Hello! I'm Qinyu (pronounced chin-yu, though frequently mispronounced as Queen Yu), a DPhil student on the StatML CDT. Previously, I completed a BSc in Mathematics with Modern Languages at University College London, followed by an MSc in Statistics at Oxford. I worked at Imperial College London as a Research Assistant before returning to Oxford to start my DPhil. 

I have broad interests in Bayesian inference, causal inference, and deep learning. I'm particularly interested in statistical and deep learning applications in biomedical sciences. 

Before joining the program, I worked as a quantitative analyst at the Bank of America as part of the CIO Portfolio Management and Investment Analytics team in New York. I completed M.S. in Financial Engineering from Columbia University and B.S. in Mathematics from Seoul National University.

Intersection of Statistics, Machine Learning and Finance. 

I am a PhD student at University of Oxford in the Departement of Statistics. My project is supervised by François Caron and Benjamin Guedj. My research journey is supported by the Clarendon Scholarship.

I am currently a second-year DPhil student studying causal inference under the supervision of Professor Frank Windmeijer. At present, I am working on a project involving valid instrumental variable selection within the two-sample Mendelian Randomisation setting. 

Before starting my DPhil, I studied at the University of Edinburgh to complete an MSc in Statistics and Operational Research. During my undergraduate studies, I obtained a BSc in Physics from Imperial College London. 

• Causal Inference
• Quantitative Social Science 
• Policy Design and Evaluation 

Marcel is a first year student on StatML CDT.  He obtained his B.A. in Natural Sciences at University of Cambridge followed by Harvard University, where he was the Choate Memorial Fellow focusing on data science.

He is also founder of Nural Research, a group exploring AI and grand challenges through a weekly newsletter and research projects.

Causality and robust Bayesian methodology with a particular interest in marrying this to emerging generative architectures and RL. Application focuses are within the field of precision medicine and drug development.

I am a first year StatML student. Prior to this, I completed my Master's in Artificial Intelligence at Imperial College London where I focused on the use of variational inference for model selection in neural networks for my dissertation.

I am particularly interested in robust deep learning, robustness to distribution shift and data-efficient learning (e.g. semi-supervised learning, self-supervised learning).

I am a first year DPhil student working in the (very) broad field of mathematical modelling of infectious diseases. My project is focused on investigating the role that heterogeneity (in all its many forms) plays in the spread of infectious diseases and how we can better incorporate these properties into mathematical and statistical models to enhance evidenced-based disease mitigation policies.  I am especially interested in pursuing an approach that combines graph theory (specifically complex network theory) and contact tracing data. After completing my BA in Maths at the Courant Institute at NYU in 2020, I earned an MSc in Mathematical Modelling and Scientific Computing from the Mathematical Institute (2021) and another in Modelling for Global Health from the Nuffield Department of Medicine (2023), where my dissertation extended a methodological approach to estimating mosquito abundance using climate data.

• Behavioral epidemiology
• Complex network theory
• Heterogeneity in infectious disease models
• Statistical inference from contact tracing and mobility data
• Real-time analysis and nowcasting of infection dynamics

I am currently a Glasstone Fellow at the department of statistics. I was mainly educated in France and I have a mixed academic background in life science and mathematics. I first graduated from the École Normale Supérieure with a major in life science, after which I obtained a PhD in probability theory from Sorbonne Université. My advisors were Amaury Lambert and Emmanuel Schertzer, and I was based at the Center for Interdisciplinary Research in Biology at the Collège de France. Before joining the department, I also did a one-year postdoc at the UQÀM in Montréal on epidemic modeling.

My research lies at the interface of probability theory and population biology. I study probabilistic objects arising from biologically motivated questions

I am both interested in the mathematical structure of these objects and in their application to understanding the underlying biological phenomena. Areas of interest:

• branching processes
• exchangeable coalescents
• random trees / random metric spaces
• population genetic aspects of recombination
• genetics of range expansion
• age-structured models in epidemiology