Department of Statistics

by Oxford University
0

The Department of Statistics at Oxford is a world leader in research including computational statistics and statistical methodology, applied probability, bioinformatics and mathematical genetics. In the 2014 Research Excellence Framework (REF), Oxford's Mathematical Sciences submission was ranked overall best in the UK.This is an exciting time for the Department. We have now moved into our new home on St Giles and we are currently settling in.The new building provides improved lecture and teaching space, a variety of interaction areas, and brings together researchers in Probability and Statistics. It has created a highly visible centre for the Department in Oxford.Since 2010, the Department has been awarded over forty research grants with a total value of £9M, not counting several very large EPSRC and MRC funded awards for Centres for doctoral training.The main sponsors are the European Commission, EPSRC, the Medical Research Council and the Wellcome Trust.We offer an undergraduate degree (BA or MMath) in Mathematics and Statistics, jointly with the Mathematical Institute. At postgraduate level there is an MSc course in Applied Statistics, as well as a lively and stimulating environment for postgraduate research (DPhil or MSc by Research). Our graduates are employed in a wide range of occupational sectors throughout the world, including the university sector.The Department co-hosts the EPSRC and MRC Centre for Doctoral Training (CDT) in Next-Generational Statistical Science- the Oxford-Warwick Statistics Programme OxWaSP.

More
Recent Episodes
  • A Theory of Weak-Supervision and Zero-Shot Learning
    Jun 9, 2022 – 01:03:33
  • Victims of Algorithmic Violence: An Introduction to AI Ethics and Human-AI Interaction
    Apr 6, 2022 – 50:33
  • The practicalities of academic research ethics - how to get things done
    Apr 5, 2022 – 52:45
  • Statistics, ethical and unethical: Some historical vignettes
    Apr 5, 2022 – 56:11
  • Joining Bayesian submodels with Markov melding
    Apr 5, 2022 – 55:11
  • Neural Networks and Deep Kernel Shaping
    Apr 5, 2022 – 55:17
  • Introduction to Advanced Research Computing at Oxford
    Apr 5, 2022 – 48:40
  • Ethics from the perspective of an applied statistician
    Mar 31, 2022 – 39:49
  • A Day in the Life of a Statistics Consultant
    Mar 31, 2022 – 40:19
  • Metropolis Adjusted Langevin Trajectories: a robust alternative to Hamiltonian Monte-Carlo
    Mar 31, 2022 – 56:00
  • Modelling infectious diseases: what can branching processes tell us?
    Mar 31, 2022 – 59:22
  • Causality and Autoencoders in the Light of Drug Repurposing for COVID-19
    Jul 29, 2021 – 58:58
  • Recent Applications of Stein's Method in Machine Learning
    Jul 29, 2021 – 56:43
  • Do Simpler Models Exist and How Can We Find Them?
    Jul 29, 2021 – 56:01
  • Practical pre-asymptotic diagnostic of Monte Carlo estimates in Bayesian inference and machine learning
    Jul 29, 2021 – 57:48
  • Complexity of local MCMC methods for high-dimensional model selection
    Jul 2, 2021 – 01:01:51
  • Assessing Personalization in Digital Health
    Jun 23, 2021 – 58:20
  • Machine Learning in Drug Discovery
    Jun 23, 2021 – 56:49
  • Several structured thresholding bandit problems
    Jun 23, 2021 – 57:14
  • A primer on PAC-Bayesian learning *followed by* News from the PAC-Bayes frontline
    May 28, 2021 – 59:06
  • Approximate Bayesian computation with surrogate posteriors
    May 21, 2021 – 56:42
  • Introduction to Bayesian inference for Differential Equation Models Using PINTS
    May 21, 2021 – 57:10
  • On classification with small Bayes error and the max-margin classifier
    May 21, 2021 – 01:00:01
  • Convergence of Online SGD under Infinite Noise Variance, and Non-convexity
    May 21, 2021 – 01:00:40
  • Distribution-dependent generalization bounds for noisy, iterative learning algorithms
    Mar 17, 2021 – 54:09
  • Finding Today’s Slaves: Lessons Learned From Over A Decade of Measurement in Modern Slavery
    Mar 1, 2021 – 56:43
  • Veridical Data Science for biomedical discovery: detecting epistatic interactions with epiTree
    Feb 26, 2021 – 01:01:58
  • (Not) Aggregating Data: The Corcoran Memorial Lecture
    Feb 5, 2021 – 01:01:47
  • Florence Nightingale Bicentennial Panel Session
    Feb 5, 2021 – 40:53
  • Florence Nightingale and the politicians’ pigeon holes: using data for the good of society
    Jan 7, 2021 – 39:15
  • Florence Nightingale and the politicians’ pigeon holes: using data for the good of society (Transcript)
    Jan 7, 2021 –
  • Probabilistic Inference and Learning with Stein’s Method
    Dec 4, 2020 – 49:02
  • Introduction to Deep Learning and Graph Neural Networks in Biomedicine
    Dec 3, 2020 – 52:41
  • Looking back on 4 years in data science
    Nov 28, 2020 – 45:58
  • Black History Month: Exploring the Data Visualizations of W.E.B. Du Bois
    Oct 23, 2020 – 34:27
  • The Science Media Centre and its work
    Jun 24, 2020 – 28:02
  • How To Set Up Continuous Integration to Make Your Code More Robust, More Maintainable, and Easier to Publish
    Jun 10, 2020 – 44:46
  • Developing better code with automated testing
    Jun 10, 2020 – 45:23
  • Cluster-Randomised Test Negative Designs: Inference and Application to Vector Trials to Eliminate Dengue
    Jun 10, 2020 – 01:02:53
  • MCMC for Hierachical Bayesian Models Using Non-reversible Langevin Methods
    Jun 10, 2020 – 01:04:36
  • Maths and Stats in Action – Real-time Analysis to Understand the Novel Coronavirus
    Mar 11, 2020 – 39:44
  • Bioinformatics at the heart of biology and genomics medicine
    Apr 27, 2016 – 49:21
Recent Reviews
  • La Piana
    Statistics
    Sorry, but I cannot even begin to read the chalkboards. Thank you so very much for the effort.
Similar Podcasts
Learning Applied Statistics
Statistics Made Simple
Disclaimer: The podcast and artwork on this page are property of the podcast owner, and not endorsed by UP.audio.