|Jose Miguel Hernandez Lobato is a lecturer in Machine Learning at the Department of Engineering of the University of Cambridge. Before that, he was a postdoctoral fellow in the Harvard School of Applied Sciences and Engineering from Sept. 2014 to Sept. 2016. His research interests are in Bayesian optimization, scalable methods for approximate inference and flexible probabilistic modelling of data. Jose Miguel's research is driven by applications of machine learning to expensive optimal design problems in engineering. Before joining Harvard, Jose Miguel was a postdoctoral research associate at the Department of Engineering of the University of Cambridge were he worked in a collaboration project with the Indian multinational company Infosys Technologies. From December 2010 to May 2011, Jose Miguel was a teaching assistant at the Computer Science Department in Universidad Autonoma de Madrid (Spain), where he obtained his Ph.D. and M.Phil. in Computer Science in December 2010 and June 2007, respectively. Jose Miguel 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.|
Part of José's research is in the application of machine learning to the efficient solution of expensive optimisation problems. For example, in optimal design in engineering, where the goal is to obtain better and more effective products. In many of these design problems the analytic form of the objective function is unknown and its evaluations are very expensive. Bayesian optimisation (BO) methods can reduce the number of evaluations required to solve the aforementioned problems. In his research José aims at designing novel methods for Bayesian optimisation that will accelerate optimal design problems across a wide range of engineering areas.