My research focuses on developing new techniques and algorithms for analyzing large-scale human genome dataset. I am primarily doing research at Oxford’s big data institute, which is the world’s largest health big institute for biomedical research, and I am working in collaboration with the tskit community, which is an international research community for population and statistical genomics. During my first year as a DPhil student in Oxford, I created a new Python software called tstrait, which can efficiently simulate quantitative traits based on a whole-genome data in the tree sequence data format at a much faster computational speed than traditional simulation algorithms. I am currently working on developing a new technique to protect people’s privacy in large-scale human genome research.

I completed my Bachelor’s degree in Mathematics-Statistics and Master’s degree in Statistics at Columbia University in 2022. I am currently part of Oxford University’s Student Union as a postgraduate academic representative of the Mathematical, Physical and Life Sciences (MPLS) division, and I am also serving as a representative of the statistics department in the Graduate Joint Consultative Forum and as a representative of the second year DPhil students in the Graduate Liaison Group.

- Genome-wide association study (GWAS)
- Population genetics
- Statistical genetics
- Ancestral recombination graph
- Genetic simulation
- Statistical computing
- Algorithm development

I am a first year DPhil student interested in applications of causal inference to the biomedical/clinical spaces and am supervised by Robin Evans. Before starting at Oxford, I worked in the pharmaceutical space and worked on developing ML methods to address problems in the clinical spaces. Even earlier to that, I completed degrees in Physical Natural Sciences and Computational Statistics/ML from the University of Cambridge and UCL, respectively.

I am interested in developing machine learning methods and specifically causal inference techniques to address challenges in clinical and medical spaces. Recent explosion of clinical data lead to a variety of attempts to draw meaningful treatment inferences from observational data. However, naive applications of non-causal methods can lead to biased findings, which could have critical impacts on patients.

My research seeks to address these challenges and learn unbiased effects from both observational and randomised trial data. Of particular interest is the challenging case when learning from data with unobserved confounders.

I am a 1st year DPhil student in the Oxford Protein Informatics Group (OPIG) in the Department of Statistics and I am interested in developing deep learning methods for computational drug development. Currently, the production of new drugs requires tedious wet lab experiments that are associated with high costs. Computational methods have the potential to greatly facilitate the drug discovery process. Specifically, my research focuses on antibodies, a class of proteins of great interest to the pharmaceutical industry, and I am working on improving methods for predicting antibody structures and flexibility from sequence data. Before starting my PhD at the Department of Statistics, I completed an undergraduate degree in Biochemistry at UCL (2020) and a Master's degree in Biochemistry at the University of Oxford (2022).

I am a DPhil student in Computational Discovery under the supervision of Mihai Cucuringu and Xiaowen Dong. Prior to that, I received my MASc in Electrical and Computer Engineering from the University of British Columbia in 2022, and BSc in Physics from Nankai University in 2019.

My research interest lies in the intersection of statistics and computation. In particular, I seek to better understand computational tasks on graphs using tools such as the spectral method, high-dimensional statistics, optimization, ML, etc. I am excited to see the transformation of ideas across different research fields and am motivated to prove things built on interesting intuitions.