Desi R. Ivanova

Desi R. Ivanova

Bayesian Inference, Statistical Machine Learning, Optimal Experimental Design

I am a second year StatML graduate student at the University of Oxford supervised by Tom Rainforth and Yee Whye Teh. I’m currently working on Bayesian Optimal Experimental Design (BOED) and I’m more broadly interested in probabilistic machine learning.

Before starting my PhD, I spent 4 years working in quant research/structuring in the City of London. Prior to that I studied MMORSE at the University of Warwick.



  • T. Rainforth , A. Foster , D. R. Ivanova , F. Bickford Smith , Modern Bayesian experimental design, Statistical Science (to appear), 2023.


  • A. Foster , D. R. Ivanova , I. Malik , T. Rainforth , Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design, International Conference on Machine Learning (ICML, long presentation), 2021.
  • D. R. Ivanova , A. Foster , S. Kleinegesse , M. U. Gutmann , T. Rainforth , Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods, 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021.