Services

Design
Ideally, clients contact us right at the beginning of their study or project. At this stage, we can question and advise on the optimal research methods, measurement scales and indicator definitions, conduct sample size calculations, advise on sampling methods for primary data collection, and draft statistical analysis plans that link the study design to the appropriate analysis tools.

Data Collection
Our support to the data collection phase includes help with randomisation, questionnaire development, testing of data collection instruments, and quality assurance of data collection in case of primary data collection. We can also help identify and secondary data sources and review their quality.

Data management/wrangling
While we ask our clients for data to clearly documented and to have been “cleaned”, we will conduct data wrangling of primary or secondary data. Raw data rarely comes in a state that is ready for analysis, and usually has to be checked, structured, combined or transformed before one can proceed with further analysis.

Analysis, visualisation and communication
Typical analysis steps involve exploratory analysis to understand structures and relationships in the data, followed by hypothesis testing and causal or predictive modelling if the data, study design and the hypotheses have been set up accordingly. Statistics are only useful if they can be easily understood, and we place emphasis on using visualisations and clear non-jargon language when presenting the results.

Review/advice/quality assurance
Much of our work involves reviewing data, designs or analysis and providing feedback and suggestions for how to take your statistical work forwards. We also support with the monitoring, evaluation and learning in the context of policy programmes.
Training
We provide tailored training in the statistical methods you need, as well as their application in R.
Breadcrumb
DPhil in Statistics student
About Me
Background in Mathematics particularly Probability Theory and Analysis in infinite dimensions.
Research Interests
Deep Learning, Kernel methods, Wasserstein gradient flow, Generalised Bayesian inference and most mathematical aspects related to deep learning and uncertainty quantification.
Contact Details
Email: veit.wild@stats.ox.ac.uk
Office: G.02
Research Groups
Breadcrumb
DPhil in Statistics student
About Me
I am a third year DPhil student working under the supervision of Robin Evans and Dino Sejdinovic.
Research Interests
My research focuses on applying causal inference to improve machine learning methodology. I have mostly focused on using these methods to improve the fairness and explainability of machine learning methods.
Contact Details
Office: 1.07
Pronouns: He/Him
Research Groups
Supervisors
Breadcrumb
DPhil in Statistics student
About Me
I am a 3rd year DPhil Student under the supervision of Patrick Rebeschini and George Deligiannidis. Before Oxford, I was at "Sapienza" University of Rome, where I completed my BSc under the supervision of Enzo Orsingher.
Research Interests
My research focuses on theory for reinforcement learning. I am interested in building and analysing reinforcement learning algorithms using standard optimization tools, such as natural gradient descent and mirror descent. In particular, I am interested in settings where it is possible to take advantage of the structure of the environment, such as multi-agent settings or low-rank environments.
Contact Details
Email: carlo.alfano@stats.ox.ac.uk
Office: 1.17
Pronouns: He/Him
Supervisor
Breadcrumb
DPhil in Statistics student
About Me
I am a DPhil student at the University of Oxford supervised by Patrick Rebeschini and Arnaud Doucet. Before this, I studied Mathematics at Imperial College London, supervised by Grigorios A. Pavliotis. I’m interested in stochastic optimisation, MCMC and theoretical foundations for machine learning. Most recently, I have been interested in the use of noise for regularisation and the relationship between optimisation and sampling.
Research Interests
Stochastic optimization, MCMC, generalization bounds, Langevin dynamics
Contact Details
Email: farghly@stats.ox.ac.uk
Office: 1.19
Pronouns: He/Him
Research Groups
Supervisor
Breadcrumb
DPhil in Statistics student
About Me
I am a DPhil student at the department of statistics, University of Oxford. Currently, I am working on Stein's characterizations of distribution with particular emphasis on Bernoulli mixture graphs under the supervision of Professor Gesine Reinert. I am also a lecturer in statistics at the department of statistics, Lahore College for Women University, Lahore (2017 - Present; currently on study leave). I also worked as a lecturer in statistics at Queen Mary College, Lahore (2015 - 2017) and as a visiting teaching assistant at the department of statistics, Lahore College for Women University (2014 - 2015). In my MS in Statistics (2013) at Lahore College for Women University, Lahore I worked on Generalized Poisson-Exponential distribution; in my BS in Statistics (2011), I worked on Distributional properties of Generalized order statistics for Extended-Exponential distribution. I also worked on Extended Poisson exponential distribution and Transmuted exponentiated Pareto-I distribution.
Research Interests
- Probability Distributions
- Stein's Method
- Networks
Research Groups
Supervisor
Breadcrumb
StatML CDT student
About Me
I am a third year StatML student in the Department of Statistics at the University of Oxford.
Research Interests
My research interests include generative models, variational inference and MCMC. Recently, I have worked on denoising generative models for discrete data, viewing the process in continuous time and simulating with chemical physics inspired integrators. Previously, I have worked on online variational inference for sequential state space models by using links with reinforcement learning. I have also explored the links between variational inference and MCMC methods for more efficient sampling with applications to sampling molecular configurations from the Boltzmann distribution.
Contact Details
Email: campbell@stats.ox.ac.uk
Office: 1.17
Pronouns: He/Him
Research Groups
Supervisor
Breadcrumb
StatML CDT student
Research Interests
Causal inference, explainable AI, deep probabilistic learning, information theory, Bayesian statistics.
Contact Details
Email: oscar.clivio@stats.ox.ac.uk
Office: G.01
Research Groups