Profiles

Diego Augusto Silva is a Ph.D. candidate at the Communications and Computing Systems Laboratory (CCSL) within the Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. His research focuses on event-based vision, object recognition, deep learning, and neuromorphic computing.

He received his Bachelor’s degree in Electrical Engineering from the Federal University of São João del-Rei (UFSJ), Brazil, in 2018, and his M.Sc. degree in Electronic Engineering and Computing from the Technological Institute of Aeronautics (ITA), Brazil, in 2020, with a specialization in Electronic Devices and Systems.

Diego’s technical expertise spans both theoretical and practical aspects, with significant experience in FPGA prototyping and asynchronous logic design. His innovative work has been recognized through two hackathon awards in his research areas, highlighting his ability to translate advanced neuromorphic computing and event-based vision concepts into real-world solutions.

Diego's research interests include the digital logic design of algorithms and event-based cameras.

Eliza Rezvanova obtained her Ph.D. in Applied Mathematics and Computational Science at King Abdullah University of Science and Technology (KAUST), advised by Professor Raúl F. Tempone. She has presented her research at workshops hosted by RWTH Aachen University, Hong Kong Polytechnic University, and KAUST. Before joining KAUST, she worked in the Russian oil industry at a Rosneft subsidiary. She received her M.S. in Applied Mathematics and Computational Science from KAUST in 2021 and a Specialist degree in Mathematical Methods in Economics from Ufa State Petroleum Technical University, Russia, in 2010.

Eliza's research focuses on stochastic optimal control, dynamic programming, and numerical methods, with applications to renewable energy and partially observed systems.

Eman Kabbas is a Saudi researcher specializing in Bayesian statistics and modeling. She earned her Bachelor’s degree in Mathematics from Imam Abdulrahman bin Faisal University, then continued her studies in the United States through the King Abdullah Scholarship Program, obtaining a Master’s degree in Applied Mathematics from the University of North Carolina. Supported by scholarships from KAUST and the Royal Commission of Jubail and Yanbu, Eman is currently a PhD student in Applied Mathematics and Computational Science (AMCS) at King Abdullah University of Science and Technology (KAUST), supervised by Professor Håvard Rue. Beyond her research, Eman is a Mathematics and Statistics faculty member at Jubail Industrial College under the Royal Commission of Jubail and Yanbu, and co-founder of Sorat Alardh, a startup advancing environmental monitoring and climate analytics. She envisions combining theory and real-world application by transforming scientific research into practical tools that enhance environmental resilience and data-driven innovation in Saudi Arabia. Eman enjoys reading, learning new languages, playing the piano, and exploring strategic games like chess.

Eman Kabbas's research interests focus on developing and applying spline models in non-parametric regression. She addresses the limitations of splines in prediction tasks with insufficient data by proposing a spline model suitable for both regular and irregular observations, leverages Bayesian techniques to ensure efficient modeling and reliable predictions.

Esmail Abdul Fattah holds a Ph.D. in Statistics from KAUST, where he is now a postdoctoral researcher in high-performance computing. His work connects the two fields to scale Bayesian inference, bringing HPC to approximate methods such as INLA. This includes GPU-accelerated sparse factorizations and selected inversion for structured matrices through the sTiles framework, and extending INLA to non-sparse models. He also works on applications in spatial and spatio-temporal disease mapping. 

More information: https://esmail-abdulfattah.github.io

• Approximate Bayesian inference for latent Gaussian models
• Sparse and dense direct solvers, Cholesky factorization, and selected inversion
• GPU-accelerated and parallel numerical linear algebra, including tile algorithms
• Statistical software development

Fabio Credali is a postdoctoral fellow in Prof. Daniele Boffi's NumPDE group at KAUST. He obtained the Ph.D. in Applied Mathematics and Computational Sciences at KAUST in 2023, in cotutelle with the joint Ph.D. program in Computational Mathematics of the Università di Pavia (Italy) and the Università della Svizzera italiana (Switzerland). After a one-year postdoc at IMATI–CNR in Pavia, he returned to KAUST, where he currently holds a postdoctoral position. His research concerns the numerical approximation of PDEs, with emphasis on fluid-structure interaction problems, finite element and virtual element methods.

Numerical approximation of fluid-structure interaction problems, Finite Element methods (FEM), Virtual Element methods (VEM).

Fahad S. Alqurashi is a Ph.D. candidate in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. His research focuses on advanced wireless communication technologies for connecting underserved and remote regions, emphasizing cost-effective and high-capacity solutions. His work explores Free Space Optics (FSO), TV White Space (TVWS), and hybrid RF/mmWave systems to achieve point-to-point data rates exceeding 10 Gbps, with a particular interest in maritime and rural connectivity.

Fahad holds a Master of Science in Electrical Engineering from KAUST, where his thesis focused on modeling FSO communication channels for next-generation deployment scenarios. He completed his Bachelor of Science in Electrical Engineering (Electronics and Communication track) at Umm Al-Qura University in Makkah, Saudi Arabia.

Fahad has led strategic connectivity projects in collaboration with global technology leaders such as Google Taara, Meta, Cambium, and national operators like Zain and STC. He is currently spearheading national-scale initiatives with the Communication, Space and Technology Commission (CST), Red Sea Global, and Neom to deliver resilient, sustainable, and scalable wireless infrastructure—including projects connecting offshore islands and rural villages using hybrid FSO/RF links powered by solar systems.

His research has been presented at major international venues including IEEE ICC and the Optical Wireless Communication Conference, and he is an active member of IEEE and the Optical Wireless Communication community. Fahad’s interdisciplinary work supports Saudi Vision 2030 and reflects a strong commitment to impactful digital transformation through cutting-edge wireless innovation.

Alongside the Saudi Vision 2030, Fahad’s interest lies in supporting the future of 5G and 6G. Because of this, he focuses on wireless communication systems, especially in free space optical communication (FSO).

Fares Fourati is a Ph.D. candidate in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST). His work has been published in leading AI and machine learning venues, including ICML, AAAI, AISTATS, EMNLP, and ECAI.

He has received several distinctions, including consecutive CEMSE Dean’s List Awards, first place in the ACM-SIAM Student Competition (2024), and first place at the Marconi Society Connectivity Summit (2021). Fares earned his Diplôme d’Ingénieur from École Polytechnique de Tunisie in 2020 and an M.S. in Electrical and Computer Engineering from KAUST in 2022.

In addition to his research, he has been actively involved in teaching and mentoring in AI and machine learning at KAUST and across Saudi Arabia. He is also the author of To What End? Meditations on AI and Humanity, reflecting his broader interest in the philosophical implications of AI.

Fares' research focuses on reinforcement learning, deep learning, large language models, and optimization, with an emphasis on advancing the foundations of intelligent systems.