Profiles

Luca is currently a PhD candidate in the Applied Mathematics and Computational Sciences department at KAUST. Additionally, he is pursuing a dual PhD degree in Aeronautical Engineering at Politecnico di Milano. He obtained his M.Sc. in Aeronautical Engineering and his B.Sc. in Aerospace Engineering from Politecnico di Milano.

Luca's research focuses on developing computational techniques for aeroacoustics and aerodynamics. In particular, he investigates high order methods for the noise prediction of Urban Air Mobility vehicles in complex scenarios.

Najwa Alshehri is a dedicated Saudi researcher with a strong background in numerical analysis. Her academic journey began with a Bachelor of Mathematics from King Faisal University, where she graduated with honors. Supported by the prestigious King Abdullah Scholarship Program (KASP), she pursued her Master's in Applied and Computational Mathematics at Old Dominion University, USA, followed by a graduate certificate in modeling and simulation.

Najwa is a PhD candidate in Applied Mathematics and Computational Sciences at King Abdullah University of Science and Technology (KAUST), supervised by Professor Daniele Boffi. Najwa received a full governmental scholarship from the Royal Commission of Jubail and Yanbu. She also received the CEMSE Dean's List award for exceptional academic achievements and dedication to research work at KAUST. Alongside her studies, she is serving as president of the SIAM Student Chapter at KAUST.

Najwa's research focuses on the numerical approximation of partial differential equations, specifically, in fluid dynamics and interface problems such as fluid-structure interactions. Her expertise lies in finite element methods and mixed finite elements.

Pirahmad Olimjoni is a Ph.D. candidate in Applied Mathematics and Computational Science (AMCS) at King Abdullah University of Science and Technology (KAUST), specializing in Discrete Differential Geometry and Applied Mathematics. He earned his bachelor’s degree from TNU and completed his master’s studies at the Moscow Institute of Physics and Technology (MIPT).

Olimjoni's research focuses on exploring mathematical structures and developing solutions to problems arising in applied mathematics. He enjoys tackling challenging analytical problems and translating theoretical ideas into practical insights.

Salim Ksous is an MS/PhD student in the Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raúl F. Tempone at King Abdullah University of Science and Technology (KAUST).

Education Profile

• Master of Science in Applied Mathematics and Computational Sciences, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia (January 2026 – Present)
• National Engineering Diploma in Multidisciplinary Engineering, Ecole Polytechnique de Tunisie, Tunis, Tunisia (September 2022 – June 2025)
• Undergraduate Degree in Mathematics and Physics, Preparatory Institute for Engineering Studies of Monastir, Monastir, Tunisia (September 2020 – June 2022)

Early Career and Awards

• Visiting Student in Applied Mathematics, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia (October 2025 – December 2025)
• Visiting Student in Applied Mathematics, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia (February 2025 – July 2025)
• EPT Scholarship, Ecole Polytechnique de Tunisie, Tunisia (2022 – 2025)

Salim's research interests include optimal control, optimization and numerical methods.

Xiaofeng Xu is a Ph.D. candidate in the AMCS program at KAUST, under the supervision of Professor Jinchao Xu. His research lies at the intersection of traditional numerical methods for partial differential equations (PDEs) and modern machine learning approaches. He is particularly interested in developing efficient and provably convergent training algorithms for neural networks in numerical PDEs.

Xiaofeng received his Bachelor's degree in Mathematics and Computer Science with First Class Honors from the Hong Kong University of Science and Technology (HKUST), and his Master’s degree from the Pennsylvania State University.

Xiaofeng Xu research focuses on the intersection of traditional numerical methods for partial differential equations (PDEs) and modern machine learning approaches. He is particularly interested in developing efficient and provably convergent training algorithms for neural networks in numerical PDEs.

Xiuping Wang is a Ph.D. candidate in the Computational Transport Phenomena Lab and Scientific Computing and Machine Learning Lab at King Abdullah University of Science and Technology (KAUST). His research interests include designing robust and efficient numerical algorithms for electronic structure calculation and fluid flow in porous media.

Zahrah Alnasser obtained her bachelor's degree from Dammam College for Women and her master's degree in pure mathematics from the University of Illinois - Urbana-Champaign.

Real analysis and applied PDEs.

Anna Talgat is currently a Ph.D. candidate in Applied Mathematics and Computational Sciences at KAUST. She earned her B.S. in Mathematics from Fatih University, Istanbul, with high honors and a full scholarship. Anna continued her studies at KAUST, completing her M.S., focusing on stochastic geometry-based analysis of LEO satellite communication systems. Her early career includes an internship at Cambridge University’s DAMTP and teaching mathematics at Galaxy International School in Kazakhstan. Additionally, she was a Graduate Teaching Assistant at KAUST, supporting academic development and enhancing teaching methodologies.

Anna Talgat's research interests primarily focus on stochastic geometry and its applications in communication systems. Specifically, her work includes the stochastic geometry-based analysis of LEO satellite communication systems, performance analysis and optimization of wireless communications, and enhancing the efficiency and reliability of these networks. Her research aims to advance the understanding and development of satellite communication networks, which are critical for various applications, including IoT (Internet of Things) and secure data transmission in space communications.

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.

Juan Pablo Bernal Tamayo is a Ph.D. student Applied Mathematics and Computational Science (AMCS) at King Abdullah University of Science and Technology (KAUST), working in Professor Jesper Tegnér’s AI Biomedicine Lab research group. He develops dynamic models to infer gene regulatory interactions during cellular differentiation. He holds an M.S. in Applied Mathematics from the Mathmods - Erasmus Mundus program (University of L’Aquila and University of Hamburg) and dual bachelor’s degrees in Engineering Physics and Pure Mathematics from Universidad Nacional de Colombia, Medellín. During his doctoral studies, he has served as a teaching assistant for graduate courses in Numerical Optimization and Numerical Linear Algebra.

Juan develops and applies dynamical models to infer gene regulatory interactions underlying cellular differentiation. By integrating high-dimensional single-cell transcriptomics and RNA-velocity inference within a Hopfield network formalism, he derives cell-type–specific interaction parameters that capture both the stability and trajectory of cell states. He developed scHopfield, a Python package that fits continuous Hopfield model parameters to infer cell-type specific regulatory interactions. Beyond this core method, Juan investigates the decomposition of Hopfield dynamics, revealing distinct mechanistic contributions to system behavior; also he analyzes small regulatory motifs within the Hopfield framework to understand their approximation limits and motif-specific dynamics. These efforts bridge mechanistic modeling and data-driven inference to generate predictive insights into cell-fate decisions and pinpoint key regulators for experimental validation.