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My main field of research is statistical machine learning. I am interested in the mathematical side of the subject as well as in the applications to statistical Natural Language Processing (NLP) and computational biology.
I am currently working on high-resolution computational models for evolutionary processes, which can be used to automatically reconstruct proto-languages or to align proteins. This work has the potential to bring methods from computer science to bear on significant problems in both historical linguistics and biology.
Other current or recent research projects include: MCMC, variational inference and using non-parametric Bayesian statistics to train machine translation models.
In the past, I also did some work on logical characterization and approximation of labeled Markov processes and on reinforcement learning.
Other topics of interest include: probability theory, design and analysis of randomized algorithms.
Variational Inference over Combinatorial Spaces
as author at Video Journal of Machine Learning Abstracts - Volume 1,