SCH Seminar List
for period 7 January to 27 June 2008
| Monday 07 January | ||
| 10:00-11:00 | Donoho, D (Stanford) | |
| Breakdown point of model selection when the number of variables exceeds the number of observations | Sem 1 | |
| 11:30-12:30 | van de Geer, S (Zurich) | |
| The deterministic lasso | Sem 1 | |
| 14:00-15:00 | Wegman, E (George Mason) | |
| Methods for visualizing high dimensional data | Sem 1 | |
| 15:30-16:30 | Young, A (Imperial) | |
| Bootstrap and parametric inference: successes and challenges | Sem 1 | |
| Tuesday 08 January | ||
| 09:00-10:00 | Wainwright, M (Berkeley) | |
| Practical and information-theoretic limitations in high-dimensional inference | Sem 1 | |
| 10:00-11:00 | Samworth, R (Cambridge) | |
| Some thoughts on nonparametric classification: nearest neighbours, bagging and max likelihood estimation of shape-constrained densities | Sem 1 | |
| 11:30-12:30 | Cook, RD (Minnesota) | |
| Model-based sufficient dimension reduction for regression | Sem 1 | |
| 14:00-15:00 | Jordan, M (Berkeley) | |
| Kernel-based contrast functions for sufficient dimension reduction | Sem 1 | |
| 15:30-16:30 | Fan, J (Princeton) | |
| Challenge of dimensionality in model selection and classification | Sem 1 | |
| 16:30-17:30 | Bickel, P (Berkeley) | |
| Regularised estimation of high dimensional covariance matrices | Sem 1 | |
| Wednesday 09 January | ||
| 09:00-10:00 | Murtagh, F (Royal Holloway) | |
| The ultrametric topology perspective on analysis of massive, very high dimensional data stores | Sem 1 | |
| 10:00-11:00 | Duembgen, L (Bern) | |
| P-values for computer-intensive classifiers | Sem 1 | |
| 11:30-12:30 | Stuetzle, W (Washington) | |
| Nonparametric cluster analysis: estimating the cluster tree of a density | Sem 1 | |
| 14:00-15:00 | West, M (Duke) | |
| Sparsity modelling in large-scale dynamic models for portfolio analysis | Sem 1 | |
| 15:30-16:30 | Candes, E (Caltech) | |
| Computationally tractable statistical estimation when there are more variables than observations | Sem 1 | |
| 16:30-17:30 | Nadler, B (Weizmann) | |
| Learning in high dimensions, noise, sparsity and treelets | Sem 1 | |
| Thursday 10 January | ||
| 09:00-10:00 | van der Vaart, AW (Amsterdam) | |
| Estimating a response parameter in missing data models with high-dimensional covariates | Sem 1 | |
| 10:00-11:00 | Wellner, J (Seattle) | |
| Persistence: alternative proofs of some results of Greenshtein and Ritov | Sem 1 | |
| 11:30-12:30 | Cook, D (Iowa State) | |
| Looking at models in high-dimensional data spaces | Sem 1 | |
| 14:00-15:00 | Tanner, J (Edinburgh) | |
| The surprising structure of Gaussian point clouds and its implications for signal processing | Sem 1 | |
| 15:30-16:30 | Lee, A (Carnegie-Mellon) | |
| Finding low-dimensional structure in high-dimensional data | Sem 1 | |
| 16:30-17:30 | Niyogi, P (Chicago) | |
| A geometric perspective on learning theory and algorithms | Sem 1 | |
| Friday 11 January | ||
| 09:00-10:00 | Buehlmann, P (Zurich) | |
| High-dimensional variable selection and graphs: sparsity, faithfulness and stability | Sem 1 | |
| 10:00-11:00 | Mammen, E (Mannheim) | |
| Time series regression with semiparametric factor dynamics | Sem 1 | |
| 11:30-12:30 | Yu, B Y (Berkeley) | |
| Using side information for prediction | Sem 1 | |
| 14:00-15:00 | Hoyle, D (Manchester) | |
| A physicist's approach to high-dimensional inference | Sem 1 | |
| 15:30-16:30 | Clarke, B (British Columbia) | |
| Models, model lists, model spaces and predictive optimality | Sem 1 | |
| Monday 14 January | ||
| 11:00-12:00 | Jin, J (Purdue) | |
