Al-Kindi Distinguished Statistics Lectures

The Al-Kindi Distinguished Statistics Lectures are an annual event in Statistics at King Abdullah University of Science and Technology (KAUST). A distinguished guest speaker presents a series of two lectures and remains in residence for some days. The first lecture is intended to demonstrate to a general audience the breadth of use of Statistics in applications. The second lecture is intended for a specialized audience.

The lectures are named after Al-Kindi (801-873 CE), a prominent figure in the House of Wisdom, whose book entitled "Manuscript on Deciphering Cryptographic Messages" is believed to be the earliest writing on Statistics. In his book, Al-Kindi gave a detailed description of how to decipher encrypted messages using Statistics and frequency analysis. This text arguably gave rise to the birth of both Statistics and cryptanalysis.

Our upcoming Al-Kindi Distinguished Statistics Lectures will be presented by Prof. David L. Donoho on November 12, 2019.


2019: David L. Donoho, Professor of Statistics and Anne T. and Robert M. Bass Professor in the Humanities and Sciences, Stanford University, USA

Lecture 1: 12:00-13:00pm.

Lecture 2: 15:00-16:00pm

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2018: Jim Berger, The Arts and Sciences Professor of Statistics, Duke University, USA

Lecture 1: Reproducibility of Science: p-values, Multiple Testing, and Optional Stopping​

Lecture 2: Gaussian Process Emulation of Computer Models with Massive Output

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2017: Steve Marron, Amos Hawley Distinguished Professor of Statistics, UNC-Chapel Hill, USA

Lecture 1: Object-Oriented Data Analysis

Lecture 2: Object-Oriented Data Analysis of Manifold Data

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2016: Noel Cressie, Distinguished Professor of Statistics, University of Wollongong, Australia

Lecture 1: The Carbon Club

Lecture 2: A Conditional Approach to Multivariate Spatial Modelling

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2015: Raymond J. Carroll, Distinguished Professor and Jill and Stuart A. Harlin 83 Chair in Statistics, Texas A&M University, USA

Lecture 1: What Percentage of Children in the U.S. are Eating an Alarmingly Poor Diet? A Statistical Approach

Lecture 2: Constrained Maximum Likelihood Estimation for Model Calibration using Summary-Level Information from External Big Data Sources

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