Topics | Computer Science

Interactive High-Quality Visualization of Large-Scale Particle Data

Mohamed Ibrahim, Postdoctoral Research Fellow, Computer Science

Nov 5, 14:00 - 15:00

B2 L5 R5209

visualization high-quality rendering large-scale simulation particle data aliasing artifact sampling visible particles

Large-scale particle data sets, such as those computed in molecular dynamics (MD) simulations, are crucial to investigating important processes in physics and thermodynamics. The simulated atoms are usually visualized as hard spheres with Phong shading, where individual particles and their local density can be perceived well in close-up views. However, for large-scale simulations with 10 million particles or more, the visualization of large fields-of-view usually suffers from strong aliasing artifacts, because the mismatch between data size and output resolution leads to severe under-sampling of the geometry.

Abdelrahman Eldesokey

Postdoctoral Research Fellow

Computer Vision Diffusion Models Vision Foundation Models Multi-Modal Large Language Models

Abdelrahman Eldesokey is a Postdoctoral Fellow at KAUST, working on Generative AI and Computer Vision. His research focuses on diffusion models, multimodal large language models, and vision foundation models, aiming to make generative systems more controllable, interpretable, and reliable.

Masheal Alghamdi

Ph.D., Computer Science

Computational Photography Computer Vision machine learning Robotics SLAM Vision SLAM

Masheal Alghamdi is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Masheal obtained her bachelor degree in Computer Science from King Abdul-Aziz University (KAU) in Jeddah, Saudi Arabia in 2009. After that she joined KAUST to receive her master degree in Computer Science in 2014. Masheal is a researcher in the National Center for Computation Technology and Applied Math (CTAM) at King Abdul-Aziz City for Science and Technology (KACST)

Visualizing Trees

Prof. Xiaoru Yuan, Peking University

Apr 15, 12:00 - 13:00

B9 L2 H1 R2322

Peking University tree visualisation GoTree visual analytics map metaphor Social media

In this talk, I will introduce a few recent works on tree visualization. First I will present a visualization technique for comparing topological structures and node attribute values of multiple trees. I will further introduce GoTree, a declarative grammar supporting the creation of a wide range of tree visualizations. In the application side, visualization and visual analytics on social media will be introduced. The data from social media can be considered as graphs or trees with complex attributes. A few approaches using map metaphor for social media data visualization will be discussed.

Computer learns to write captions with feeling

1 min read · Tue, May 31 2022

News

machine learning visual computing artificial intelligence Visual Art Computer science

Mohamed Elhoseiny and his PhD student Youssef Mohamed are teaching AI to capture the full spectrum of human emotions when annotating artwork in order to reduce emotional bias in computer-generated captions.

Learning for Visual Data Synthesis and Analysis

Dr. Timo Ropinski, Professor, Visual Computing, Ulm University, Germany

Oct 7, 12:00 - 13:00

B9 L2 H1 R2322

Deep learning visual data synthesis visual data analysis estimation and segmentation Monte carlo methods

Abstract In this talk, I will present our recent advances in deep learning to synthesize and analyze visual data, such as images, volumes, and point clouds. The presented approaches are loosely aligned along the classical computer graphics rendering pipeline, whereby both structured and unstructured data are handled. I will first present concepts for learning in object space, i.e., directly on the data to be rendered. To realize different visual tasks, such as normal estimation and segmentation, I will discuss how Monte Carlo integration can be used to realize convolutions on point cloud data

Learning for Visual Data Synthesis and Analysis

Dr. Timo Ropinski, Professor, Visual Computing, Ulm University, Germany

Oct 7, 12:00 - 13:00

B9 L2 H1 R2322

Deep learning visual data synthesis visual data analysis estimation and segmentation Monte carlo methods

Abstract In this talk, I will present our recent advances in deep learning to synthesize and analyze visual data, such as images, volumes, and point clouds. The presented approaches are loosely aligned along the classical computer graphics rendering pipeline, whereby both structured and unstructured data are handled. I will first present concepts for learning in object space, i.e., directly on the data to be rendered. To realize different visual tasks, such as normal estimation and segmentation, I will discuss how Monte Carlo integration can be used to realize convolutions on point cloud data

Task-driven Automated Data Visualization

Dr. Yunhai Wang, Professor, Computer Science, Shandong University, China

Oct 2, 12:00 - 13:00

B9 L2 H2 R2325

data visualisation visualisation techniques parameters time series data high dimensional data EdWordle ShapeWordle

