Causal inference, explainable AI, deep probabilistic learning, information theory, Bayesian statistics.

I’m a Florence Nightingale Bicentennial Fellow at the Department of Statistics, University of Oxford. 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 Research 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. I have worked on Bayesian experimental design, causality and neural data compression. Nowadays I'm mostly interested in robust evaluations of language models ("LLM Evals") and uncertainty quantification for LLMs.

I occasionally blot at Probably Approximately Incorrect.

I am a doctoral student in the Modern Statistics and Statistical Machine Learning (StatML) CDT, interested in developing statistical and machine learning methods for population genetics leveraging ancient DNA data. I am particularly motivated by healthcare-related applications of Machine Learning and genetics. I have also worked on developing deep learning algorithms for disease diagnosis from medical imaging scans. Prior to my DPhil in Oxford, I completed a Bachelor’s in Mathematics and an MPhil in Epidemiology & Biostatistics (focused on breast cancer genetic epidemiology), both at the University of Cambridge.

Statistical and population genetics, ancient DNA, genealogical inference and its applications to the detection of association and natural selection, machine learning for healthcare, biomedical imaging

I am a DPhil student on the CDT Mathematics of Random Systems, a program that is jointly run by the University of Oxford and Imperial College London. My research is focused on continuous-time branching processes, and my expected date of completion is October 2024. Before starting my DPhil, I obtained a MMath in Mathematics at the University of Oxford in 2020.

• Branching processes
• Lévy processes
• Interval Partitions
• Diffusions
• Continuum random trees
• Scaling limits

My research focuses on statistical methods to analyse how biomarkers affect the risk of disease (joint modelling of longitudinal and time-to-event data).

I am developing a joint model to understand how Covid-19 antibodies affects the risk of infection after receiving a vaccine.

I graduated from a Masters in Mathematics and Statistics from the University of Oxford in 2020. I then worked as a Statistician on the Covid-19 vaccine trials at the Oxford Vaccine Group, before starting my DPhil in 2021.

• Joint modelling of longitudinal and time-to-event data
• Survival analysis
• Bayesian modelling
• Multiple imputation

I am interested in developing flexible joint models using which can scale to large datasets. I am also keen to learn more about competing risks, interval censoring, causal inference and spatial statistics.

Please get in touch if you're interested in collaborating, or just want to chat!

Phillips, D. J. and Christodoulou, M. D. and Feng, S. and Pollard, A. J. and Voysey, M. and Steinsaltz, D. Improved estimates of COVID-19 correlates of protection, antibody decay and vaccine efficacy waning: a joint modelling approach. medRxiv (2024).

I am a second-year DPhil student supervised by Prof. Robin Evans. My current project aims at developing a robust methodology to combine experimental and observational datasets for better causal inference. Before starting my DPhil, I spent five years working as a consulting actuary in Australia. I specialised in using data analytics and statistical modelling to help public sector clients make better decisions.

Causal Inference

I am a 2nd year DPhil student at the Department of Statistics, supervised by Alison Etheridge. My project is on superprocesses, which are measure valued diffusions that can be understood as models for large populations evolving in time and space. Mathematically, they are at the intersection of stochastic analysis, scaling limits of particle systems, and partial differential equations. I have undergraduate degrees in Mathematics, Computer Science, and Physics from the Karlsruhe Institute of Technology (2020), and a Masters in Mathematics from the University of Oxford (2021). My undergraduate theses were on Noise in the Anderson Model, and on Consistency of Gaussian process regression in metric spaces. My Master's thesis was on a novel proof for the dimension of level sets of superBrownian motion.

• Superprocesses
• Scaling limits of particle systems
• General probability theory and stochastic analysis

I am a PhD student in the Department of Statistics at the University of Oxford, supervised by Arnaud Doucet and George Deligiannidis. I am broadly interested in Applied Statistics and Machine Learning, both in theory and application. My current research lies in the field of Sequential Decision-Making, particularly Bandit Learning. The general idea behind my research is to apply ideas from optimization, robust statistics, fairness, and causal inference for better real-world decision-making. Prior to joining Oxford, I completed my Bachelor’s degree in Electrical Engineering with a double major in Computer Science at Sharif University of Technology, where I worked with Babak Khalaj on Causal Inference and its application in Computational Genomics. Also, I was a lead data scientist at CafeBazaar, an Iranian Android app-store with more than 40 million users, for almost two years.

• Sequential Decision-Making
• Game Theory
• Optimization
• Robust Statistics
• Conformal Prediction

I am currently pursuing a DPhil in Statistics. I am fortunate to be advised by Prof. Arnaud Doucet, Prof. Yee Whye Teh, Prof. George Deligiannidis, and Dr. Tom Rainforth. I am a recipient of the Clarendon Fund Scholarship. 

From 2018-21, I worked as an Applied Scientist II at AWS AI in Pasadena, CA, USA. From 2014-18, I did my B.Sc. in Computer Science with Honors (Turing Scholars Honors) and B.Sc. in Mathematics with Honors from the University of Texas at Austin, TX, USA, with Prof. Adam Klivans as my thesis advisor.

Statistical Machine Learning

I am a 3rd year DPhil student in the Department of Statistics specialising on probability. My research is focused on the theoretical analysis of stochastic models. I am especially interested in models that have both continuous and discrete aspects. Before coming to Oxford, I completed by Bachelor (2019) and Master (2021) degrees in Mathematics at LMU Munich. As an undergraduate student, I have spent time at the NUS Singapore and ENS Lyon. Besides mathematics, I enjoy doing lots of sports, currently mostly triathlon and rowing.

• Probability theory, stochastic processes
• Random trees, tree valued Markov chains
• Continuum random trees, scaling limits
• Interacting particle systems, branching processes
• Random walks in random environment