Short CV in PDF - Max-Planck-Gesellschaft

Francesco Dinuzzo, Ph.D.
Max Planck Institute for Intelligent Systems
Department of Empirical Inference,
Spemannstrasse 38,
72076 Tübingen, Germany
Phone: +49 7071601569
Fax: +49 7071601552
Email: fdinuzzo@tuebingen.mpg.de
Personal
Born on July, 4, 1984.
Italian Citizen.
Education
01/2011 Ph.D. Degree in Mathematics and Statistics, University of Pavia, Italy.
10/2007 M.S. Degree (Diploma), Istituto Universitario di Studi Superiori, Pavia, Italy.
07/2007 M.S. Degree in Computer Science Engineering (110/110 et laude), University of Pavia, Italy.
09/2005 B.S. Degree (Laurea) in Computer Science Engineering (110/110 et laude), University of Pavia, Italy.
09/2002 Conservatory of Music Diploma (clarinet). Conservatory of Matera, Italy.
Research Experience
Since 04/2011 Research scientist. MPI for Intelligent Systems, Tübingen, Germany.
11/2010-03/2011 Visiting researcher. Institute of Statistical Mathematics, Tokyo, Japan.
11/2007-11/2010 PhD student. University of Pavia, Italy.
05/2010-10/2010 Academic guest. ETH (Machine Learning Laboratory), Zürich, Switzerland.
07/2009-09/2009 Visiting researcher. National Taiwan University, Taiwan, R.O.C.
01/2009-06/2009 Visiting student. Massachusetts Institute of Technology (CBCL) Cambridge, MA, USA.
Teaching
26-08-2013/6-09-2013 Lectures on Learning with Kernels (Machine Learning Summer School 2013, Tübingen,
Germany).
20-04-2012 Lecture on Optimization methods for large scale regularization problems, (Universidad Autónoma de
Madrid, Graduate School).
2008-2009 Assistant for Mathematical Methods for Engineering (Faculty of Engineering, Pavia).
2008-2009 Assistant for Calculus A (Faculty of Engineering, Pavia).
2007-2008 Assistant for System Identification and Data Analysis LS (Faculty of Engineering,Pavia).

Francesco Dinuzzo, Ph.D. 2
2004-2007 Mathematics for newly enrolled students course (Faculty of Engineering,Pavia).
2003-2006 Tutor for Linear Algebra and Analytic Geometry (Faculty of Engineering, Pavia).
Language Skills
Italian (native), English (fluent), German (intermediate), Mandarin chinese (basic), Japanese (basic).
Programming Experience
Java / Javascript, C/C++, HTML, PHP, Matlab
Awards and Grants
April, 2011 - December 2013 Max Planck Postdoc Grant
Max Planck Society (MPI for Intelligent Systems)
April, 2009 Recipient of the Scholarship
Summer program in Taiwan for Italian graduate students
April, 2008 Recipient of the Italia-MIT Fellowship, awarded by UMI (Unione Matematica Italiana)
Competitive Fellowship to support a visiting student position at Massachusetts Institute of Technology.
July 5, 2008 Best student award 2006/2007, Faculty of Engineering, University of Pavia.
Awarded to only one student among all Engineering graduates, based on grades.
October, 2007 PhD Scholarship, Faculty of Engineering, University of Pavia (declined).
Competitive scholarship to support a three year PhD student position in Computer Engineering.
October, 2007 PhD Scholarship, Faculty of Mathematics, University of Pavia.
Competitive scholarship to support a three year PhD student position in Mathematics and Statistics.
PC member / Reviewer
Journals: Journal of Machine Learning Research, Machine Learning, IEEE Transactions on Neural Networks,
IEEE Transactions on Automatic Control, IEEE Transactions on Pattern Analysis and Machine Intelligence, Neural
Networks, Automatica (journal of IFAC), International Journal of Control, International Journal of Adaptive Control
and Signal Processing, Journal of Circuits, Systems and Signal Processing, Data Mining and Knowledge Discovery,
ACM Transactions on Knowledge Discovery from Data,
Conferences and Workshops: American Control Conference (ACC), IFAC Symposium on System Identification
(SYSID), IEEE Conference on Decision and Control (CDC), International Joint Conference on Artificial Intelligence
(IJCAI), Neural Information Processing Systems (NIPS) conference, International Conference on Machine Learning
(ICML), Asian Conference on Machine Learning (ACML), International Joint Conference on Artificial Intelligence
(IJCAI), International workshop on Regularization, Optimization, Kernel methods and Support vector machines
(ROKS), NIPS Workshop on Optimization for Machine Learning, NIPS Workshop on Output Representation Learning.