Abstract By providing visual representations of data, visualization can help people to carry out many data analysis tasks more effectively. Given a data set, however, there are often too many possible visualization techniques, with each technique having many parameters to be tuned. This arises the question is if it is possible to automatically design a visualization that is best suited to pursue a given task on given input data. With my group, I developed a number of techniques to achieve this goal for different data sets such as time-series data, high-dimensional data, text as well as graphs

Towards Scalable and Structured Understanding in Visual LLMs

Mohamed Elhoseiny, Associate Professor, Computer Science

Feb 23, 12:00 - 13:00

B9 L2 R2325

LLM Visual Language Models VLMs visual computing

In this talk, we explore a suite of recent advances toward scalable, structured video comprehension using Large Vision Language Models (Video LLMs).

Point cloud and volume data reconstruction and visualization

Dr. Ciril Bohak, Postdoctoral Researcher, Faculty of Computer and Information Science, University of Ljubljana, Slovenia

Sep 25, 13:00 - 14:00

B1 L2

point cloud volume data reconstruction visualization real-time rendering web-based framework

Abstract Our ongoing research on the reconstruction of aerial point cloud data includes reconstructing the parts of the data that are not captured by the airplane mounted LiDAR system due to the shadowing effect and lack of reflections. We are developing methods for reconstructing (1) building walls, (2) reconstruction of bridges and underlying terrain; (3) reconstruction of water surfaces of lakes and rivers, and (4) reconstruction of terrain overhangs. In all the cases we are merging the point cloud data with cadaster data or digital terrain model data for filling the gaps in the point cloud

Eliminating Waiting Time Inequities for Deceased Donor Kidney Transplantation in the USA

Dr. Joris van de Klundert, Professor of Operations Management, Prince Mohammad Bin Salman College (MBSC) of Business & Entrepreneurship

Nov 18, 12:00 - 13:00

B9 L2 H1 R2322

organ transplantation wait time modeling Equity Maximization network flows queuing theory Rawls theory

This research formally models, analyzes and maximizes equity of transplant waiting times and probabilities using queuing theory and network flows, based on Rawls' theory of justice. The presented formal models address inequities resulting from blood type incompatibilities, which are interrelated to ethnic differences in patient and donor rates.

KAUST showcases its smart health technologies at global forum

1 min read · Mon, Dec 8 2025

News Press Releases

smart health AI wearable devices

Through AI, smart sensors, and computational biology, KAUST is transforming healthcare with practical solutions that address critical health needs in Saudi Arabia and beyond.

A predictive eye on the prize

3 min read · Sun, Oct 16 2022

Awards News

HPC weather forecast climate science

Climate prediction models have had a mathematical makeover that has won attention from a computer science award.

Finding the linchpins of the dark web: Understanding and detecting malicious infrastructure

Dr. Sumayah Alrwais, Assistant Professor, King Saud University, Riyadh, KSA

Oct 21, 12:00 - 13:00

B9 L2 H1 R2322

Cyber attacks and defenses Web and Network security cybersecurity malicious infrastructure detection

In this talk, Sumayah will survey a number of malicious hosting infrastructures for different services and approaches to detecting them. Among them are works on an emerging trend of Bulletproof hosting services reselling infrastructure from lower-end service providers, use of residential proxy as a service to avoid server-side blocking and DNS based hosting infrastructure.

Mehdi Zakroum

Postdoctoral Research Fellow, Computer, Electrical and Mathematical Sciences and Engineering

Cyber Security Web Security Trustworthy AI

Nizar Ajeeb

Researcher (former), King Abdullah University of Science and Technology

Programming language MATLAB data analysis Web Technologies

Full Time Researcher Full at King Abdullah University of Science and Technology.

Point cloud and volume data reconstruction and visualization

Dr. Ciril Bohak, Postdoctoral Researcher, Faculty of Computer and Information Science, University of Ljubljana, Slovenia

Sep 25, 13:00 - 14:00

B1 L2

point cloud volume data reconstruction visualization real-time rendering web-based framework

Abstract Our ongoing research on the reconstruction of aerial point cloud data includes reconstructing the parts of the data that are not captured by the airplane mounted LiDAR system due to the shadowing effect and lack of reflections. We are developing methods for reconstructing (1) building walls, (2) reconstruction of bridges and underlying terrain; (3) reconstruction of water surfaces of lakes and rivers, and (4) reconstruction of terrain overhangs. In all the cases we are merging the point cloud data with cadaster data or digital terrain model data for filling the gaps in the point cloud

Web-based 3D Visualization

Ivan Viola, Professor, Computer Science

Apr 10, 12:00 - 13:00

B9 L2 H2 H2

nanovisualization computer graphics 3D graphics WebGPU WebAssmbly

In this seminar I will present how to create 3D computer graphics and visualization systems for the web, using WebAssmbly and WebGPU language specifications, which are new, bleeding-edge technologies. Previously, accelerated graphics on the web was based on JavaScript libraries, which is still very popular, but they do not offer detailed memory management and code optimization, necessary for systems requiring high memory load and high computational demands. WebAssembly and WebGPU can be compiled from the C++ or Rust code, which also allows the deployment of the same codebase either for web or for the desktop-based applications.

