About

I am currently working as a Quantitative Researcher at Optiver. I am also an Associate Editor of Bayesian Analysis. Prior to this I was an Assistant Professor at the Department of Statistics, University of Warwick. Before that I spent a year as a postdoc of Judith Rousseau at the Department of Statistics, University of Oxford; and two years as a postdoc of Peter Grünwald in the Machine Learning group at CWI. I have obtained my PhD at the University of Amsterdam under the supervision of Harry van Zanten.

Research: 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: Optiver, Strawinskylaan 3095, 1077 ZX Amsterdam, the Netherlands

Email: alicekirichenko  (at) optiver (dot) com