Massimiliano Pontil received an MSc degree in Physics from the University of Genova in 1994 (summa cum laude) and a PhD in Physics from the same University in 1999. His main research interests are in machine learning, function representation and approximation, numerical optimization, regularization methods, and statistical learning theory. He has made contributions in the areas including kernel methods, multitask and transfer learning, online learning, sparsity regularization, and statistical learning theory. He also studied machine learning applications arising in affective computing, bioinformatics, computer vision, and user modelling, among others. He has published over 30 international journals papers, 10 book chapters and 60 peer reviewed conference proceedings. His research papers have been cited approximately 12,000 times and his h-index is 47 (Google Scholar, March 2016). He was a full time member of academic staff at University College London (UCL) between 2003 and 2015, a Research Associate in the Department of Information Engineering at University of Siena (2001--2002) and a Post-doctoral Fellow at the Massachusetts Institute of Technology (1998--2000). He has been on the programme committee of the main machine learning conferences, including the Annual Conference on Learning Theory (COLT), the International Conference on Machine Learning (ICML), and the Annual Conference on Neural Information Processing Systems (NIPS). He is on the editorial board of the Machine Learning Journal, Statistics and Computing, the Journal of Machine Learning Research, and he is on the Scientific Advisory Board of the Max Planck Institute for Intelligent Systems.
IIT People Search
Computational Statistics and Machine Learning
Title: Ph.D. in Physics
Institute: University of Genoa
From: 1996 To: 1999
Cavallo A., Romeo L., Ansuini C., Battaglia F., Nobili L., Pontil M., Panzeri S., Becchio C.
Identifying the signature of prospective motor control in children with autism
Scientific Reports, vol. 11, (no. 1)
Modeling and Optimization in Science and Technologies, vol. 15
Editorial Book Series
Oneto L., Donini M., Pontil M., Maurer A.
Learning fair and transferable representations with theoretical guarantees
Proceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020, pp. 30-39
Conference Paper Conference
Colleagues of Computational Statistics and Machine Learning