Web-based 3D Visualization

Ivan Viola, Professor, Computer Science

Apr 10, 12:00 - 13:00

B9 L2 H2 H2

nanovisualization computer graphics 3D graphics WebGPU WebAssmbly

In this seminar I will present how to create 3D computer graphics and visualization systems for the web, using WebAssmbly and WebGPU language specifications, which are new, bleeding-edge technologies. Previously, accelerated graphics on the web was based on JavaScript libraries, which is still very popular, but they do not offer detailed memory management and code optimization, necessary for systems requiring high memory load and high computational demands. WebAssembly and WebGPU can be compiled from the C++ or Rust code, which also allows the deployment of the same codebase either for web or for the desktop-based applications.

Lifting up female scientists

1 min read · Mon, Jun 11 2018

News

WiDS data science

Women in Data Science (WiDS) is like the Global Women's March for analytics; it's a phenomenon too big for just one city. WiDS is the largest data science conference on Earth—taking place in over 50 countries, it has attained more than 100,000 attendees and is tagged as a global movement. The annual conference aims to inspire and educate data scientists worldwide—regardless of gender—and support women in the field. This year's WiDS conference was held at Stanford University with more than 100 regional institutions all over the world participating, including KAUST.

Publishing Advanced Materials Science – How to maximize your success!

Dr. Jos Lenders, Deputy Editor, Advanced Materials, Wiley

Jul 9, 14:00 - 15:00

B3 L5 R5209

materials science wiley advanced materials

Materials science is a multidisciplinary field of research with many different scientists and engineers having various backgrounds active in it. The literature landscape consequently is populated currently by a wide range of journals which greatly differ in purpose, scope, quality, and readership. Jos Lenders, Deputy Editor of Advanced Materials, Advanced Functional Materials, and Advanced Optical Materials, will track some of the most important developments and trends in the research field and the Advanced journals program. Last year, Advanced Materials reached an Impact Factor of 21.95 and received over 8,300 submissions – and Advanced Functional Materials over 9,200. Only around 15% of all those papers made it to publication in the journal, and this rate is similar for all other Advanced journals. So, what do editors do to select the very best papers, and what can authors do to optimize their chances of having their manuscripts accepted?

Choosing the right scheme for green communications

1 min read · Sat, Aug 22 2015

News

Computer science wireless computational science engineering

Performance analysis reveals how different resource allocation schemes may impact the energy efficiency of a cognitive radio system.

Zhandos Zhakipov

Ph.D. Student, Electrical and Computer Engineering

5G and beyond wireless communication networks digital twins

Mohammed Amine Labbas

Visiting Student (former), King Abdullah University of Science and Technology

IoT Signal processing wireless communications. embedded systems

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri

Kamran Jamshaid

Postdoctoral Research Fellow, Networking Research Lab

computer networks wireless data networks pervasive computing mobile computing

Kamran Jamshaid was a Postdoctoral Research Fellow at Networking Research Lab (NETLAB) in King Abdullah University of Science and Technology (KAUST), working with Professor Basem Shihada in his research group. Research Interests Kamran's research interests included computer networks, wireless data networks, distributed systems, and pervasive and mobile computing. Selected Publications A. Showail, A. Elrasad, A. Meer, A. Daghistani, K. Jamshaid, and B. Shihada, "iFrag: Dynamic Partial Packet Recovery for Sensor Systems", ACM Journal of Wireless Networks, Vol. 20, No. 4, pp. 1-18, 2014. [ PDF] I

Kaiqiang Lin

Postdoctoral Research Fellow, Electrical and Computer Engineering

Wireless Underground Sensor Networks LoRAWAN Space-Air-Ground-Underground Integrated Networks Wireless Energy Transfer Reinforcement Learning