| Innovative higher criticism for detecting sparse signals in correlated noise | Sem 2 | |
| Wednesday 16 January | ||
| 11:00-12:00 | Nan, B (Michigan) | |
| Hierarchically penalised Cox regression for censored data with grouped variables and its oracle property | Sem 2 | |
| Friday 18 January | ||
| 11:00-12:00 | Pontil, M (UCL) | |
| A spectral regularisation framework for multi-task structure learning | Sem 2 | |
| Tuesday 22 January | ||
| 11:00-12:00 | Banks, D (Duke ) | |
| Statistical issues amd metabolomics | Sem 2 | |
| Thursday 24 January | ||
| 11:00-12:00 | Butucea, C (Paris VI) | |
| Excess mass estimation | Sem 2 | |
| 15:00-17:00 | Cook, D (Minnesota) | |
| An informal introduction to sufficient dimension reduction | Sem 2 | |
| Friday 25 January | ||
| 11:00-12:00 | Clarke, J (Miami School of Medicine) | |
| An ensemble approach to improved prediction from multitype data | Sem 2 | |
| Tuesday 29 January | ||
| 11:00-12:30 | Zhou, H (Yale) | |
| Model selection and sharp asymptotic minimaxity | Sem 2 | |
| Thursday 31 January | ||
| 09:00-10:00 | Kirk, E (Cantab Capital Partners) | |
| High frequency micro structure in futures markets | Sem 1 | |
| 10:00-10:45 | Rogers, C (Cambridge ) | |
| Choosing a portfolio of many assets | Sem 1 | |
| 11:00-12:00 | Clarkson, P (BNP Paribas) | |
| A database of foreign exchange deals | Sem 1 | |
| Tuesday 05 February | ||
| 11:00-12:00 | Belabbas, A (Harvard) | |
| Approximation methods in statistical learning theory | Sem 2 | |
| Thursday 07 February | ||
| 11:00-12:00 | Lawrence, N (Manchester) | |
| Modelling human motion with Gaussian processes | Sem 2 | |
| Friday 08 February | ||
| 11:00-12:00 | Caponnetto, A (City University of Hong Kong) | |
| Properties of regularisation operators in learning theory | Sem 2 | |
| Tuesday 12 February | ||
| 11:00-12:00 | Kent, J (Leeds) | |
| Procrustes methods for projective shape | Sem 2 | |
| Wednesday 13 February | ||
| 14:00-15:00 | Said, YH (George Mason) | |
| Text mining and high dimensional statistical analysis | Sem 2 | |
| Friday 15 February | ||
| 11:00-12:00 | Titterington, M (Glasgow) | |
| An introduction to variational methods for incomplete-data problems | Sem 2 | |
| Monday 18 February | ||
| 15:00-15:30 | Heller, K (UCL) | |
| Bayesian hierarchical clustering | Sem 1 | |
| 15:30-16:00 | Ghahramani, Z (Cambridge) | |
| Bayesian nonparametric latent feature models | Sem 1 | |
| 16:00-16:30 | Silva, R (Cambridge) | |
| New models for relational classification | Sem 1 | |
| 16:30-17:00 | Snelson, E (Microsoft Cambridge) | |
| Gaussian process methods for large and high-dimensional data sets | Sem 1 | |
| Tuesday 19 February | ||
| 11:00-12:00 | Seeger, M (Max-Planck) | |
| Expectation Propagation -- Experimental Design for the Sparse Linear M | Sem 2 | |
| Thursday 21 February | ||
| 11:00-12:00 | Cristianini, M (Bristol) | |
| Some statistical problems from artificial intelligence | Sem 2 | |
| Friday 22 February | ||
| 11:00-12:00 | Lafferty, J (Carnegie Mellon) | |
| Functional sparsity | Sem 2 | |
| Tuesday 26 February | ||
| 11:00-12:00 | Storkey, A (Edinburgh) | |
| Learning latent activites in large scale dynamical problems | Sem 2 | |
| Thursday 28 February | ||
| 11:00-12:00 | George, E (Pennsylvania) | |
| Pre-modelling via BART | Sem 1 | |
| Friday 29 February | ||
| 11:00-12:00 | Hennig, C (UCL) | |
| Some thoughts about the design of dissimilarity measures | Sem 2 | |
| Tuesday 04 March | ||
| 11:00-12:00 | Shi, JQ (Newcastle) | |
| Gaussian process functional regression model for curve prediction and clustering | Sem 2 | |
| Thursday 06 March | ||
| 11:00-12:00 | Olhede, S (UCL) | |