Francesco Dinuzzo, Ph.D. 3
Organization
ICML 2013 Workshop on Machine Learning for System Identification with A. Boularias and L. Ljung
Special session on Machine Learning and System Identification within CDC 2013 with M. Sznaier
Selected Talks
09-07-2013 Dolomites Research Week on Approximation, Alba di Canazei, Italy.
09-07-2013 ROKS 2013 Workshop, Leuven, Belgium.
Output Kernel Learning Methods
16-10-2012 IBM Research, Dublin, Ireland.
Regularization techniques for structured machine learning problems
20-04-2012 Universidad Autonoma de Madrid, Spain.
Revealing structures in the output space with Output Kernel Learning
22-11-2011 Tokyo Institute of Technology, Japan.
Learning kernels for the output space.
12-11-2011 Asian Conference on Machine Learning, Taiwan (R.O.C.).
Learning low-rank output kernels.
01-07-2011 International Conference on Machine Learning, Bellevue, WA, USA.
Learning output kernels with block coordinate descent.
29-11-2010 Institute of Statistical Mathematics, Tokyo, Japan.
Kernel machines with two layers.
02-11-2010 Institute of Science and Technology Austria, Wien, Austria.
07-06-2010 ETH, Zürich, Switzerland.
17-12-2009 Conference on Decision and Control, Shanghai, China.
A second order sliding mode controller with polygonal constraints
28-09-2009 University of Siena, Italy.
Kernel machines con due strati
09-07-2009 National Taiwan University, Taiwan (R.O.C.).
Kernel machines with two layers and multiple kernel learning
18-02-2009 MIT, Cambridge, MA, USA.
Client-server multi-task learning.
26-06-2008 ICNPAA Conference, Genova, Italy.
A recursive Bayesian approach to multitask learning.
25-06-2008 ICNPAA Conference, Genova, Italy.
A class of HOSM algorithms with optimal finite-time reaching.
04-06-2008 MIT, Cambridge, MA, USA.
Quantitative representer theorems and degrees of freedom for non-smooth kernel methods.

Francesco Dinuzzo, Ph.D. 4
Publications
Articles in peer-reviewed journals
1. C. A. Gudín, F. Dinuzzo, S. Sra. Correlation matrix nearness and completion under observation
uncertainty, IMA Journal of Numerical Analysis, (in press) 2013.
2. S. Del Favero, D. Varagnolo, F. Dinuzzo, L. Schenato, and G. Pillonetto. Finding potential support
vectors in separable classification problems. IEEE Transactions on Neural Networks and Learning
Systems, 24(11):1799-1813, 2013.
3. F. Dinuzzo. Learning output kernels for multi-task problems, Neurocomputing, 118:119-126, 2013.
4. F. Dinuzzo. Analysis of fixed-point and coordinate descent algorithms for regularized kernel methods.
IEEE Transactions on Neural Networks, 22(10), 2011.
5. F. Dinuzzo, G. Pillonetto, and G. De Nicolao. Client-server multi-task learning from distributed
datasets. IEEE Transactions on Neural Networks, 22(2):290-303, 2011.
6. F. Dinuzzo. Learning functional dependencies with kernel methods. Scientifica Acta, 4(1):MS 16-25,
2010.
7. G. Pillonetto, F. Dinuzzo, and G. De Nicolao. Bayesian online multi-task learning of Gaussian
processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(2):193-205, 2010.
8. F. Dinuzzo and A. Ferrara. Higher order sliding modes controllers with optimal reaching. IEEE
Transactions on Automatic Control 54(9):2126-2136, 2009.
9. F. Dinuzzo and A. Ferrara. Finite-time output stabilization with second order sliding modes. Automatica,
45(9): 2169-2171, 2009.
10. F. Dinuzzo and G. De Nicolao. An algebraic characterization of the optimum of regularized kernel
methods. Machine Learning, 74(3): 315-345, 2009.
11. F. Dinuzzo, M. Neve, G. De Nicolao, and U. P. Gianazza. On the representer theorem and equivalent
degrees of freedom of SVR. Journal of Machine Learning Research, 8: 2467-2495, 2007.
Refereed international conference publications
1. F. Dinuzzo and B. Schölkopf. The representer theorem for Hilbert spaces: a necessary and sufficient
condition. In Proceedings of the Neural Information Processing Systems (NIPS) Conference, Lake Tahoe
NV (USA), 3-6 December 2012.
2. K. Muandet, K. Fukumizu, F. Dinuzzo, and B. Schölkopf. Learning from Distributions via Support
Measure Machines. In Proceedings of the Neural Information Processing Systems (NIPS) Conference, Lake
Tahoe NV (USA), 3-6 December 2012.
3. C. Persello, and F. Dinuzzo. Interactive domain adaptation technique for the classification of remote
sensing images. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium,
Munich, Germany, July 2012.
4. S. Del Favero, D. Varagnolo, F. Dinuzzo, L. Schenato, and G. Pillonetto. On the discardability of data
in support vector classification problems. In Proceedings of the IEEE Conference on Decision and Control
and European Control Conference, Orlando, FL (USA), December 2011.

