Judith is an associate editor of the Annals of Statistics, Bernoulli, ANZJS and stat and is currently  the program secretary of IMS. She has also been active on various aspects of the ISBA society. She is an ISBA and an IMS fellow and has received the Ethel Newbold prize in 2015 and gave a medallion lecture in July 2017. Before coming to Oxford, she was a Professor at University Paris Dauphine.

• Bayesian Statistics
  • Default Bayesian analysis
  • Nonparametric Bayesian statistics
  • Bayesian testing
• Interaction between Bayesian and frequentist approaches
  • Frequentist properties of Bayesian methods
  • Asymptotic analysis
• Mixture distributions
• MCMC algorithms

Judith’s research interests range from theoretical aspects of Bayesian procedures, both parametric and nonparametric, to more methodological developments. From a theoretical perspective she is interested in the interface between Bayesian and frequentist approaches, looking at frequentist properties of Bayesian methods. From a more methodological perspective, she has worked on MCMC or related algorithms and on the elicitation of subjective priors.

I received my PhD in computer science from Columbia University in 2014. I then spent three and a half years working on statistical and population genetics as a postdoctoral fellow at the Harvard Chan School of Public Health, and at the Broad Institute of MIT and Harvard. Prior to that, I obtained a bachelor’s and a master’s degree from Rome’s Sapienza University, and a master’s degree from Columbia University, all in computer science with a focus on artificial intelligence, machine learning, and cognitive robotics.

My research is at the intersection of statistics, computer science, and genetics. I develop methods to enable new types of analyses in statistical and population genetics, with a particular interest in problems that involve modeling and inference in large datasets. Specific areas of research include studying evolutionary parameters in the human genome (natural selection, mutation/recombination rates), reconstructing past demographic events using genetic data (migration, expansion/contraction of populations), studying the heritability and genetic architecture of complex traits (nature vs nurture), and detecting disease-causing variation in the human genome.

Prior to joining Oxford, I was a Lecturer then Reader of Computational Statistics and Machine Learning at the Gatsby Neuroscience Unit, UCL from 2007 to 2012. I obtained my PhD in Computer Science at the University of Toronto in 2003. This was followed by two years as a postdoctoral fellow at University of California, Berkeley, then as Lee Kuan Yew Postdoctoral Fellow at the National University of Singapore.

My research interests lie in the general areas of machine learning, Bayesian statistics and computational statistics. Although my group works on a variety of topics ranging from theoretical, through to methodological and applications, I am personally particularly interested in three (overlapping) themes: Bayesian nonparametrics and probabilistic learning, large scale machine learning, and deep learning.

These themes are motivated by the phenomenal growth in the quantity, diversity and heterogeneity of data now available. The analysis of such data is crucial to opening doors to new scientific frontiers and future economic growth. In the longer term, the development of general methods that can deal with such data are important testing grounds for artificial general intelligence systems.

I moved to Oxford from Queen’s University in Kingston, Ontario, where I was Associate Professor in the Department of Mathematics and Statistics. Before then I was a postdoc at UC Berkeley for six and a half years, in the Departments of Demography and Statistics, following stints at the Technical University of Delft and the Technical University of Berlin. I completed my PhD in probability theory in the Harvard University Department of Mathematics in 1996, working with Persi Diaconis.

• Stochastic processes
• Random dynamical systems
• Biodemography
• Survival analysis
• Human sex ratio
• Markov chain Monte Carlo

I am currently interested primarily in biological and demographic questions connected with ageing and mortality. I have been working on improving the probability-theory machinery that underlies some theoretical analyses of the evolution of ageing, and developing statistical methods that help to bring together experiments with these theories. This has largely been in the area of survival analysis, but I have also increasingly been concerned with Bayesian methods for analysing longitudinal data. My demographic interests have branched out to include the human sex ratio and genetic determinants of human life history traits.

I continue to work on fundamental questions of stochastic processes, in particular the behaviour of stochastic flows, the asymptotics of killed Markov processes, and the growth rates of populations dynamics in random environments.

I have a Ph.D. in Operations Research and Financial Engineering from Princeton University (2014). After that, I joined the Yale Institute for Network Science at Yale University. I worked two years as a Postdoctoral Associate in the Electrical Engineering Department, and one year as an Associate Research Scientist with a joint appointment as a Lecturer in the Computer Science Department at Yale.

My research interests lie at the intersection of probability, statistics, and computer science. I am interested in the investigation of fundamental principles  in high-dimensional probability, statistics and optimisation to design computationally efficient and statistically optimal algorithms for machine learning.

