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 Michael I. Jordan. Date: Spring 2024.
Lecture 1: TBA
Lecture 2: TBA
Videos 1 & 2, Pictures
Lecture 1: Detecting Cellwise Outliers in Your Data
Lecture 2: New Graphical Displays for Classification
Lecture 1: We used Reinforcement Learning; but did it work?
Lecture 2: Inference for Longitudinal Data After Adaptive Sampling
Videos 1 & 2, Picture 1, Picture 2, Picture 3, Picture 4, Picture 5
Lecture 1: Statistical Leaning: Causal-oriented and Robust
Lecture 2: Deconfounding
Lecture 1: Design and Analysis of Prevalence Surveys for Neglected Tropical Diseases
Lecture 2: TBA
Lecture 1: Deepnet Spectra and the Two Cultures of Data Science
Lecture 2: Optimal Singular Value Thresholding in Correlated Noise
Lecture 1: Reproducibility of Science: p-values, Multiple Testing, and Optional Stopping
Lecture 2: Gaussian Process Emulation of Computer Models with Massive Output
Lecture 1: Object-Oriented Data Analysis
Lecture 2: Object-Oriented Data Analysis of Manifold Data
Lecture 1: The Carbon Club
Lecture 2: A Conditional Approach to Multivariate Spatial Modelling
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