Alessandro Rudi is Researcher at INRIA, Paris from 2017. He received his PhD in 2014 from the University of Genova, after being a visiting student at the Center for Biological and Computational Learning at Massachusetts Institute of Technology. Between 2014 and 2017 he has been a postdoctoral fellow at Laboratory of Computational and Statistical Learning at Italian Institute of Technology and University of Genova.
Topics of interest
Large scale machine learning, structured prediction
Project in Prairie
My main research interest is in machine learning. In particular my focus is on theoretical and algorithmic aspect of statistical machine learning, with the goal of devising algorithms that at the same time can (a) scale on big data (b) be easily applied in practice (c) have strong theoretical guarantees in terms of statistical and computational aspects (d) achieve state of the art error on the prediction, with reduced computational costs.
Large Scale ML with statistical guarantees. ML algorithms can be divided in (a) non-parametric, with strong theoretical guarantees, but high computational requirements especially in terms of memory footprint, as kernel methods (b) parametric, as deep nets, with small computational complexity and effective results in practice, but without theoretical guarantees. The goal of my work is to develop hybrid methods that take the best of both worlds: fast, effective and with theoretical guarantees. Structured Prediction. Nowadays data are very often more complex than vectors. In many fields learning problems consist in predicting structured/complex objects from other structured objects. Using the power of infinite-dimensional implicit embeddings, my goal in this direction consists in developing a unified theoretical and algorithmic framework able to deal effectively with a wide family of structured inputs and outputs.