Distinguished Lecture Series in Statistical Sciences​

Faculty of Science, School of Mathematical Sciences
Queensland University of Technology, QUT Centre for Data Science

1. Sep 20: Bayesian Modelling and Analysis of Challenging Data: Making New Sources of Data Trust worthy: Navier-Stokes, spatial-temporal kriging and combustion stability: a prominent example of physics-based analytics
2. Sep 21: Bayesian Modelling and Analysis of Challenging Data: Identifying the Intrinsic Dimension of High-Dimensional Data

School of Industrial and Systems Engineering, Georgia Institute of Technology

1. Sep 28: Navier-Stokes, spatial-temporal kriging and combustion stability: a prominent example of physics-based analytics
2. cmenet: a new method for bi-level variable selection of conditional main effects

Department of Electrical Engineering and Computer Science and Department of Statistics, University of California, Berkeley

1. Inference Meets Computation: Dynamical, Stochastic and Economic Perspectives
2. On Gradient-Based Optimization: Accelerated, Stochastic and Nonconvex

Department of Statistics, Harvard University

Oct 29: Improving Health Mobile Interventions

Oct 30: Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time

Department of Statistics, Texas A&M University

School of Mathematical and Physical Sciences, University of Technology
Sydney, Australia

1. Oct 4: A Personal Tour: The Complex Nature of Nutrient and Food Intakes, and the Effects this Complexity has on Understanding Dietary Distributions and Their Effects on Mortality and Chronic Disease
2. Oct 5: Semiparametric Analysis of Complex Polygenic Gene-Environment Interactions in Case-Control Studies

Department of Statistics, Harvard University

1. Nov 23: Essential Concepts for Causal Inference in Randomized Experiments and Observational Studies
2. Models for Imputing Missing Data, Including Methods for Assessing Sensitivity of Conclusions to Them

University of California, Berkeley

1. Apr 3: Stability
2. Apr 24: The multi-facets of a data science project to answer: how are organs formed?

Walter & Eliza Hall Institute of Medical Research, Melbourne

1. Apr 9: Epigenetics: A New Frontier
2. Apr 10: Normalization of omic data after 2007

Harvard University

1. Nov 14: Trivial Mathematics but Deep Statistics: Simpson's Paradox and Its Impact on Your Life
2. Nov 15: Who is crazier: Bayes or Fisher?

Department of Biostatistics, University of Washington

1. Dec 6: Clinical Trials and Epidemiology: Reflections of the Statistician for the National Wilms Tumor Study
2. Dec 7: Inference on Hazard Ratios and Survival Probabilities from Two-phase Stratified Samples

Department of Statistics, Stanford University

1. Oct 5: Statistical Learning with Large Numbers of Predictor Variables
2. Oct 6: Predictive Learning via Rule Ensembles

Department of Operation Research and Financial Engineering, Princeton University

1. May 3: Vast-dimensionality and sparsity
2. May 4: ISIS: A vehicle for the universe of sparsity

Winton Professor of the Public Understanding of Risk,
Centre for Mathematical Sciences, Cambridge

1. May 27: Analysing Uncertainty
2. May 28: Communicating Uncertainty

Mary V. Sunseri Professor of Statistics and Mathematics, Stanford University

1. Sept 27: Mathematics and Magic Tricks
2. Sept 28: Gibbs sampling, orthogonal polynomials and Alternating projections

University of Washington

1. April 3, 2006 Latent variables, uncertainty and evidence.
2. April 4, 2006 Uncertainty in inheritance and the detection of genetic linkage

Stanford University

1. Nov 9: Fifty Years Of Empirical Bayes
2. Nov 10: Correlation And Large-Scale Simultaneous Significance Testing

Department of Statistics, University of Oxford

1. Sep 14: Graphical Models and their Statistical Interpretations
2. Sep 15: Graphical Models: Some Technical Developments 

Mathematics and Statistics, Carleton University and McGill University

1. Apr 23: Probabilistic Phenomena in Mathematics and Science
2. Apr 24: Probabilistic Phenomena in Mathematics and Science

University of Toronto, Statistics Department

1. Apr 16: Is the future Bayesian or frequentist?
2. Apr 18: Is the future Bayesian or frequentist?

Australian National University

1. Sep 28: Data Tuning
2. Oct 26: Estimating Fault Lines and Boundaries