Future Directions in High-Dimensional Data Analysis: New Methodologies: New Data Types and New Applications

23 June to 27 June 2008

Isaac Newton Institute for Mathematical Sciences, Cambridge, UK

Organisers: Dr David Barber (University College London), Professor Iain Johnstone (Stanford University), Dr Richard Samworth (University of Cambridge) and Professor Michael Titterington (University of Glasgow)

in association with the Newton Institute programme Statistical Theory and Methods for Complex, High-Dimensional Data (7 January to 27 June 2008)

Programme | Participants | Poster Presenters | Contributed Talks | Application | Accommodation and Cost | Web Seminars | Photograph

Theme of Workshop:

This closing workshop of the research programme will look both backwards and forwards, although mainly the latter. There will be a retrospective component that reviews some of the ideas and advances that have been generated or initiated during the preceding six months, but the emphasis will be on presentations that look to the future, in this vital area of the analysis of large-scale data-sets. The invited speakers will include leading researchers from statistics and machine learning, and will describe theoretical/methodological advances as well as issues associated with implementation in important applied fields, such as finance, climatology, analysis of sparse signals and genomics.

Confirmed Speakers:

D Barber (UCL); T Cai (Pennsylvania); U Gather (Dortmund); P Hall (Melbourne); V Koltchinskii (Georgia); E Levina (Michigan); KC Li (Academia Sinica); N Meinshausen (Osford); A Owen (Stanford); J Rice (Berkeley); J Robins (Harvard); MW Seeger (MPI); JS Shawe-Taylor (UCL); R Tibshirani (Stanford); A Tsybakov (Paris); J van Houwelingen (Leiden); J-L Wang (Davis); E Wit (Lancaster); PJ Wolfe (Harvard); M Yuan (Georgia)

Contributed Talks and Posters:

There will be a limited number of contributed talks and poster places. If you wish to be considered to present a talk or poster please indicate your request on the application form.

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