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 CDT programme at the University of Oxford, working with Tom Rainforth and Yee Whye Teh. During my PhD I did two research internships: one at Microsoft Reseach Cambridge, where I focused on causal machine learning and one at Meta AI (FAIR Labs) NYC, where I worked on neural data compression. I am expected to complete my PhD in early 2025.

Before joining the StatML CDT in late 2020, 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.

For more about my work, see my talks and publications.

  • 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