| Non-parametric estimation of HARDI diffusion weighted magnetic resonance imaging data | Sem 1 | |
| Tuesday 11 March | ||
| 11:00-12:00 | Kovac, A (Bristol) | |
| Total variation and curves | Sem 2 | |
| 14:00-15:00 | House, L (Durham) | |
| Proteomics data analysis | Sem 2 | |
| Wednesday 12 March | ||
| 16:15-17:00 | Titterington, M (Glasgow) | |
| Some issues raised by high dimension in Statistics - a partial overview of the SCH Programme | Sem 1 | |
| Thursday 13 March | ||
| 11:00-12:00 | Dryden, I (Nottingham) | |
| Multilevel modelling of proteomic mass-spectrometry data | Sem 2 | |
| Monday 17 March | ||
| 17:00-18:00 | Donoho, D (Stanford University) | |
| More unknowns than equations? Not a problem! Use Sparsity! | Sem 1 | |
| Tuesday 18 March | ||
| 11:00-12:00 | Hancock, E (York) | |
| Analysis of graphs using diffusion processes and random walks (a random walk through spectral graph theory) | Sem 1 | |
| Wednesday 19 March | ||
| 11:00-12:00 | Whittaker, J (Lancaster) | |
| Bootstrapping divergence weighted independence graphs | Sem 2 | |
| Wednesday 26 March | ||
| 11:00-12:00 | Teh, WH (UCL) | |
| Improvements to variational Bayesian inference | Sem 2 | |
| Thursday 27 March | ||
| 11:00-12:00 | Kleijn, B (Free University) | |
| The semiparametric Bernstein-Von Mises theorem | Sem 2 | |
| Monday 31 March | ||
| 10:00-11:00 | Birney, E (EBI) | |
| The evolution of promoter sequence | Sem 1 | |
| 11:30-12:30 | Pachter, L (UC, Berkeley) | |
| Functional genomics and the forest of life | Sem 1 | |
| 14:00-15:00 | Brunak, S (Denmark) | |
| Understanding interactomes by data integration | Sem 1 | |
| 15:30-16:30 | McLachlan, GJ (Queensland) | |
| On mixture models in high-dimensional testing for the detection of differential gene expression | Sem 1 | |
| 16:30-17:30 | Margulies, E (National Human Genome Research) | |
| Statistical challenges in using comparative genomics for the identification of functional sequences | Sem 1 | |
| Tuesday 01 April | ||
| 09:00-10:00 | Hurles, M (Sanger) | |
| Structural variation in the human genome | Sem 1 | |
| 10:00-11:00 | Benjamini, Y (Tel Aviv) | |
| Selective inference in complex research problems | Sem 1 | |
| 11:30-12:30 | Durbin, R (Sanger) | |
| Efficient use of population genome sequencing data | Sem 1 | |
| 14:00-15:00 | West, M (Duke) | |
| Sparsity modelling in gene expression pathway studies | Sem 1 | |
| 15:30-16:30 | Dermitzakis, M (Sanger) | |
| Population genomics of human gene expression | Sem 1 | |
| Wednesday 02 April | ||
| 09:00-10:00 | Enright, A (Sanger) | |
| Computational analysis and prediction of microRNA binding sites | Sem 1 | |
| 10:00-11:00 | Bühlmann, P (ETH Zürich) | |
| L1-regularisation, motif regression and ChIP-on-chip data analysis | Sem 1 | |
| 11:30-12:30 | Huber, W (EBI) | |
| Extraction and classification of cellular and genetic phenotypes from automated microscopy data | Sem 1 | |
| 14:00-15:00 | Beerenwinkel, N (ETH Zürich) | |
| Ultra-deep sequencing of mixed virus populations | Sem 1 | |
| Thursday 03 April | ||
| 09:00-10:00 | Segal, E (Weizmann Institute) | |
| Cracking the regulatory code: predicting expression patterns from DNA sequence | Sem 1 | |
| 10:00-11:00 | Bickel, PJ (UC Berkeley) | |
| Refined nonparametric methods for genomic inference | Sem 1 | |
| 11:30-12:30 | Marcotte, EM (Texas at Austin) | |
| Steps toward directed identification of disease genes: predicting the consequences of genetic perturbations | Sem 1 | |
| 14:00-15:00 | Crawford, G (Duke) | |
| High-resolution identification of active gene regulatory elements | Sem 1 | |
| 15:30-16:30 | Bulyk, ML (Harvard Medical School) | |
