I am currently working as a postdoc at the University of Oxford with Prof. dr. Judith Rousseau.
I am broadly interested in analyzing statistical and machine learning methods from a theoretical perspective. In particular, I study asymptotic properties of Bayesian procedures; relational data, such as networks and graphs; and performance guarantees for machine learning algorithms. I specialize in high-dimensional and nonparametric methods.
My work explores limitations of estimation methods and shows how to tune them in order to achieve optimal performance. Topics include point processes, frequentist analysis of Bayesian methods, minimax rates, generalized Bayes and Gibbs posteriors, small ball probabilities, and inference on large graphs.
Office: 1.11, 29 St Giles’
Oxford, OX1, UK
Email: alisa (dot) kirichenko (at) stats (dot) ox (dot) ac (dot) uk
Machine Learning, Nonparametric Bayesian Analysis, Asymptotic Statistics, Networks, Inference on Graphs.