Kaiqiang Lin is currently a Postdoctoral Research Fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Kaiqiang Lin received his B.Eng. degree in Electronic and Information Engineering from Fujian Agriculture and Forestry University, Fuzhou, China, in 2018, and his Ph.D. degree with the College of Surveying and Geo-Informatics Tongji University, Shanghai, China, in 2024. From 2021 to

Faisal Nawab

M.S., Computer Science

big data management cloud computing wireless mesh networks

Faisal Nawab graduated with an MS degree in Computer Science from King Abdullah University of Science and Technology (KAUST) in 2011. Nawab currently works as an Assistant Professor of computer science and engineering in the Department of Computer Science and Engineering at the University of California, Santa Cruz, where he works on developing‏ systems for quick and accurate data analysis of new applications in cloud computing and Big Data. Education and Early Career Nawab earned a B.S degree in Computer Science from the KFUPM (King Fahd University of Petroleum and Minerals). He then studied

Anas Daghistani Received The Best Paper Award At (IEEE WiMob, 2013)

1 min read · Wed, Oct 9 2013

News

Wireless sensors Green communications MIMO

Anas Daghistani, MSc Alumni in the Computer Science (CS) Program at the Computer, Electrical, and Mathematical Science and Engineering (CEMSE) division received the best paper award at the International Conference on Wireless and Mobile.

Basem Shihada

Program Chair, Computer Science

Data Networks Network Systems Wireless Systems

Professor Shihada's research philosophy is to bridge the gap between theory and practice in a working system. Having worked intensively in computer networks from both theoretical and systems perspectives, Shihada has acquired a solid mathematical background and an acute sense of applying these results to build practical systems.

6G: The time is now!

1 min read · Sun, May 17 2020

News

wireless telecommunication technology 6G

While 5G companies and opponents are competing over the deployment of the first 5G platforms, the next generation of wireless telecommunication technology is being concocted right now in laboratories all around the world.

Abdulrahman Alhamed

Assistant Professor, Electrical and Computer Engineering

Radio Frequency Integrated Circuits wireless transceivers system on chip phased-arrays Satellite Communications

Dr. Abdulrahman Alhamed is an assistant professor of Electrical and Computer Engineering at KAUST, specializing in RF integrated circuits and systems. His research focuses on advancing wireless technologies for telecommunications and sensing applications

Kaiqiang Lin

Postdoctoral Research Fellow, Electrical and Computer Engineering

Wireless Underground Sensor Networks LoRAWAN Space-Air-Ground-Underground Integrated Networks Wireless Energy Transfer Reinforcement Learning

Kaiqiang Lin is currently a Postdoctoral Research Fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Kaiqiang Lin received his B.Eng. degree in Electronic and Information Engineering from Fujian Agriculture and Forestry University, Fuzhou, China, in 2018, and his Ph.D. degree with the College of Surveying and Geo-Informatics Tongji University, Shanghai, China, in 2024. From 2021 to

Account Sharing and Protection

Prof. Zhongmin Cai, Automation Department, Xi’an Jiaotong University, China

Jan 29, 12:00 - 13:00

B1 L4 R4214

account sharing account protection Xi'an Jiaotong University

Abstract Our lives and society are digitized from every perspective by computers, smart devices and various information systems. A large amount of accounts are appearing and becoming essential tools to access resources and services in this digital age. Besides the power and convenience, people are also experiencing new challenges and difficulties in their use and protection of various accounts. The first part of this talk will present a recent study on account sharing practices in the workplace. Across two surveys, we will examine the sharing process at work, probing what accounts people share

Prototypical Random Walk Learning Mechanisms for Few-shot Learning, Novel Visual Generation, and (Continual)? Zero-shot Recognition

Mohamed Elhoseiny, Associate Professor, Computer Science

Jan 30, 12:00 - 13:00

B9 L2 H2 H2

prototypical random walks zero-shot recognition

In this talk, we will define prototypical random walks, a mechanism we introduced to improve visual classification with limited data (few-shot learning), and then developed the mechanism in a conceptually different way to facilitate novel image generation and unseen class recognition tasks. More specifically, in the few-shot learning setting, we will show how we can develop a random walk semi-supervised loss that enables the network to learn representations that are compact and well-separated.

PCCFD - Predictive Complex Computational Fluid Dynamics

David Keyes, Professor, Applied Mathematics and Computational Science

May 22, 08:45 - May 24, 05:00

B9 L2 H1

CFD algorithms applied mathematics numerical analysis Computer science

The PCCFD workshop will focus on cutting-edge research in the field of algorithmic development for CFD and multi-scale complex flow simulations.