| High-resolution binding specificity profiles of transcription factors and cis regulatory codes in DNA | Sem 1 | |
| Friday 04 April | ||
| 09:00-10:00 | Bertone, P (EBI) | |
| Functional genomic approaches to stem cell biology | Sem 1 | |
| 10:00-11:00 | McVean, G (Oxford) | |
| Approximate genealogical inference | Sem 1 | |
| 11:30-12:30 | Luscombe, N (EBI) | |
| Genomic principles for feedback regulation of metabolism | Sem 1 | |
| 14:00-15:00 | Huang, H (UC Berkeley) | |
| A bayesian probabilistic approach to transform public microarray repositories into disease diagnosis databases | Sem 1 | |
| Tuesday 08 April | ||
| 11:00-12:00 | Rubin, D (Berkeley) | |
| Empirical efficiency maximisation: improved locally efficient covariate adjustment | Sem 2 | |
| Thursday 10 April | ||
| 11:00-12:00 | Taylor, C (Leeds) | |
| Boosting kernel estimates | Sem 2 | |
| Tuesday 15 April | ||
| 11:00-12:00 | Hurley, C (NUI Maynooth) | |
| Data visualisation via pairwise displays | Sem 2 | |
| Thursday 17 April | ||
| 11:00-12:00 | Nadler, B (Weizmann Institute) | |
| Determining the number of factors in a linear mixture model from limited noisy data | Sem 2 | |
| Monday 21 April | ||
| 11:00-12:00 | Shawe-Taylor, J (University College London) | |
| Spectra and generalisation | Sem 2 | |
| Tuesday 22 April | ||
| 11:00-12:00 | Hjort, NL (Oslo) | |
| Empirical likelihood with a growing number of parameters | Sem 2 | |
| Thursday 24 April | ||
| 11:00-12:00 | Robert, C (Paris-Dauphine) | |
| A Bayesian reassessment of nearest-neighbour classification | Sem 2 | |
| Tuesday 29 April | ||
| 11:00-12:00 | Roweis, S (Toronto) | |
| Making the sky searchable: large scale astronomical pattern recognition | Sem 1 | |
| Wednesday 30 April | ||
| 11:00-12:00 | Wellner, J (Washington) | |
| Testing for sparse normal means: is there a signal? | Sem 2 | |
| 14:00-15:00 | Cook, D (Iowa State) | |
| Looking at data and models in high-dimensional spaces: (1) Tools and tips for making good plots | Sem 1 | |
| Tuesday 06 May | ||
| 11:00-12:00 | Bunea, F (Florida State) | |
| Non-asymptotic variable identification via the Lasso and the elastic net | Sem 2 | |
| Wednesday 07 May | ||
| 14:00-15:00 | Cook, D (Iowa State) | |
| Looking at data and models in high-dimensional spaces: (2) How, when and why to use interactive and dynamic graphics | Sem 1 | |
| Thursday 08 May | ||
| 11:00-12:00 | Wegkamp, M (Florida State) | |
| Lasso type classifiers with a reject option | Sem 2 | |
| Tuesday 13 May | ||
| 11:00-12:00 | Hero, A (Michigan) | |
| Entropic graphs for high-dimensional data analysis | Sem 1 | |
| Wednesday 14 May | ||
| 11:00-12:00 | Marron, S (North Carolina) | |
| Object oriented data analysis | Sem 1 | |
| 14:00-15:00 | Cook, D (Iowa State) | |
| Looking at data and models in high-dimensional spaces: (3) Determining significance of structure | Sem 1 | |
| Thursday 15 May | ||
| 11:00-12:00 | Murray, I (Toronto) | |
| Assessing high-dimensional latent variable models | Sem 1 | |
| Monday 19 May | ||
| 16:40-17:10 | Hand, D (Imperial) | |
| Frontiers in applications of data mining | Sem 1 | |
| 17:10-17:40 | Bishop, C (Microsoft Research, Cambridge) | |
| Frontiers in applications of machine learning | Sem 1 | |
| 17:40-18:30 | Wallace, D | |
| Panel discussion | Sem 1 | |
| Tuesday 20 May | ||
| 11:00-12:00 | Girolami, M (Glasgow) | |
| On stratified path sampling of the thermodynamic integral: computing Bayes factors for nonlinear ODE models of biochemical pathways | Sem 1 | |
| Wednesday 21 May | ||
| 11:00-12:00 | Maurer, A | |
| Slow subspace learning | Sem 1 | |
| Thursday 22 May | ||
| 11:00-12:00 | Rattray, M (Manchester) | |
