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 (DAMTP, Cambridge, UK), researches Bayesian statistical methodology and lectures in statistics in the Department. He teaches probability, statistics and applied mathematics in St Peter's college. Geoff joined the Statistics Department in 2005 from the Mathematics Department of the University of Auckland in New Zealand. He was Head of Department 2012-2015.  

• Applied Bayesian inference
• Bayesian statistical methods
• Computational statistics
• Monte Carlo and Variational methods

Geoff is working on Bayesian statistical inference for problems with computationally demanding prior and likelihood evaluations. Research is driven by problems presented by scientists and scholars working in a range of application areas, including Geoscience, Linguistics, History and Sociology, Genetics and Archaeology. Solving these problems motivates new statistical models, well adapted computational tools and novel statistical methodology.

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.

I am responsible for the administration of the MSc in Statistical Science, both on-course and admissions (for admissions queries, please e-mail graduate.admissions@stats.ox.ac.uk). I also assist with administration of the undergraduate Mathematics and Statistics course, and in particular organise the timetable and intercollegiate classes. I run the Statistics Library (for queries, please e-mail lib@stats.ox.ac.uk). I am a Local Canvas Co-ordinator (the Virtual Learning Environment) and run the Department SharePoint sites. I am also a harassment advisor, first aider and DSE assessor.

Contact me for:

Hardware support & monitoring (desktop, laptop, server, mobile devices)
Software support & licensing (Linux, MacOS, Windows Operating Systems)
Printing support
Anti-virus & security
Networking, wireless & telephony
Audio-visual support
 

Contact me for:

• Building access 
• General administration including building & facilities related issues/enquiries
• Ad hoc enquiries
• Mental Health First Aid
• First Aid
   

I have worked in the Department of Statistics since July 2008. I enjoy getting to meet and talk to lots of lovely people, so do feel free to pop by and say hello!