Conference

Reproducing kernel Hilbert spaces (RKHSs): Motivation: supervised learning with linear rules in feature space.

Reproducing kernel Hilbert spaces (RKHSs): Finite dimensional RKHSs. General RKHSs and examples

RKHSs for machine learning: Supervised learning with RKHSs: uniform concentration, representer theorem, and universal consistency.

RKHSs for machine learning: Approximation based on random features. 

RKHSs for machine learning: Kernel mean embedding and maximum mean discrepancy. 

Santiago Mazuelas received the Ph.D. in Mathematics and Ph.D. in Telecommunications Engineering from the University of Valladolid, Spain, in 2009 and 2011, respectively. He is currently an Ikerbasque Research Associate at the Basque Center for Applied Mathematics (BCAM). Prior to joining BCAM, he was a Staff Engineer at Qualcomm Corporate Research and Development from 2014 to 2017. He previously worked from 2009 to 2014 as Postdoctoral Fellow and Associate in the Laboratory for Information and Decision Systems (LIDS) at the Massachusetts Institute of Technology (MIT). His general research interest is the application of mathematics to solve practical problems, currently his work is primarily focused on machine learning and statistical signal processing. He has received the Young Scientist Prize from the Union Radio-Scientifique Internationale (URSI) Symposium in 2007, and the Early Achievement Award from the IEEE ComSoc in 2018. His papers received the IEEE Communications Society Fred W. Ellersick Prize in 2012, the SEIO-FBBVA Best Applied Contribution in the Statistics Field in 2022, and Best Paper Awards from the IEEE ICC in 2013, the IEEE ICUWB in 2011, and the IEEE Globecom in 2011.