| Latent variable models of transcriptional regulation | Sem 1 | |
| Tuesday 27 May | ||
| 15:00-15:30 | Gramacy, B (Cambridge) | |
| On estimating covariances between many assets with histories of highly variable length | Sem 1 | |
| 15:30-16:00 | Cule, M (Cambridge) | |
| Nonparametric estimation of a log-concave density | Sem 1 | |
| 16:00-16:30 | Silva, R (Cambridge) | |
| Factorial mixture of Gaussians and the marginal independence model | Sem 1 | |
| 16:30-17:00 | Spiegelhalter, D (Cambridge) | |
| Understanding uncertainty | Sem 1 | |
| Wednesday 28 May | ||
| 11:00-12:00 | Mueller, HG (California) | |
| Functional regression and additive models | Sem 1 | |
| Thursday 29 May | ||
| 11:00-12:00 | Cristianini, N (Bristol) | |
| Learning curves: lessons from statistical machine translation | Sem 2 | |
| Tuesday 03 June | ||
| 11:00-12:00 | Wolfe, PJ (Harvard) | |
| On the approximation of quadratic forms and sparse matrix products | Sem 1 | |
| Thursday 05 June | ||
| 11:00-12:00 | Guillas, S (University College London) | |
| Approximation of functional spatial regression models using bivariate splines | Sem 1 | |
| Friday 06 June | ||
| 11:00-12:00 | Nychka, DW (National Centre for Atmospheric Research) | |
| Challenges of regional climate modelling and validation | Sem 1 | |
| Tuesday 10 June | ||
| 11:00-12:00 | Koltchinskii, V (Georgia Institute of Technology) | |
| Sparse recovery in convex hulls based on entropy penalisation | Sem 1 | |
| Thursday 12 June | ||
| 11:00-12:00 | Rohde, A (Weierstrass) | |
| Confidence sets for the optimal approximating model - bridging a gap between adaptive point estimation and confidence regions | Sem 2 | |
| Tuesday 17 June | ||
| 11:00-12:00 | van Houwelingen, JC (Leiden University Medical Centre) | |
| Global testing of association and/or predictability in regression problems with p>>n predictors | Sem 1 | |
| Wednesday 18 June | ||
| 14:00-15:00 | Godsill, S (Cambridge) | |
| Sequential inference for dynamically evolving groups of objects | Sem 1 | |
| 15:30-16:10 | Cai, Y (Plymouth) | |
| A Bayesian method for non-Gaussian autoregressive quantile function time series models | Sem 1 | |
| 16:10-16:50 | Luo, X (Oxford) | |
| State estimation in high dimensional systems: the method of the ensemble unscented Kalman filter | Sem 1 | |
| 16:50-17:30 | Whiteley, N (Cambridge) | |
| A modern perspective on auxiliary particle filters | Sem 1 | |
| Friday 20 June | ||
| 09:00-09:40 | McLachlan, GJ (Queensland) | |
| Clustering of time course gene-expression data via mixture regression models | Sem 1 | |
| 09:40-10:20 | Titsias, MK (Manchester) | |
| Markov chain Monte Carlo algorithms for Gaussian processes | Sem 1 | |
| 10:20-11:00 | Aston, J (Warwick) | |
| Is that really the pattern we're looking for? Bridging the gap between statistical uncertainty and dynamic programming algorithms | Sem 1 | |
| 11:30-12:30 | Moulines, E (CNRS) | |
| Adaptive Monte Carlo Markov Chains | Sem 1 | |
| 14:00-15:00 | Papaspiliopoulos, O (Universitat Pompeu Fabra ) | |
| A methodological framework for Monte Carlo estimation of continuous-time processes | Sem 1 | |
| 15:30-16:10 | Sykulski, A, Olhede, SC (Imperial/UCL) | |
| High frequency variability and microstructure bias | Sem 1 | |
| 16:10-17:10 | Ghahramani, Z (Cambridge) | |
| Nonparametric Bayesian times series models: infinite HMMs and beyond | Sem 1 | |
| Monday 23 June | ||
| 10:00-11:00 | Hall, PG (Melbourne) | |
| Variable selection in very high dimensional regression and classification | Sem 1 | |
| 11:30-12:30 | Gather, U (Dortmund) | |
| Dimension reduction | Sem 1 | |
| 14:00-15:00 | Meinshausen, N (UC Berkeley) | |
