KAUST researchers rank in top 1% of cited work worldwide

The Clarivate Web of Science Group recognizes pioneers in their fields, demonstrated by the production of multiple highly-cited papers that rank in the top 1% by citations for field and year in the Web of Science™. Photo: Clarivate Web of Science

Fifteen KAUST faculty members and one postdoctoral researcher rank among the 2020 Highly Cited Researchers from around the world, according to Clarivate Web of Science Group, a global leader of academic research analytics. The list names individuals whose work accounts for the top 1% of research cited worldwide.

Representing diverse disciplines, these scholars are leaders in their fields, champions of the KAUST community, and visionaries who are pushing the boundaries of how we understand the world, with expertise in areas such as health, the environment, communications and food security. Academic peers rely on their findings in developing and publishing innovative work.

KAUST researchers have access to world-class laboratories, research infrastructure, and a network of support from colleagues, students, staff and external partners that help to make their projects possible.

KAUST 2020 Highly Cited Researchers:

Computer, Electrical, and Mathematical Sciences and Engineering Division

Dr. Mohamed-Slim Alouini
Distinguished Professor of Electrical and Computer Engineering

Mohamed-Slim Alouini's research is in the modeling, design, and performance evaluation of optical wireless communications systems and networks, with a focus on developing new generations of aerial and space networks that provide connectivity to remote, less-populated areas. He is also the associate dean of the Computer, Electrical, and Mathematical Sciences and Engineering Division at KAUST.

Dr. Håvard Rue
Professor of Statistics

Håvard Rue's research is in computational Bayesian statistics and Bayesian methodology such as priors, sensitivity and robustness, with contributing expertise to the R-INLA project, which aims to provide a practical tool for the approximate Bayesian analysis of latent Gaussian models, often at extreme data scales.

The Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE) offers world-class faculty and a research environment endowed with cutting-edge technical infrastructure and unmatched resources. Photo: KAUST

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