About

About me

I am a Senior Researcher in Machine Learning at Microsoft Research Cambridge. Currently, I am primarily interested in (re)building statistical intution for modern ML methods and apparently elusive phenomena in deep learning – such as double descent and the surprisingly good performance of models that would appear to be heavily overfitted – as I strongly believe in the importance of demystification of the area. Most of my earlier research centered around how to best use ML to estimate personalized causal effects of treatments. Overall, I am broadly interested in many topics relating to causality, missing data, distribution shifts and (essentially) all of statistical machine learning. You can find a selection of my work under the Publications page or on my Google Scholar Profile.

My professional background

Albeit a ML researcher today, I am a statistician at heart – and most of my research therefore reflects my desire to connect ideas from (applied) statistics with machine learning. I hold a BSc in Econometrics and Operations Research (summa cum laude) and a BSc in Economics and Business Economics (summa cum laude) with specialisation in Policy Economics from the Erasmus University Rotterdam, and a MSc in Statistical Science (with distinction and prize for top performance in the cohort) from the University of Oxford. Between 2020 and 2024, I completed a PhD in Machine Learning at the University of Cambridge, advised by Prof. Mihaela van der Schaar. Before starting the PhD, I also briefly worked as a data scientist evaluating the effectiveness of marketing campaigns, and as a research intern at Pacmed, a Dutch start-up bringing ML solutions to clinical practice.

Me, personally

When not thinking about stats and ML, I like to stay active. I played waterpolo for 14 years (I earned half-blue status in both Oxford and Cambridge by representing both universities in some of the annual varsity matches) and – like every other Oxbridge student – became infatuated with rowing during my student time. One may sometimes find me away from water on a run or a hike. I am also always ready to binge-watch a good show and love good food & cooking.

News!

• Nov ‘24: The last paper of my PhD, on a simple model for understanding modern ML phenomena, was accepted to NeurIPS24. Find it here!
• Oct ‘24: Big life update – I’ve handed in my PhD thesis and joined Microsoft Research Cambridge as a Senior Researcher in Machine Learning!
• Sep ‘24: Wrote a little note on what I wish I knew as a Masters student to understand modern ML phenomena like double descent, read it here!
• Feb ‘24: New preprint ‘Why do Random Forests Work?’ (check it out!)
• Jan ‘24: Our paper ‘Cautionary Tales On Synthetic Controls in Survival Analyses’ was accepted as an Oral Presentation at the Conference on Causal Learning and Reasoning (CLeaR) happening in April 2024!
• Jan ‘24: Our review ‘Using Machine Learning to Individualize Treatment Effect Estimation: Challenges and Opportunities’ was accepted for publication in Clinical Pharmacology & Therapeutics, check it out here!
• Dec ‘23: I was interviewed on the ‘Causal Bandits’ Podcast, check it out here!
• Sep ‘23: Our paper ‘A U-turn on Double Descent’ (joint work with Alan Jeffares) has been accepted to NeurIPS23 as an Oral!!
• Aug ‘23: I will be interning with Javier González in the Health Futures team of Microsoft Research for the next 3 months (August-October)!
• Jul ‘23: I am going to Honolulu and I am bringing… three posters, on topics relating to treatment effect estimation, to ICML23!
• April ‘23: Find me in Valencia presenting a poster on heterogeneous treatment effect estimation in the presence of competing risks at AISTATS23!
• Dec ‘22: Check out our paper on Benchmarking Heterogeneous Treatment Effect Estimators through the Lens of Interpretability at NeurIPS22!
• Jul ‘22: I will be attending my first in-person conference, ICML22, in Baltimore – presenting work on Automated Imputation at the main conference, and work on Adaptive Clinical Trials at a workshop!
• Dec ‘21: 3 papers accepted at NeurIPS21 – including a spotlight on inductive biases in heterogeneous treatment effect estimation!
• April ‘21: I will be presenting the first paper of my PhD – on heterogeneous treatment effect estimation – at AISTATS21!
• Oct ‘20: I’ve started a PhD with Prof. Mihaela van der Schaar at the Department of Applied Mathematics at the University of Cambridge!