Francesco Dinuzzo, Ph.D. 5
5. F. Dinuzzo and K. Fukumizu. Learning low rank output kernels. JMLR: Workshop and Conference
Proceedings. Proceedings of the Asian Conference on Machine Learning, 20:181–196, Taoyuan, Taiwan,
November 13-15, 2011.
6. F. Dinuzzo, C. S. Ong, P. V. Gehler, and G. Pillonetto. Learning output kernels with block coordinate
descent. In Proceedings of the 28th International Conference on Machine Learning, Bellevue, WA (USA),
June 2011.
7. F. Dinuzzo. A second order sliding mode controller with polygonal constraints. In Proceedings of the
IEEE Conference on Decision and Control, Shanghai (China), December 2009.
8. M. V. Basin, D. C. Alvarez, A. Ferrara, and F. Dinuzzo. Sliding mode optimal regulator for a Bolza-
Meyer criterion with non-quadratic state energy terms. In Proceedings of the American Control Conference,
St. Louis, MO (USA), June 2009.
9. G. Pillonetto, F. Dinuzzo, and G. De Nicolao. Bayesian online multi-task learning using regularization
networks. In Proceedings of the American Control Conference, Seattle, WA (USA), June 2008.
Book Chapters
F. Dinuzzo and G. De Nicolao. Some comments on ν-SVM in A tribute to Antonio Lepschy, G. Picci
and M. E. Valcher Eds. Ed. Libreria Progetto, 2007.
Reports
1. F. Dinuzzo. Learning Output Kernels for Multi-Task Problems. arXiv:1301.3816, 2013.
2. F. Dinuzzo and B. Schölkopf. The representer theorem for Hilbert spaces: a necessary and sufficient
condition. arXiv:1205.1928, 2012.
3. F. Dinuzzo. Kernels for linear time invariant system identification. arXiv:1203.4930, 2012.
4. K. Muandet, K. Fukumizu, F. Dinuzzo, and B. Schölkopf. Learning from Distributions via Support
Measure Machines. arXiv:1008.5090, 2012.
5. F. Dinuzzo. Fixed-point and coordinate descent algorithms for regularized kernel methods. arXiv:1008.5090,
2010.
6. F. Dinuzzo. Kernel machines with two layers and multiple kernel learning. arXiv:1001.2709, 2010.
7. F. Dinuzzo, G. Pillonetto, and G. De Nicolao. Client-server multi-task learning from distributed
datasets. arXiv:0812.4235, 2008.
Thesis
F. Dinuzzo. Learning functions with kernel methods. PhD Thesis in Mathematics and Statistics.
Advisor. G. De Nicolao. Università di Pavia, 2010.
F. Dinuzzo. Higher Order Sliding Modes design via optimal control arguments. Advisor: A. Ferrara.
Istituto Universitario di Studi Superiori (IUSS), 2007
F. Dinuzzo. Regularized Kernel Methods. Advisor. G. De Nicolao. Co-Advisor U.P. Gianazza. Università
di Pavia, 2007.