Desi R. Ivanova

Desi R. Ivanova

DPhil Student in Statistics

University of Oxford

I’m interested in machine learning and statistics more broadly, particularly Bayesian. Currently, I’m on the StatML programme at the University of Oxford, working with Tom Rainforth and Yee Whye Teh.

Before joining the StatML CDT, I spent four years in quant finance – first in quantitative equity research at UBS and later in cross-asset systematic trading strategies structuring at Goldman Sachs.

I’m passionate about the rigorous use of statistics and machine learning in applied settings. Specifically, I firmly believe it is crucial to 1) incorporate domain knowledge when training data-driven models; 2) thoroughly evaluate these models against suitable baselines; and 3) properly interpret the results of statistical analyses.

For more about my work, see my talks and publications. I’m very happy to collaborate and/or give a talk.

  • Probabilistic machine learning, deep generative models
  • Information theory, optimal experimental design, neural compression
  • Statistics and data science
  • Sports
  • DPhil Statistics (StatML programme), 2020-2024

    Univeristy of Oxford

  • Master of Mathematics, Operational Research, Statistics and Economics (MMORSE), 2011-2016

    University of Warwick

  • Mathematics (Erasmus programme), 2014-2015

    LMU Munich

Upcoming Talks

Aalto Seminar on Advances in Probabilistic Machine Learning