Distinguished Lecture Series in Statistical Sciences​

University Professor, Department of Statistical Sciences, University of Toronto

1. Sep 29 (3:30-4:30 pm): Talk Title: TBD
2. Sep 30 (3:30-4:30 pm): Talk Title: TBD
   

Professor of Statistics, Stanford University

1. May 22 (3:30-4:30 pm): Hidden variables: using statistics to decode heterogeneous microbiome data
2. May 23 (3:30-4:30 pm): Statistics and Geometry for Heterogeneous Data
   

Professor, Statistics and Data Science, Luddy Family President’s Distinguished Professor, The University of Pennsylvania

1. May 9: (3:30-4:30 pm): An (un)Holy Union: Causal Inference, Semiparametric Statistics and Machine Learning in the Age of Data Science
2. May 10 (3:30-4:30 pm): Single Proxy Control

Professor, Department of Biostatistics; Coordinating Director, Program in Quantitative Genomics; Harvard T.H. Chan School of Public Health; Professor of Statistics, Department of Statistics, Harvard University

1. Sep 29 (3:30-4:30 pm): Lessons Learned from the COVID-19 Pandemic: A Statistician’s Reflection
2. Sep 30 (3:30-4:30 pm): Ensemble Methods for Testing a Global Null Hypothesis

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

1. Sep 20 (4:00-5:00 pm): Bayesian Modelling and Analysis of Challenging Data: Making New Sources of Data Trust worthy
2. Sep 21 (4:00-5:00 pm): 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 (3:30-4:30 pm): Navier-Stokes, spatial-temporal kriging and combustion stability: a prominent example of physics-based analytics
2. Sep 29 (3:30-4:30 pm): 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. Oct 15 (3:30-4:30 pm): Inference Meets Computation: Dynamical, Stochastic and Economic Perspectives
2. Oct 16 (3:30-4:30 pm): On Gradient-Based Optimization: Accelerated, Stochastic and Nonconvex

Department of Statistics, Harvard University

Oct 29 (3:30-4:30 pm): Improving Health Mobile Interventions

Oct 30: (3:30-4:30 pm) 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 (2:00-3:00 pm): 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 (2:00-3:00 pm): Semiparametric Analysis of Complex Polygenic Gene-Environment Interactions in Case-Control Studies

Department of Statistics, Harvard University

1. Nov 23 (3:30-4:30 pm): Essential Concepts for Causal Inference in Randomized Experiments and Observational Studies
2. Nov 24 (3:30-4:30 pm): Models for Imputing Missing Data, Including Methods for Assessing Sensitivity of Conclusions to Them

University of California, Berkeley

1. Apr 23 (3:30-4:30 pm): Stability
2. Apr 24 (3:30-4:30 pm): 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 (3:30-4:30 pm): Epigenetics: A New Frontier
2. Apr 10 (3:30-4:30 pm): Normalization of omic data after 2007

Harvard University

1. Nov 14 (11:00 am-12:00 pm): Trivial Mathematics but Deep Statistics: Simpson's Paradox and Its Impact on Your Life
2. Nov 15 (11:00 am-12:00 pm): Who is crazier: Bayes or Fisher?

Department of Biostatistics, University of Washington 

1. Dec 6 (3:30-4:30 pm): Clinical Trials and Epidemiology: Reflections of the Statistician for the National Wilms Tumor Study
2. Dec 7 (3:30-4:30 pm): Inference on Hazard Ratios and Survival Probabilities from Two-phase Stratified Samples

Department of Statistics, Stanford University

1. Oct 5 (3:30-4:30 pm): Statistical Learning with Large Numbers of Predictor Variables
2. Oct 6: Predictive Learning via Rule Ensembles(11:00 am-4:30 pm) 

Department of Operation Research and Financial Engineering, Princeton University

1. May 3 (3:30-4:30 pm): Vast-dimensionality and sparsity
2. May 4 (3:30-4:30 pm): ISIS: A vehicle for the universe of sparsity

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

1. May 27 (3:30-4:30 pm): Analysing Uncertainty
2. May 28 (3:30-4:30 pm): Communicating Uncertainty

Australian National University

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

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

1. Sept 27 (3:30-4:30 pm): Mathematics and Magic Tricks
2. Sept 28 (3:30-4:30 pm): Gibbs sampling, orthogonal polynomials and Alternating projections

University of Washington

1. April 3, 2006 (4:00-5:00 pm): Latent variables, uncertainty and evidence.
2. April 4, 2006 (4:00-5:00 pm);Uncertainty in inheritance and the detection of genetic linkage

Stanford University

1. Nov 9  (4:00-5:00 pm): Fifty Years Of Empirical Bayes
2. Nov 10 (11:00-12:00 qaa,pp: Correlation And Large-Scale Simultaneous Significance Testing

Department of Statistics, University of Oxford

1. Sep 14 (3:30-4:30 pm): Graphical Models and their Statistical Interpretations
2. Sep 15: Graphical Models 3:30-4:30 p) : Some Technical Developments 

Mathematics and Statistics, Carleton University and McGill University

1. Apr 23 (3:30-4:30 pm): Probabilistic Phenomena in Mathematics and Science
2. Apr 24 (3:30-4:30 pm): Probabilistic Phenomena in Mathematics and Science

University of Toronto, Statistics Department

1. Apr 16 (4:00-5:00 pm): Is the future Bayesian or frequentist?
2. Apr 18 (4:00-5,pm): Is the future Bayesian or frequentist?