Alessandro Rudi
Researcher
Computer Science Department
Ecole Normale Superieure (ENS)
France
Biography
Jan 2014 - current Post-doc, Massachusetts Institute of Technology - IIT, University of Genova, Italy. Fast and provably accurate large scale Statistical Machine Learning. May 2012 - Jan 2013 Visiting PhD, CBCL, MIT, Cambridge, MA. Statistical Machine Learning for big data. Jan 2011 - April 2014 PhD in Computer Science, Italian Institute of Technology, University of Genoa. Thesis: Learning Sets and Subspaces: a Spectral approach Large-scale statistical Machine Learning Jan 2010 - Jul 2010 Student Excellence Program, Sapienza University of Rome, Italy. Collaboration with ALCOR laboratory. Areas of specialization: Machine Learning, Computer Vision, Pattern Recognition Oct 2008 - Jul 2010 Master in Computer Science, Sapienza University of Rome, Italy, 110 cum laude/110. Machine Learning and Computer Vision Oct 2005 - Jul 2008 Bachelor Degree in Computer Science, Roma TRE University, Rome, Italy, 110 cum laude/110.
Research Interest
machine learning approximation techniques for large scale learning problems, advanced statistical machine learning, kernel methods, gaussian processes, spectral methods and inverse problems. computer vision structure from motion, stereo vision, multiple view geometry. mathematics numerical linear algebra, optimization, advanced probability and statistics, operator theory, functional analysis, spectral theory, harmonic analysis.
Publications
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An Approach to Projective Reconstruction from Multiple Views, A.Rudi, S. Fanello et al., In Proceedings of Signal Processing, Pattern Recognition and Applications Conference 2010, SPPRA 2010.
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A general method for the Point of Regard Estimation in 3D Space, F.Pirri, M.Pizzoli, A. Rudi, In IEEE Proceedings of Computer Vision and Pattern Recognition 2011, CVPR 2011.
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Book Chap. 2014 Learning Sets and Subspaces, A. Rudi, G.D. Canas, E. De Vito, L. Rosasco, Regularization, Optimization, Kernel Methods and Support Vector Machines, Chapman & Hall/CRC Machine Learning Series