data science

On Knowledge Graph and Causality for Recommender Systems

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B1 L4 R4214

Knowledge Graph (KG) is a large-scale semantic network consisting of entities/concepts as well as the semantic relationships among them, which could be considered as a concise version of Semantic Web. Recently KG is emerging as a hot topic of knowledge discovery and management under artificial intelligence, facilitating semantic computing. Causal relation is a reflection of user behaviours with backend intention, which is related another emerging hot topic – recommendation interpretability. This talk will cover the recent research progresses in these two areas and highlight some open research challenges in recommender systems.

Digital Health: promises and pitfalls

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B9 L3 R3223

This talk will try to clarify some misconceptions about what digital health (DH) is, and what it should not be.

Designing High-Performance and Scalable Middleware for HPC, AI, and Data Science

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B9 L2 R2322 H1

This talk will focus on challenges and opportunities in designing middleware for HPC, AI (Deep/Machine Learning), and Data Science. We will start with the challenges in designing runtime environments for MPI+X programming models by considering support for multi-core systems, high-performance networks (InfiniBand and RoCE), GPUs, and emerging BlueField-2 DPUs. Features and sample performance numbers of using the MVAPICH2 libraries will be presented. For the Deep/Machine Learning domain, we will focus on MPI-driven solutions to extract performance and scalability for popular Deep Learning frameworks (TensorFlow and PyTorch), large out-of-core models, and Bluefield-2 DPUs.

Scalable Spatial Data Science for Social Scientists

Prof. Amr Magdy

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B4 L5 R5220

The proliferation of spatial data from modern and increasingly prevalent technologies has resulted in large datasets, ripe for extracting insightful knowledge that can drive many applications.

Lifting up female scientists

1 min read ·
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.

Alumni Feature: Majed Alzahrani

1 min read ·
Working in one of the largest companies in the world is a dream come true for many. For Majed Alzahrani, a former Ph.D. student who worked under the supervision of Associate Professor Xin Gao, things couldn't be better. After graduating in 2017, he quickly joined Saudi Aramco as a data scientist in its Drilling Data Science unit.

Joining forces to advance healthcare analytics in the Kingdom

1 min read ·
The recent KAUST-Prince Mohammed Bin Salman College (MBSC) Invitational Healthcare Analytics and Data Science Workshop brought together top-level clinicians, healthcare executives, representatives to discuss digitization and advancement of healthcare analytics in Saudi Arabia.

Merey Ramazanova

Ph.D. Student, Computer Science
Merey Ramazanova is a Computer Science Ph.D. Student in Image and Video Understanding Lab (IVUL) Group at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem. Education and Early Career Merey obtained her bachelor's degree in Computer Science from Nazarbayev University in Kazakhstan in 2018. She also was a Research Intern at Okinawa Institute of Science and Technology (OIST) in Okinawa, Japan in 2018. Research Interest She is focusing on her research on data science, computer vision, and machine learning. Education Profile M.S., Computer

Abdulellah Abualshour

Abdulellah Abualshour is a Ph.D. student under the supervision of Prof. Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Abdulellah was chosen to join the KAUST Gifted Student Program (KGSP). He is a graduate of Rutgers University where he received a B.S. degree in Computer Science, with a focus on data science and artificial intelligence. Regarding his early career, Abdulellah did many internships during his studies. In 2015, he worked for 1 year as a Public Relations Officer at Rutgers U niversity in