| Stability - based regularisation | Sem 1 | |
| 15:30-16:30 | Cai, T (Pennsylvania ) | |
| Large-scale multiple testing: finding needles in a haystack | Sem 1 | |
| Tuesday 24 June | ||
| 09:00-10:00 | van Houwelingen, J (Leiden University Medical Center) | |
| Fitting survival models with P>>n predictors: beyond proportional hazards | Sem 1 | |
| 10:00-11:00 | Yuan, MY (Georgia Institute of Technology) | |
| Model selection and estimation with multiple reproducing Karnel Hilbert spaces | Sem 1 | |
| 11:30-12:30 | Tsybakov, A (CREST and Paris 6) | |
| Sparsity oracle inequalities | Sem 1 | |
| 14:00-14:20 | Airoldi, E (Princeton) | |
| The exchangeable graph model for statistical network analysis | Sem 1 | |
| 14:20-14:40 | West, M (Duke) | |
| Data, models, inference and computation for dynamic cellular networks in systems biology | Sem 1 | |
| 14:40-15:00 | Xing, E (Carnegie Mellon) | |
| Statistical network analysis and inference: methods and applications | Sem 1 | |
| 15:30-16:30 | Wit, E (Lancaster) | |
| High dimensional inference in bioinformatics and genomics | Sem 1 | |
| 16:30-17:30 | Li, KC (Adademia Sinica) | |
| Liquid association for large scale gene expression and network studies | Sem 1 | |
| Wednesday 25 June | ||
| 09:00-10:00 | Tibshirani, R (Stanford) | |
| The Lasso: some novel algorithms and applications | Sem 1 | |
| 10:00-11:00 | Shawe-Taylor, JS (UC London) | |
| Sparsity in machine Learning: approaches and analyses | Sem 1 | |
| 11:30-12:30 | Owen, A (Stanford) | |
| Transposably invariant sample reuse: the pigeonhole bootstrap and blockwise cross-validation | Sem 1 | |
| Thursday 26 June | ||
| 09:00-10:00 | Wang, J-L (UC Davis) | |
| Covariate adjusted functional principal component analysis for longitudinal data | Sem 1 | |
| 10:00-11:00 | Koltchinskii, V (Georgia Institute of Technology) | |
| Penalized empirical risk minimization and sparse recovery problems | Sem 1 | |
| 11:30-12:30 | Wolfe, P (Harvard) | |
| The Nystrom extension and spectral methods in learning: low-rank approximation of quadratic forms and products | Sem 1 | |
| 14:00-14:20 | Pan, G (Eurandom) | |
| Limiting theorems for large dimensional sample means, sample covariance matrices and Hotelling's T2 statistics | Sem 1 | |
| 14:20-14:40 | Shi, JQ (Newcastle) | |
| Generalised gaussian process functional regression model | Sem 1 | |
| 14:40-15:00 | Wang, Y (NSF) | |
| Estimation of large volatility matrix for high-frequency financial data | Sem 1 | |
| 15:30-16:30 | Barber, D (UC London) | |
| Graph decomposition for community identification and covariance constraints | Sem 1 | |
| 16:30-17:30 | Levina, E (Michigan) | |
| Permutation-invariant covariance regularisation in high dimensions | Sem 1 | |
| Friday 27 June | ||
| 09:00-09:20 | Helland, IS (Oslo) | |
| Optimal prediction from relevant components | Sem 1 | |
| 09:20-09:40 | Koch, I (New South Wales) | |
| Dimension selection with independent component analysis and its application to prediction | Sem 1 | |
| 09:40-10:00 | Li, L (North Carolina) | |
| Model free variable selection via sufficient dimension reduction | Sem 1 | |
| 10:00-11:00 | Robins, J (Harvard) | |
| Estimation of nonlinear functionals: recent results and open problems | Sem 1 | |
| 11:30-12:30 | Seeger, MW (MPI for Biological Cybernetics) | |
| Applications of approximate inference and experimental design for sparse (generalised) linear models | Sem 1 | |
| 14:00-15:00 | Rice, J (UC Berkeley) | |
| Statistics in astronomy: the Taiwanese-American occultation survey | Sem 1 | |
| Other Seminars |
|
Seminars in the University National and International Scientific Research Meetings |