Profiles

Arved Bartuska obtained his bachelor's and master's degrees at the University of Vienna. He received his Ph.D. in 2025 at RWTH Aachen University and is currently a postdoctoral fellow at KAUST.

Arved Bartuska's research interests include applied mathematics, stochastic analysis, Bayesian optimal experimental design, and uncertainty quantification.

Dr. Daria Sushnikova is a Postdoctoral Research Fellow at KAUST specializing in fast algorithms for large-scale scientific computing. She earned her Ph.D. in Mathematical Modeling and Numerical Methods at the Institute of Numerical Mathematics, Russian Academy of Sciences, under Prof. Ivan Oseledets. Her research spans numerical linear algebra, hierarchical matrices, and high-performance computing, with contributions such as the FMM-LU solver, Compress-and-Eliminate factorization, and H2-MG algorithm. She received the Rising Stars in Computational & Data Sciences Award (2020).

David Jesus received his Ph.D. from the University of Coimbra in Portugal under the supervision of José Miguel Urbano and Edgard Pimentel in 2023. Then completed 2 years of postdoc in Bologna before joining KAUST in 2025.

Georgios Grekas is a Postdoctoral Research Fellow in the Applied PDE Group of the Applied Mathematics and Computational Science (AMCS) program, within the CEMSE Division at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. He received his Ph.D. degree in Applied Mathematics from the University of Crete in 2019. Part of his Ph.D. studies took place at the University of Sussex from March 2016 to March 2018. From 2019 to 2022, he held a postdoctoral position at the Department of Aerospace Engineering and Mechanics at the University of Minnesota. He then worked as a postdoctoral researcher at the Institute of Applied and Computational Mathematics (IACM) of FORTH, Greece. He joined KAUST and the Applied PDE Group in March 2024.

Majed Sofiani is a Postdoctoral Research Fellow at the Applied PDE group of the Applied Mathematics and Computational Science program (AMCS) of CEMSE division at the King Abdullah University of Science and Technology, Saudi Arabia. He received his Master's and PhD degrees from the department of mathematics at the University of Kansas in 2024. He joined KAUST at the AppliedPDE group in July 2024.

His research interests lie in the area of non-linear partial differential equations and dynamical systems. In particular his research focuses on the Eriksen-Leslie model for nematic liquid crystals. 

Melih Ucer is primarily interested in the analysis of the mean field game PDE system, which is a PDE system consisting of a Hamilton-Jacobi-Bellman equation and a Fokker-Planck equation, especially in terms of weak solution notions in degenerate cases.

Nuno J. Alves is a postdoctoral researcher in the Free Boundary and Interface Problems group, led by Prof. José Miguel Urbano. He earned his PhD in Applied Mathematics and Computational Science from KAUST in 2023, under the supervision of Prof. Athanasios E. Tzavaras. Before returning to KAUST in March 2026, he was a postdoctoral researcher at the University of Vienna, working with Prof. Peter Markowich. His research lies broadly in mathematical analysis, with particular emphasis on functional analysis, harmonic analysis, the calculus of variations, and the analysis of partial differential equations.

Nuno Alves’ research focuses on mathematical analysis and its applications to nonlinear partial differential equations. His interests include functional and harmonic analysis, compactness and convergence in function spaces, fractional integral operators, asymptotic problems for PDEs, and Gamma-convergence methods in the calculus of variations. His work also involves fluid and kinetic models, including Euler-type, BGK-type, and Vlasov-type equations, with an emphasis on stability, singular limits, weak-strong uniqueness, and relative entropy methods.

Roberto earned a B.Sc. degree in Mechanical Engineering from University of Padova in 2014. Then he obtained a M.Sc. in Applied Mathematics at Politecnico di Torino in 2017. Subsequently he earned a Ph.D. in pure and applied mathematics at Politencnico di Torino under a Marie Curie Horizon 2020 program in 2021.

Roberto's research involves numerical analysis of high-order algorithms for solving partial, ordinary, fractional, and integral equations. He is also interested in approximation theory in the context of numerical methods for partial differential equations. Furthermore, he also works on multiphysics models, coupled algorithms, and randomized linear algebra.

Thomas Trigo Trindade is a Postdoctoral Fellow in the Stochastic Numerics group at KAUST. He specialises in the intersection of uncertainty quantification, model order reduction and numerical methods.

He obtained his PhD in 2025 under the supervision of Prof. Fabio Nobile at EPFL, focusing on model order reduction techniques applied to parametrised dynamical systems and filtering problems. Thomas was selected as a KAUST Global Postdoctoral Fellow for the 2026 Cohort of the KAUST Global Fellowship Program.

Thomas's research interests focus on data assimilation for high-dimensional problems, with emphasis on dynamical low-rank methods and Kalman and particle filtering.

Dr. Vladimir Pimanov is a Postdoctoral Fellow at KAUST working on computational plasma physics and high-performance scientific computing under Prof. David Keyes. He earned his Ph.D. at Lomonosov Moscow State University.

Yang Liu is a Postdoctoral Fellow at the Stochastic Numerics Research Group (STOCHNUM) of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). His primary research interests involve uncertainty quantification, Monte Carlo methods, and finite element methods.

