I am a DPhil student in the Department of Statistics at the University of Oxford, supervised by David Steinsaltz and Maria Christodoulou. In general, my research focuses on plant population dynamics in stochastic environments by incorporating Gaussian Process models. In particular, I am also interested in applying advanced simulation methods, like Particle filters for static or time-series models, and Bayesian methods, like Approximate Bayesian computation methods, to population modelling methods. Before joining Oxford, I completed my BSc in Statistics at University College London, where I created an R package with Paul Northrop for the following students in the department to study Markov chains. I then moved to Oxford to study for an MSc in Statistical Science and stayed on for the DPhil.

• Integral projection model
• Approximate Bayesian computation methods
• Gaussian process models
• Sequential Monte Carlo

Background in Mathematics particularly Probability Theory and Analysis in infinite dimensions.

Deep Learning, Kernel methods, Wasserstein gradient flow, Generalised Bayesian inference and most mathematical aspects related to deep learning and uncertainty quantification.

I am a third year DPhil student working under the supervision of Robin Evans and Dino Sejdinovic.

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.

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.

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.

I am a 3rd year Phd student keen to better understand the influence of varying population size on population genetics models! Most models in population genetics assume the same population density and interactions at all space-time positions. In contrast, inhomogeneous dynamics are present in ecological models, which in turn often neglect the development of the genetic information carried by the population. With my current projects, I hope to make a step towards weakening this separation. Apart from mathematics, I enjoy running and memory competitions. Feel free to get in touch!

• Stochastic Processes
• Population Genetics

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.

Stochastic optimization, MCMC, generalization bounds, Langevin dynamics

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.

• Probability Distributions
• Stein's Method
• Networks

I am a third year StatML student in the Department of Statistics at the University of Oxford.

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.