high dimensional statistics

2018 Workshop on Statistics and Data Science

Nov 12, 08:30 - Nov 15, 17:00

Auditorium 0215 between Buildings 4 & 5

data science high dimensional statistics modeling biological processes physical processes

The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). View Workshop's Agenda.

Random Matrix Theory: Selected Applications from Statistical Signal Processing and Machine Learning

Khalil Elkhalil, Postdoctoral Research Fellow, Electrical and Computer Engineering

Jun 24, 09:00 - 10:00

B1 L4 R4214

Random Matrix Theory machine learning high dimensional statistics data science

Random matrix theory is an outstanding mathematical tool that has demonstrated its usefulness in many areas ranging from wireless communication to finance and economics. The main motivation behind its use comes from the fundamental role that random matrices play in modeling unknown and unpredictable physical quantities. In many situations, meaningful metrics expressed as scalar functionals of these random matrices arise naturally. Along this line, the present work consists in leveraging tools from random matrix theory in an attempt to answer fundamental questions related to applications from statistical signal processing and machine learning.

Ph. D. Student Khalil Elkhalil nominated finalist for the Best Student Paper Award in the IEEE MLSP Conference

1 min read · Mon, Nov 20 2017

machine learning high dimensional statistics data science

Fast-placing Machine Learning (ML) is continuously in need of new algorithms, but testing their effectiveness is utterly time-consuming. "Asymptotic Performance Of Regularized Quadratic Discriminant Analysis Based Classifiers," a new paper presented by CEMSE Ph.D. Student Khalil Elkhalil puts ML on the fast gear and a finalist for the Best Student Paper Award in the IEEE MLSP Conference held last September in Roppongi, Tokyo, Japan.

Zipei Geng

Ph.D. Student, Statistics

spatio-temporal statistics high dimensional statistics graphical models

Sarbojit Roy

Postdoctoral Research Fellow, Statistics

high dimensional statistics Time Series

Khalil Elkhalil

Postdoctoral Research Fellow, Electrical and Computer Engineering

machine learning high dimensional statistics data science Random Matrix Theory Selected Applications statistical signal processing Supervised Learning Algorithms Feedback Reduction in Multiuser and Relay Networks

PhD degree candidate of the Electrical Engineering, King Abdullah University of Science and Technology.

Ayed M. Alrashdi

Ph.D. Student (former), Electrical and Computer Engineering

statistical signal processing Compressive Sensing high dimensional statistics mathematical optimization statistical learning Wireless Communications

Ph.D. degree in electrical and computer engineering from the King Abdullah University of Science and Technology (KAUST) working with Professor Tareq Al-Naffouri in the Information System Lab (ISL),Assistant Professor with the Electrical Engineering Department at University of Ha'il