Research
publications
My current research focus lies in machine learning
(LCI).
I am especially interested in simulation based methods for solving
problems in Bayesian statistics.
Most recently my focus is in developing
Monte Carlo methods to sample from high-dimensional distributions
using a combination of MCMC and SMC
(see publications).
Such problems arise in models from a variety of disciplines,
e.g. physics, genetics, economy, etc.
In particular I am concentrating on solving general (nonlinear) state-space
models, both time discrete or continuous, with nonlinear noisy observations,
as well as parameter estimation in such models.
I'm also interested in applying Monte Carlo methods to problems in
Game Theory. For example computing Bayes-Nash equilibrium in Auction
games, as shown in my course
project for Machine Learning and Game Theory.
Past Research Interests
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