My expertise lies at the intersection of scientific visualization, big data analytics, visual analytics, artificial intelligence, and high-performance computing. For more than 15 years, I have worked with researchers, students, and multidisciplinary teams to develop computational and visual solutions that transform complex scientific data into meaningful insights.
Today, as a Lead Senior Research Scientist at KAUST's Visualization Core Lab, I lead research, collaborations, and technical initiatives that help researchers use advanced visualization, data analytics, AI, and HPC technologies to solve challenging scientific problems.
CORE AREAS OF EXPERTISE
Designing visual representations and interactive systems for complex scientific and engineering data, enabling researchers to explore phenomena that are difficult to understand through conventional analysis.
Designing computational approaches for large, complex, data-intensive workloads and transforming those datasets into scalable analytical workflows and intuitive dashboards.
Combining computational analysis with interactive visualization to help researchers move from data → patterns → understanding → insight.
Applying AI and machine learning as part of scientific computing and visual analytics workflows—not simply as standalone models, but as tools that help researchers extract knowledge from complex data.
Developing and supporting visualization and data-analysis workflows that take advantage of high-performance computing infrastructure.
Using interactive 3D environments and immersive technologies to provide researchers with new ways to explore and understand complex scientific data.
MULTIDISCIPLINARY RESEARCH DOMAINS
My work is inherently interdisciplinary. I collaborate with researchers across scientific domains where computation, data, and visualization play a central role in discovery.
Visualization and analysis of complex climate, ocean, atmospheric, and environmental datasets, including large-scale simulation data and multiscale phenomena.
Visual analytics and computational approaches for biological and genomic datasets, helping researchers explore complex relationships and patterns in high-dimensional data.
Visualization and analysis of simulation and engineering data to support understanding of complex physical processes and computational models.
Scientific visualization of large-scale oceanographic simulations, including complex flow fields, internal waves, and multivariate ocean phenomena.
Development of AI-enabled analytical and visualization workflows that help researchers integrate computational intelligence with human exploration and interpretation.
Visual analytics of large-scale cybersecurity and web data to identify suspicious patterns, detect anomalies, investigate potential fraud, and support early threat detection and prevention.
CORE SKILLS
RESEARCHER-CENTERED TECHNOLOGY
The objective is not simply to deliver software or visualizations, but to create tools and workflows that let researchers ask new questions, investigate complex phenomena, and uncover insights that would otherwise remain hidden.
CAPABILITY BUILDING
Through the Visualization Core Lab at KAUST, I combine research collaboration, technical consultation, training, mentoring, and reusable research workflows.
The goal is to leave researchers and research teams more capable than when they started—with the knowledge, tools, and workflows needed to continue exploring their data independently.
An important part of my work is making advanced technologies accessible to researchers who may not be experts in big data, visualization, AI, data science, or HPC.
I design and deliver hands-on training and workshops covering areas such as:
The emphasis is on practical, researcher-oriented learning—helping participants understand not only how to use a technology, but when and why it can improve their research.
RESEARCH FOUNDATION
Academic research, industry research, and more than a decade of research technology leadership at KAUST have shaped the way I approach complex data and computing problems.
Lead Senior Visualization Scientist. Scientific visualization, visual analytics, data science, advanced computing, HPC/GPU workflows, collaboration and training.
Assistant Professor. Research in big data analytics, big data systems and large-scale visualization alongside teaching and mentoring.
Research internship focused on analyzing and visualizing large and complex brain network datasets through interactive visual analytics.
Research internship focused on large-scale cybersecurity and web data, visual analytics, suspicious activity, fraud and early detection.
THE COMMON THREAD
Whether working with scientific simulations, big data, cybersecurity data, brain networks, genomic datasets, or complex environmental systems, the objective remains the same:
Turn complex data into something researchers can explore, understand, and use to make discoveries.
WHAT I BRING
Understanding the research question, scientific context, data characteristics, and computational challenges before selecting the technology.
Combining visualization, data science, big data, AI, HPC, GPU computing, and interactive systems to develop practical research solutions.
Working directly with scientists and domain experts to translate scientific questions into computational, analytical, and visual approaches.
Leading teams, research collaborations, technical initiatives, and the development of specialized research capabilities.
Building knowledge and capability through workshops, hands-on training, mentoring, and direct collaboration.
Designing systems that do more than display data—they help researchers see patterns, investigate hypotheses, and discover insights.