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Veit David Wild

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

Jake Fawkes

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

Supervisors

Prof Robin Evans

Prof Dino Sejdinovic

Carlo Alfano

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

Tyler Farghly

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

Anum Fatima

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

Contact Details

Office: 2.14

Pronouns: She/Her

Research Groups

Supervisor

Andrew Campbell

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

Supervisor

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