About me

I am currently a postdoctoral researcher at the Department of Engineering of the University of Cambridge under the supervision of Prof. Ghaharamani.

Prior to this I was a Humboldt post-doctoral fellowship holder at Max Planck Institute for Software Systems, where I work with Manuel Gomez Rodriguez. I obtained my PhD in 2014 and my Master degree in 2012 from the University Carlos III in Madrid. During my PhD I worked under the supervision of Fernando Perez-Cruz.

Research interest

My research turns around the development of general machine learning methods that leverage the availability of data to solve real-world problems. In particular, I have three main goals in my research: i) capturing complex real-world phenomena, which often involve being able to ii) handle time-dependent, unstructured and heterogeneous data, and iii) provide interpretable results that allow us to better understand these phenomena, i.e., to include humans in the loop.

I have extensive expertise in probabilistic modeling, including Bayesian nonparametric models, as well as discrete- and continuous-time models such as HMMs and temporal point processes, respectively. My learning approaches include approximate Bayesian inference, e.g., Monte Carlo and variational methods, as well as convex optimization. I am interested in diverse applications which range from bioengineering and psychiatry to social and communication systems.