I am a second-year DPhil student at Oxford and part of the StatML CDT. Previously, I completed a BSc in Computer Science and Statistics at the University of Toronto.

My research centers on generative modelling and sampling, with a focus on bridging various fields. I’m particularly interested in using techniques from differential geometry, optimal transport, probability, and statistical learning theory to deepen our understanding of the strengths and limitations of algorithms in these areas. I also have a broader interest in machine learning theory.

Hello! My name is Sam, and I am a first-year DPhil student in Statistics. I work in the Oxford Protein Informatics Group (OPIG), where I research machine learning applications for small molecule drug discovery. I am funded via an EPSRC CASE conversion studentship in collaboration with Lhasa Limited. I hold an MSc in Bioinformatics from Imperial College London and a BSc in Biochemistry from the University of Edinburgh.

• Structural bioinformatics.
• Machine learning for drug discovery.

I am a first-year Dphil student in Statistics supervised by Prof. Geoff Nicholls and Dr. Chieh-Hsi Wu (from University of Southampton). Before joining Oxford, I obtained my bachelor's degree in Statistical Science at the University of Hong Kong, Shenzhen , and during my third year of undergraduate study, I spent a wonderful time at Oxford as a visiting student. Nice to be back!

My current research interest focuses on Bayesian statistics, especially Bayesian model misspecification. On my way to explore new knowledge and extend my interest!

Bayesian statistics

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).