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

I am a PhD student majoring in Statistics and a member of the Oxford-Man Institute of Quantitative Finance at the University of Oxford, supervised by Professors Mihai Cucuringu, Alex Shestopaloff, and Stefan Zohren. My primary focus is using machine learning and data-driven methods for extracting information from noisy financial datasets. Prior to starting my DPhil programme, I completed a bachelor’s degree in Mathematics and Statistics and a master’s degree in Industry Engineering and Operational Research from the University of Toronto, which was supervised by Professors John Hull and Chi-guhn Lee. I also worked as a researcher at the Rotman Finhub, Department of Statistics, Department of Economics, and RiskLab, all at the University of Toronto. Moreover, I also secured a position as a researcher at the Fields Institute for Research in Mathematical Sciences and Centre de recherches mathématiques established at the University of Montreal. In addition to my academic background, I worked as an intern associate of the TD Securities Global Credit Trading Team. Besides this, I also worked as a quantitative analyst on the Manulife Models and Analytics team. That said, I am available for next summer and off-cycle internships within the financial industry.

• Quantitative finance
• Machine learning
• Data science
• Optimization
• High-dimensional statistics

I am a DPhil student in the Department of Statistics. I currently work on weak attacks in biological networks, using both theoretical methods and simulations to understand how weak attacks affect key statistics in random networks. Previously, I obtained my MPhil degree in Computational Biology from the University of Cambridge, and my bachelor’s degree in Mathematics with Statistics for Finance from Imperial College London.

• Networks
• Computational biology
• Statistical modelling
• Applied probability

Hello! I am Yunong Lin. Second year DPhil student from China.

My current research is on exploring shared and distinct genetic architectures of multiple related traits.

I am currently a DPhil student working on statistical theory under the supervision of Judith Rousseau. Before that, I graduated with a Bsc/Msc from ENS Rennes & Lyon (France) in mathematics, with a specialization in probability and statistics.

I am currently engaged in research focused on the theory and computation of Bayesian nonparametric methods applied to data generating distributions supported on non-Euclidean spaces, specifically graphs or manifolds. My primary interest lies in exploring the asymptotic frequentist properties of classical density/probability distribution estimation or nonparametric regression (particularly with regards to mixture models and Gaussian processes), using tools from functional analysis and spectral theory of graphs and manifolds.

I am DPhil Statistics student working on machine learning based drug development methods.

I am interested in machine learning based drug development methods. These can be used for screening and lead optimization.

Hello! I'm Lucy, a 2nd year DPhil student on the Computational Discovery CDT and working in OPIG. I'm currently working on fragment-based drug discovery. This consists of developing drugs for a given target protein beginning from fragments that are known to weakly bind to it. When elaborating on a fragment in such campaigns, information about known ligands and the protein pocket can both be leveraged to maximise the binding affinity of the end result. However, using information about known ligands has been demonstrated to bias the drug design process. In collaboration with IBM Research, I am investigating methods to extract information directly from protein pockets in a way that can be used in elaboration campaigns. Before this, I attended Durham University, where I did an integrated Masters degree in Physics.

Machine learning for drug design

Hi, I'm Lewis! My background is in Physics, but I have now pivoted from quarks to antibodies as a PhD student within the Oxford Protein Informatics Group (OPIG). I started my PhD in 2020 through the SABS CDT program and I am funded by BBSRC.

My research focuses on applying structure-based Deep Learning methods to predict antibody-antigen binding interfaces. The goal of such work is to reduce our reliance on traditional experimental techniques, thereby speeding up and reducing the cost of therapeutic antibody development.

I am a third-year DPhil student in the Genomic Medicine and Statistics programme run by the Wellcome Centre for Human Genetics. I am co-supervised by Cecilia Lindgren and Simon Myers. Prior to Oxford, I graduated from Brown University with a degree in applied mathematics. I also worked for two years in the Neale Lab at the Broad Institute of MIT and Harvard.

Complex disease, statistical genetics, next-generation sequencing, genome-wide association studies