The Corcoran Memorial lectures are named in memory of Stephen Corcoran who was a graduate student in the Department of Statistics until his death in 1996. Stephen was a student of Wadham College, Oxford and graduated First Class Honours in Mathematics in 1991. He subsequently gained a Diploma in Mathematical Statistics from Cambridge University before returning to Oxford to study for a DPhil in Statistics.
Stephen's research was in the field of empirical likelihood. He made substantial progress in this work but sadly his thesis remained unfinished at the time of his death from cancer. Part of Stephen's uncompleted thesis was edited by Professor A. C. Davison and published in Biometrika (1998, pages 967-972).
A family bequest has established an annual lecture in honour of Stephen in which distinguished guest lecturers are invited to deliver a lecture on important aspects of their work. In addition, the Corcoran Memorial Prize is awarded every two years to students of the Department of Statistics for outstanding graduate work. The prizewinners are also invited to give a lecture.
Corcoran Memorial Prize Awards
- 2024 - Dr Alissa Hummer
- 2022 - Dr Adam Foster
- 2020 - Dr Chris J. Maddison
- 2018 - Dr Sarah Penington
- 2016 - Dr Fiona Skerman
- 2014 - Dr Therese Graversen
- 2012 - Dr Robin Ryder
- 2010 - Dr Chris Yau
- 2008 - Dr Ludger Evers & Dr Chris Spender (joint winners)
- 2006 - Dr Simon Myers
- 2004 - Dr Anja Sturm
- 2002 - Dr Yih-Choung Teh
- 2000 - Dr Matthew Stephens
- 1998 - Dr Mark Mathieson
Past Corcoran Memorial Lectures
2026
Thursday 11th June
Speaker: Professor Dr Frank Noe, Freie Universität Berlin/Microsoft Research AI for Science (Berlin)
2024
Monday 29th January
Speaker: Professor Michael Gutmann, Edinburgh (29th January 2024)
Title: 'Self-supervised learning for Bayesian experimental design'
2022
Thursday 1st December
Speaker: Dr Ruth Keogh, London School of Hygiene & Tropical Medicine (1st December 2022)
Title: ‘From population to person: Counterfactual risk prediction’
2021
Friday 3rd December
Speaker: David Silver, DeepMind and UCL (3rd December 2021)
2020
Thursday 21st January 2021
Speaker: Professor Kerrie Mengersen, Queensland University (21st January 2021 – Online)
Title: ‘(Not) Aggregating Data’
2019
Friday 31st January 2020
Speaker: Professor Frank den Hollander, University of Leiden, Netherlands
Title: ‘Synchronisation with noise’
2017
Monday 27th November
Speaker: Professor Steffen Lauritzen, Department of Mathematical Sciences, University of Copenhagen
Title: ‘Maximum likelihood estimation in Gaussian models under total positivity’
2015
Professor Arthur Gretton, Gatsby Computational Neuroscience Unit, University College London
‘Kernel Embeddings of Probabilities: Applications in Hypothesis Testing and Inference’
2013
Professor Nils Lid Hjort (Department of Mathematics, University of Oslo)
‘Distributions of Confidence’
2007
Professor David Spiegelhalter FRS, MRC Biostatistics Unit, University of Cambridge
'Bayesian evidence synthesis 1': Meta-analysis allowing for the rigour and relevance of studies' and 'Bayesian evidence synthesis 2': Evaluating the introduction of a high-risk operation for congenital heart disease'
2003
Professor Terry Speed, The Walter & Eliza Hall Institute of Medical Research, Melbourne & Department of Statistics, University of California, Berkeley
'Measuring Gene Expression: Why Biologists Do, and Why Statisticians should show an interest'
2002
Professor Anthony Davison, Swiss Federal Institute of Technology, Lausanne
`Galaxies, ticks and stock market crashes: hard times for the Poisson process'
2001
Professor Peter Hall, Australian National University
`Nonparametric inference under constraints'
2000
Professor Bernard Silverman, University of Bristol
`Using wavelet methods to fit models for time-frequency dependence'
1999
Professor Peter McCullagh, University of Chicago
`Re-sampling and exchangeable arrays' `Linear models and representation theory'
1998
Professor Adrian Smith, Imperial College, London
`Bayesian curves, CARTS and MARS'
1997
Professor Don Rubin, Harvard University
`Techniques for Drawing Causal Inferences from Imperfect Studies'