Prof Mohamed Siala is a Professor at Ecole Supérieure des Communications de Tunis (Higher School of Communication of Tunisia, Sup’Com) where he has been teaching since 2001.
Information Science Lab would like to extend a warm welcome to 6 new students who joined KAUST from different universities from all parts of the world during the Fall 2022/23 semester.
Xing Liu, one of the PhD students under Prof. Tareq Al-Naffouri has recently completed a one-month internship from August to September 2022 at Delft University of Technology (TU Delft), Faculty of Civil Engineering and Geoscience, under the supervision of Prof. Peter JG Teunissen.
He is working on Distributed Least-Square Estimation for GNSS Networks.
Dr. Abdulaziz Alorainy completed his Ph.D. in 2017 from The University of British Columbia, Canada. Since then, he has been working as an Assistant Professor at King Abdullah City of Science and Technology, Riyadh, Saudi Arabia. He joined KAUST as a visiting faculty member for 1 year starting in August 2021.
We at Information Science Lab would like to extend our heartfelt congratulations to the following students who have graduated during the academic year 2021/2022.
On March 12th, Professor Tareq Al-Naffouri delivered an online public colloquium titled, "Sensing, Localization, and Communications to Enable Future IoT Systems".
The abstract of the article can be found here
On April 30th 2020, Professor Tareq Al-Naffouri delivered a talk on Optimal Regularization in Estimation, Detection, and Classification. Due to the COVID-19 pandemic, the talk was delivered online via Zoom.
To find the abstract of the talk, press here.
This year, the ISL department has welcomed 13 new interns from across the globe. These students are working with Professor Tareq Al-Naffouri on projects and research. Due to COVID-19, many interns are working remotely which is a unique experience for everyone involved.
Title: Asymptotic Performance Analysis of the Randomly-Projected RLDA Ensemble Classifier
Date: June 24th, 2019
Thesis topic : An asymptotic study of a particular classifier using random matrix theory tools in order to derive an approximation for its error rate.