I am particularly interested in causal inference, uncertainty quantification, and enhancing the robustness of machine learning methods, especially for policy learning, evaluation, and healthcare applications. Before joining the StatML CDT, I worked as an AI Applied Scientist at Microsoft New England Research Center, where I gained two years of experience addressing real-world challenges. I hold a master's degree in Computational and Mathematical Engineering from Stanford University. My passion for causal inference drives my commitment to developing statistical and machine learning solutions that are both rigorous and applicable in critical domains like healthcare.

• Causal Inference
• Robust Statistics
• Uncertainty Quantification

I am a second year DPhil student under the supervision of Professor Christina Goldschmidt. I previously completed my BSc in Honours Mathematics, and MSc in Mathematics and Statistics, at McGill University.

Topics in random graphs and trees, mixing times of Markov chains, and interacting particle systems.

I am half English (Wessex) and half Italian (Sicily) and to make things more confusing I have lived in a few other places: New Hampshire, New Zealand (Dunedin, close to Rohan and Lothlórien) and Copenhagen. I collect hobbies of the nerdier kind: everything for me seems to turn into an intriguing experiment and in my free time, I sometimes code for fun, rewire the electrics in the house, 3D print something utterly useless and cursed or solder components for an equally useless and janky Raspberry-Pi–based sensor.

I am a computational biochemist working in drug design.

My loyalties lie with molecular thermodynamics, but I am starting to fall the allure of deep learning.

I am currently working on Fragmenstein, github.com/matteoferla/Fragmenstein, a tool that stitches molecules together and reanimates them in order to help assess follow up compounds in fragment-based drug discovery.

Previously, I have developed Michalenglo (michelanglo.sgc.ox.ac.uk/), a tool allowing the sharing of representations of 3D structures of protein with interactive annotations, and Venus (venus.cmd.ox.ac.uk/venus), a tool that combines different sources of information in order to determine what could be the effect of a missense variant beyond simple destabilisation.

For other projects of mine, visit my github: github.com/matteoferla

Additionally, I have a science blog (blog.matteoferla.com) ranging from technical tutorials to monographic discussions/rants.

Jeffrey is from Hong Kong and studied at Pui Ching. He spent his undergraduate years at the University of Waterloo in Canada, where he majored in pure mathematics, statistics and combinatorics & optimisation. Jeffrey also studied at the University of Warwick for a year. Before his DPhil, he obtained his master’s degree in mathematical sciences (OMMS) from Balliol College, Oxford. As a Christian, he follows Christ the Lord as his light and his salvation. In his spare time, Jeffrey enjoys reading novels and collecting small trinkets of sentimental value.

• Causal inference
• Instrumental variables estimation
• Mendelian randomisation

I am a DPhil student in Statistics at St Anne's College under the supervision of Mihai Cucuringu and Álvaro Cartea. I am physically based in the Oxford-Man Institute most of the time. Prior to that, I completed my Undergraduate and Masters degree in Mathematics and Statistics at Mansfield College, University of Oxford in 2022.

• Network Time Series
• Machine Learning
• Financial Time Series

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