The Centre for Computational Statistics and Machine Learning (CSML) spans three departments at University College London, Computer Science, Statistical Science, and the Gatsby Computational Neuroscience Unit.
The Centre will pioneer an emerging field that brings together statistics, the recent extensive advances in theoretically well-founded machine learning, and links with a broad range of application areas drawn from across the college, including neuroscience, astrophysics, biological sciences, complexity science, etc. There is a deliberate intention to maintain and cultivate a plurality of approaches within the centre including Bayesian, frequentist, on-line, statistical, etc.
PI: M. De Iorio; CO-I A. Beskos, D. Balding, A. Jasra
The main objective is to develop and characterise principled approximations to complex statistical models.
The research work aims to reduce both the variance of simulation-based estimates, and the computational time of stochastic algorithms. This will facilitate the implementation of stochastic models in real biological applications. The grant will support a postdoctoral researcher for three years.
Funded by EPSRC for 3 years
ENGAGE: Interactive Machine Learning Accelerating Progress in Science, An Emerging Theme of ICT Research In short, it brings together Machine Learning, Computer Vision, Human-Computer Interaction, and Biodiversity Science to help cope with the global extinction crisis.
The PI's on the project are:
Gabriel Brostow - UCL CS
Mark Girolami - UCL Stats
Kate Jones - UCL Department of Genetics, Evolution and Environment Mike Terry - University of Waterloo, CS and ZSL Chair, Ecology and Biodiversity
Our vision is to establish and lead a new theme in ICT research based on Interactive Machine Learning (IML). Our expansion of IML will give scientists and non-ICT specialists unprecedented access to cutting-edge Machine Learning algorithms by providing a humancomputer interface by which they can directly interact with large scale data and computing resources in an intuitive visual environment. In addition, the outcome of this particular project will have a direct transformative impact on the sciences by making it possible for non-programming individuals (scientists), to create systems that semi-automatically detect objects and events in vast quantities of A) audio and B) visual data.
By working together across two parallel, highly interconnected streams of ICT research, we will develop the foundations of statistical methodology, algorithms and systems for IML. As an exemplar, this project partners with world leading scientists grappling with the challenge of analysing enormous quantities of heterogeneous data being generated in Biodiversity Science.
Please go to http://www.i-like.org.uk/launch-day-31st-january-2013.html for details of this event.
The day is principally aimed at phd and post doc level, although others are more than welcome to attend as well.
The Gatsby Computational Neuroscience Unit at University College London is looking to recruit a junior or senior level faculty member in machine learning or statistics. We especially seek candidates who work in probabilistic or statistical machine learning.
The closing date for applications is 20 December 2012
For further information, please see www.gatsby.ucl.ac.uk/vacancies/FacultyJD.pdf
Professor Mark Girolami awarded EPSRC funding to lead UK research network on computational statistic
EPSRC are providing three years of funding to establish and build a UK wide research network on Computational Statistics and Machine Learning. This network will be led by the UCL Centre of Computational Statistics and Machine Learning (www.csml.ucl.ac.uk). Website http://gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K009788/1
Dr Simon Byrne has been awarded a Post-Doctoral Fellowship from the Engineering and Physical Sciences Research Council (EPSRC) for his project entitled "Information geometry for Bayesian hierarchical models". This will provide support for three years, with the aim of developing both theoretical understanding of the geometric structure of hierarchical models, as well as practical computational tools to make these models feasible for larger and more complex problems.
Dr Ben Calderhead, Research Fellow in CoMPLEX, and Professor Mark Girolami, Director of CSML, have been awarded dCSE funding from EPSRC and The Numerical Algorithms Group (NAG) to employ a software developer for 1 year for an ambitious plan to develop highly parallelised code implementing differential geometric MCMC statistical methodology for cluster computers in UCL, and for HECToR, the national supercomputer.
Professor Mark Girolami, Director of CSML, has been awarded an EPSRC Established Career Research Fellowship: "Advancing the Geometric Framework for Computational Statistics: Theory, Methodology and Modern Day Applications". Furthermore, three members of CSML have been awarded EPSRC grants supporting their research projects:
The Institute of Statistical Mathematics (ISM) in Tokyo and CSML have signed an agreement to undertake academic and collaboration to develop mutually beneficial, creative and productive scholarly activities in the field of statistical machine learning. Professor Shiro Ikeda from ISM visited UCL this week to officially sign the agreement, here seen with the Dean of MAPS Richard Catlow, Ricardo Silva and Mark Girolami both from CSML. This is an important agreement and opens a number of new developing collaborations between UCL and ISM.
One collaboration currently underway is on the application of kernel methods from machine learning to problems in statistics (hypothesis testing and Bayesian inference), undertaken by Professor Fukumizu at the department of statistical modelling at ISM, and Arthur Gretton at the Gatsby Unit.