I am currently a Humboldt research postdoctoral fellowship holder. My host institution is the Max Planck Institute for Software Systems and I work with Manuel Gomez-Rodriguez.
Prior to this 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, and I had the opportunity to work with Krishna Gummadi during my internship at the MPI-SWS and with Prof. Ghahramani during my stay in Cambridge University.
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.