International Conference PRIB 2010. Lecture Notes in Bioinformatics, Subseries in Lecture Notes in Computer Science: Vol. 6282. 5th IAPR conference on pattern recognition in bioinformatics (PRIB 2010). Nijmegen, The Netherlands, Sep. 2010 (pp. 325-334).

Signoretto, M., De Lathauwer, L., Suykens, J. (2010). Kernel-based learning from infinite dimensional 2-way tensors. In Diamantaras, K. (Ed.), Duch, W. (Ed.), Iliadis, L. (Ed.), Proc. of the 20th International Conference on Artificial Neural Networks (ICANN 2010), LNCS 6353. 20th International Conference on Artificial Neural Networks (ICANN 2010). Thessaloniki, Greece, Sep. 2010 (pp. 59-69) Springer.

Signoretto, M., Pelckmans, K., De Lathauwer, L., Suykens, J. (2010). Improved non-parametric sparse recovery with data matched penalties. Proc. of the 2nd International workshop on cognitive information processing (CIP). 2nd International workshop on cognitive information processing (CIP). Elba Island, Italy, apr. 2010.

Signoretto, M., De Lathauwer, L., Suykens, J. (2010). Convex multilinear estimation and operational representations. Proc. of NIPS Workshop : Tensors, kernels and machine learning (TKML). NIPS Workshop : Tensors, kernels and machine learning (TKML). Whistler, Canada, Dec. 2010.

Signoretto, M., Daemen, A., Savorgnan, C., Suykens, J. (2009). Variable selection and grouping with multiple graph priors. Proc. OPT 2009 : 2nd NIPS workshop on Optimization for Machine Learning. OPT 2009 : 2nd NIPS workshop on Optimization for Machine Learning. Whistler, Canada, Dec. 2009.

Signoretto, M., Pelckmans, K., Suykens, J. (2008). Quadratically constrained quadratic programming for subspace selection in kernel regression estimation. Proc. of the 18th International Conference on Artificial Neural Networks (ICANN). 18th International Conference on Artificial Neural Networks (ICANN). Prague, Czech Republic, Sep. 2008.

IMa (Meeting abstracts, presented at international scientific conferences and symposia, published or not published in proceedings or journals)

Gins, G., Signoretto, M., Suykens, J., Van Impe, J. (2011). Comparing Partial Least Squares with Nuclear Norm-Based Optimization for the prediction of batch-end quality. Benelux Meeting on Systems and Control. Lommel, Belgium, 15-17 March 2011, Abstract No. Book of Abstracts 30th Benelux Meeting on Systems and Control : 137.

This list is generated from Lirias and contains data from Lirias as it is entered and validated by the researcher.

Newsflash

Two OPTEC professors have been awarded three "Gouden Krijtjes", the yearly teaching awards given by the organization of engineering students (vtk). Prof. Lombaert was awarded the prize for the best course in civil engineering, and Prof. Diehl the prizes for the best professor and the best course in mathematical engineering (where he teaches numerical optimization). They received these awards at the yearly "proffentap" where experienced students taught them how to draft beer professionally.