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Alexander is a Postdoctoral Research Associate in the Biostatistics Unit of the Medical Research Council at the University of Cambridge, working with Sylvia Richardson. He completed a PhD in applied mathematics at the Center for research in economics and statistics (CREST), Paris supervised by Nicolas Chopin. The topic was on high dimensional Bayesian computation, with a focus on improving Monte Carlo simulations for models with numerous parameters to be inferred. He did his graduate studies in economics, mathematics and statistics under the double degree program of Humboldt University Berlin and ENSAE ParisTech. His research has applications in statistics and machine learning. His research interest lies in Monte Carlo methods and approximate inference in general and in particular Hamiltonian Monte Carlo, sequential Monte Carlo, quasi Monte Carlo, approximate Bayesian computation and variational inference. As a postdoc he studies computational methods for Bayesian inference in genomics.
- Buchholz A, Chopin N, Jacob PE(2018). Adaptive Tuning Of Hamiltonian Monte Carlo Within Sequential Monte Carlo .
- Buchholz A, (2018). Thesis: High dimensional Bayesian Computation. University Paris Saclay.
- Buchholz* A, Wenzel* F, Mandt S, (2018). Quasi-Monte Carlo Variational Inference. ICML 2018. *(equal contributions)
- Buchholz A, Chopin N (2018). Improving approximate Bayesian computation via quasi Monte Carlo. Journal of Computational and Graphical Statistics.