Abderrahmene Ben Romdhane 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, École Polytechnique de Tunisie, Tunis, Tunisia (September 2022 - September 2025).|
• Undergraduate Degree in Maths and Physics, Preparatory Institute for Engineering Studies of el Manar, Tunis, Tunisia (September 2020 - June 2022).

Early Career

• Visiting Student in Applied Mathematics, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia (October 2025 - December 2025)
• Visiting Student in Mathematics, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, (February 2025- August 2025).

Abderrahmene's research interests include Numerical Analysis, Computational Finance, Uncertainty Quantification.

AbdulRahman Mohammad Alharbi is a Ph.D. candidate in Applied Mathematics at King Abdullah University of Science and Technology (KAUST), working under the supervision of Professor Diogo Gomes. His primary area of research focuses on first-order mean-field games on bounded domains, with an emphasis on entry-exit flow constraints, contact-set conditions, and the analytical challenges posed by nonstandard boundary behavior.

AbdulRahman received his M.S. degree in Applied Mathematics from KAUST in 2019, with a thesis exploring the Lp-integrability of Green’s functions for elliptic differential operators. Prior to that, he received his BA in Mathematics from Rutgers University with a minor in Physics in 2017.

In addition to his research, AbdulRahman has held teaching roles at KAUST and the Islamic University of Madinah, assisting in courses on calculus, linear algebra, and partial differential equations at both undergraduate and graduate levels. He has also participated in and presented his research at international and local conferences, including the American Institute of Mathematical Sciences (AIMS ) Conference on Dynamical Systems, the conference of the S ITE center hosted by NYU Abu Dhabi, and the CMSA conference hosted by the Saudi Association of Mathematics (SAM).

His most recent work examines the well-posedness of MFG systems with nonstandard mixed boundary conditions, variational formulations of MFGs, the local regularity of weak solutions, and the monotone operator structure underlying such systems. These topics lie at the interface of partial differential equations, variational analysis, and optimal control.

First-order mean-field games, linear and quasi-linear elliptic equations, the transport equation, and free boundary problems.  

Ahmed Abdali graduated from the University of Bahrain with a BSc in Mathematics in 2022.

Nonlinear PDEs and applications.

Ms. Aseel AlNajjar is a Ph.D candidate in the AppliedPDE group of the Applied Mathematics and Computational Science program (AMCS) of CEMSE division at the King Abdullah University of Science and Technology, Saudi Arabia. She graduated from Taibah University in 2019 with a bachelor's degree in Mathematics and received a Master's degree in Applied Mathematics from KAUST in 2021. During 2018 Ms. AlNajjar was a summer research intern in the AppliedPDE group. 

Eliza Rezvanova is a Ph.D. candidate 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.

Grigory Malinovsky is a Ph.D. candidate in Applied Mathematics and Computational Science at King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtárik in the Optimization and Machine Learning Lab.

Grigory holds a Master of Science in Applied Mathematics and Computational Science from KAUST and a Bachelor's degree in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology, where he completed his thesis under the guidance of Boris Polyak.

During his Ph.D., he completed a six-month internship at Samsung R&D Institute UK, and collaborated during research visits with Sebastian Stich at CISPA, as well as with Samuel Horváth and Eduard Gorbunov at MBZUAI. His work has been published in leading machine learning conferences such as NeurIPS, ICLR, ICML, AISTATS, AAAI, and UAI. Alongside his research, he has contributed to teaching graduate-level optimization courses and actively mentored research interns, leading to multiple joint publications.

Grigory's research focuses on optimization and its applications in machine learning, particularly federated learning, distributed optimization, and deep learning theory.

Hanan Albarqi obtained a master’s degree in Pure Mathematics from the University of Illinois at Urbana–Champaign in 2022 and a bachelor’s degree in Mathematics from King Khalid University in 2017.

Analysis and applied PDEs.

Hikmatullo Ismatov earned a Bachelor's degree in Applied Mathematics and Computer Science from Lomonosov Moscow State University (2017-2021). He joined KAUST as an MS/PhD student in 2023. Before starting his graduate studies, Hikmatullo completed an internship at KAUST in the same year.

Hikmatullo Ismatov's research centers on nonlinear PDEs, particularly the Infinity-Laplace equation. This class of equations has significant applications in understanding various optimization problems and phenomena, such as tug-of-war games and image processing. His work aims to contribute to the theory and applications of nonlinear PDEs, particularly through rigorous mathematical analysis and computational approaches.

Juan Pablo Muñoz Díaz is a Ph.D. candidate under the supervision of Prof. Jesper Tegnér (KAUST), co-supervised by Dr. Narsis Kiani (karolinska institutet). His research merges machine learning and bifurcation theory to uncover hidden variables in nonlinear biological systems. He has conducted collaborative research at the University of Cambridge and presented at leading international conferences, including the International Conference on Systems Biology and the SIAM Conference on Dynamical Systems.

Juan Pablo is interested in learning Generative Causal Models from Sparse Temporal Observations during Cellular Reprogramming. His research focuses on identifying efficient algorithms for generation of ensembles of dynamical models and using supervised deep learning algorithms for pattern discovery in large-scale simulation data-sets. These steps aiming to and motivated both by our quest of asking fundamental questions of relevance to human nature and discovery of transformative intelligent technologies inspired from nature.