I’m a Florence Nightingale Bicentennial Fellow at the Department of Statistics, University of Oxford (faculty fellowship). Prior to that I was a graduate student on the StatML CDT programme at the University of Oxford, working with Tom Rainforth and Yee Whye Teh.

During my PhD I’ve interned as a Research Scientist at Microsoft Reseach Cambridge, where I focused on causal machine learning, and at Meta AI (FAIR Labs) NYC, where I worked on neural data compression. Before StatML, 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 broadly interested in probabilistic machine learning, with a focus on robustness and uncertainty quantification. Currently, I’m thinking a lot about rigorous evaluation of LLMs and “foundation models” more generally. For more about my work, see my talks, publications and blog posts.

Mentorship: Many women in machine learning (and technical fields in general) face unique challenges, and the scarcity of female mentors and role models makes it worse. Whether you’re transitioning from industry, considering graduate studies, or navigating an academic career in ML, connecting with someone who has walked a similar path can be super valuable. If you are a woman and are interested in scheduling an informal mentoring chat (online) please get in touch via this form.