Welcome to my webpage. I am postdoctoral researcher in the Wellcome Trust Centre for Human Genetics.

Research interests

• Applied statistics and Machine learning methods: Gaussian processes, Bayesian non-parametric models, probabilistic graphical models, kernel methods
• Approximate inference: variational methods, Markov chain Monte Carlo
• Dynamical systems: nonlinear models, stochastic differential equations
• Applications: computational systems biology and statistical genetics.

• M. K. Titsias and M. Lázaro-Gredilla.
  Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning. To appear in NIPS, 2012. [supplementary, code].
  
• A. C. Damianou, M. K. Titsias and N. D. Lawrence.
  Variational Gaussian Process Dynamical Systems. To appear in NIPS, 2012.
  
• M. Lázaro-Gredilla and M. K. Titsias.
  Variational Heteroscedastic Gaussian Process Regression. International Conference on Machine Learning (ICML), 2011, Distinguished Paper Award.
  
• M. K. Titsias.
  Discussion on the paper: Riemann manifold Langevin and Hamiltonian Monte Carlo methods, by Girolami and Calderhead. Journal of the Royal Statistical Society, Series B (Statistical Methodology), 73(2):201, 2011.
  
• M. K. Titsias and N. D. Lawrence.
  Bayesian Gaussian Process Latent Variable Model. Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR: W&CP 9, pp. 844-851, 2010.
  

• Bayesian Gaussian Process Latent Variable Model, AISTATS (2010). [slides]
• Variational Model Selection for Sparse Gaussian Process Regression, BARK (2008). [slides]
• Markov Chain Monte Carlo Algorithms for Gaussian Processes, Newton Institute workshop (2008) [slides]