José Miguel Hernández Lobato

Since Sep 2016, I am a University Lecturer (equivalent to US Assistant Professor) in Machine Learning at the Department of Engineering in the University of Cambridge, UK. I was before a postdoctoral fellow in the Harvard Intelligent Probabilistic Systems group at the School of Engineering and Applied Sciencies of Harvard University, working with the group leader Prof. Ryan Adams. This position was funded through a post-doctoral fellowship given by the Rafael del Pino Foundation. Before that, I was a postdoctoral research associate in the Machine Learning Group at the Department of Engineering in the University of Cambridge (UK) from June 2011 to August 2014, working with Prof. Zoubin Ghahramani. During my first two years in Cambridge I worked in a collaboration project with the Indian multinational company Infosys Technologies. I also spent two weeks giving lectures on Bayesian Machine Learning at Charles University in Prague (Czech Republic). From December 2010 to May 2011, I was a teaching assistant at the Computer Science Department in Universidad Autónoma de Madrid (Spain), where I completed my Ph.D. and M.Phil. in Computer Science in December 2010 and June 2007, respectively. I also obtained a B.Sc. in Computer Science from this institution in June 2004, with a special prize to the best academic record on graduation. My research revolves around model based machine learning with a focus on probabilistic learning techniques and with a particular interest on Bayesian optimization, matrix factorization methods, copulas, Gaussian processes and sparse linear models. A general feature of my work is also an emphasis on fast methods for approximate Bayesian inference that scale to large datasets. The results of my research have been published at top machine learning journals (Journal of Machine Learning Research) and conferences (NIPS and ICML).