Professor Geoff Nicholls, B.Sc. (Physics, Canterbury, New Zealand), MA, PhD (HEP, Cambridge, UK), teaches probability, statistics and applied mathematics. Geoff Nicholls joined the Statistics Department in 2005 from the Mathematics Department of the University of Auckland in New Zealand. Geoff took his BSc at the Physics Department of the University of Canterbury in Christchurch, New Zealand, and his PhD at Clare College, Cambridge, where he studied particle physics in the Department of Applied Mathematics and Theoretical Physics.

• Bayesian inference
• Statistical methods
• Computational statistics
• Monte Carlo, statistical senetics
• Applied statistics

Geoff is working on Monte-Carlo based Bayesian statistical inference for problems with computationally demanding prior and likelihood evaluations. Practical computational methods for making Bayesian model comparison for complex stochastic systems are needed. Research is driven by problems from a range of application areas, including Geoscience, Linguistics, Genetics and Archaeology.

My group’s research interests focus on the area of population genetics, specifically the use of stochastic models to understand patterns of variation in samples drawn from a population. Work is also ongoing on methods to map disease genes in admixed populations, and methods for fine-mapping association signals. I spend part of my time in the Department of Statistics, and part of my time at the Wellcome Trust Centre for Human Genetics (WTCHG).

For a number of years I have worked on studying patterns of recombination in different species, currently including humans, chimpanzees and mice, and this continues to be a strong theme of the group. I developed methods to detect such hotspots from genetic data, based on using the coalescent with recombination as a model for the population genealogy. The key achievements of this work have been in demonstrating that recombination occurs very unevenly throughout the human genome, with most recombination occurring in narrow hotspots in both sexes, and that most hotspots have a short lifespan and are not shared with chimpanzee. In the last three years, this work has further led directly on to the identification of the first sequence motifs that are associated with hotspot activity in humans, evidence that these same motifs mark sites of recurrent disease-causing genomic rearrangements in humans, and the identification of a gene, PRDM9, binding one of these motifs. Currently, my group is actively continuing this research, using both population genetics based and experimental approaches.

I am also working, in collaboration with researchers at the Broad Institute of MIT and Harvard as well as Oxford, on developing methods for association mapping of disease genes and for fine-mapping casual variants in ethnically diverse disease cohorts and in admixed populations. I am a member of the analysis groups of the Wellcome Trust Case-Control consortium (WTCCC+) and the 1000 genomes project. Finally, I am interested in developing and applying approaches to identify and characterise ancient admixture in populations of humans and other species, using modern genome-wide genetic variation data.

Professor Morris read Chemistry as an undergraduate and graduate at Oxford, completing his Part II (1987-88) and DPhil (1988-91) with Prof. W. Graham Richards in molecular modelling and graphical protein sequence analysis. In 1991 he moved to The Scripps Research Institute, California, developing the widely-cited protein-ligand docking software, AutoDock. He helped launch the first biomedical screensaver computing project, FightAIDS@Home, spawning other biomedical projects on IBM’s World Community Grid. He returned to the UK in 2008 to join the Oxford spinout InhibOx (now Oxford Drug Design), doing ‘real-world’ drug discovery and developing novel virtual screening methods, as well as spearheading the use of cloud computing in drug discovery. He moved on to become Head of Computational Chemistry at another Oxford spinout, Crysalin, developing novel protein engineering techniques for reliable protein crystallization.

Prof. Morris has co-organized the Royal Society of Chemistry’s "AI in Chemistry” Conference since 2022, and the MGMS-CSAT Joint Sheffield Cheminformatics Conference since 2019. He also founded Comp Chem Kitchen, hosting events highlighting best-practices in computational chemistry, cheminformatics and related fields.

Prof. Morris works closely with Prof. Deane in the Department of Statistics in the Oxford Protein Informatics Group (OPIG). He is also Programme Co-Director of the SABS R³ (Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research) Centre for Doctoral Training. In September 2019, he became Deputy Director of Graduate Studies. He is a Research Fellow of Green Templeton College, Oxford.

Former students have gone on to work in academia (Universities of Auckland; Malta; Oxford; and Stanford); pharma (AstraZeneca, Astex, Bayer, Chemify, and Isomorphic Labs); patent law (Carpmaels & Ransford); research software engineering (University of Oxford and Swiss National Supercomputing Centre); research funding (UKRO/UKRI); and venture capital (Redalpine).

My research is focused on the development of novel methods in computer-aided drug discovery, docking, virtual screening, cheminfomatics and bioinformatics. I am particularly interested in the intersection of physics, chemistry, and machine learning, including active learning, deep learning, Bayesian optimization, explainable AI, and generative AI.