Profiles

Alessandro Astolfi is professor of applied mathematics and computational science at KAUST and a leading scholar and pioneer in nonlinear systems and control. His research focuses on the analysis and design of advanced control strategies for complex, large-scale, and interconnected systems, with applications spanning robotics, power systems, aerospace, and social and biological systems.

Before joining KAUST, he was professor of nonlinear control theory at Imperial College London and also held professorial appointments at the University of Rome Tor Vergata and Politecnico di Milano.

Astolfi’s work delivers mathematically rigorous methods and algorithms that address engineering challenges. He collaborates closely with academic institutions and industry leaders worldwide, advancing technologies that support infrastructure, energy, and intelligent automation.

An accomplished educator and mentor, Astolfi is committed to developing the next generation of engineers and researchers. At KAUST, he leads interdisciplinary research initiatives and provides master’s and Ph.D. students with opportunities to engage in high-impact projects at the forefront of systems and control theory. 

Alessandro Astolfi’s research is focused on the analysis and design of nonlinear control systems and on the development of mathematically rigorous methods that deliver quantifiable and reliable performance in the presence of uncertainty and changing operating conditions.

He has introduced general analytical frameworks and methodologies, including the Immersion and Invariance approach, homogeneous approximation techniques, discontinuous coordinate transformation–based designs, and the notion of nonlinear moment. These tools provide strong theoretical foundations for control design and system analysis.

His work enables the design of scalable, high-performance solutions across a wide range of applications. 

Xin Zheng is a postdoctoral research fellow in the Nonlinear Systems and Control group at KAUST. He received his Ph.D. in systems theory from the Academy of Mathematics and Systems Science in 2026 and his B.S. in statistics from Sichuan University in 2021.

Yasmin Selim is a postdoctoral researcher in the Nonlinear Systems and Control Research Group at KAUST. Her research focuses on nonlinear systems and control, particularly the stability and control of time-periodic and switched systems. Her current work also explores geometric control methods for enhancing stall-recovery mechanisms, applying geometric control theory to flight-dynamics problems.

She holds a Ph.D. in Aerospace Engineering from Cairo University. Her research background includes nonlinear dynamics and flight-control robustness, averaging and perturbation methods for time-periodic systems, as well as modal decomposition and hydrodynamic stability analysis of time-periodic flows.

Her work combines analytical and computational methods to study stability and control in dynamical systems, with applications in aerospace and flight dynamics.

Expertise

• Nonlinear dynamical systems, control, and stability analysis
• Flight dynamics and aerospace control
• Perturbation, averaging, and numerical methods

Research Interests

• Time-periodic and switched systems
• Geometric and nonlinear control
• Stability and control of dynamical systems

Yeva's research spans planetary dynamics, mean-field games, and control theory. She develops rheological models and analytical tools to study tidal dissipation, internal structure, and rotational dynamics of satellites and exoplanets, with applications including Enceladus, Mimas, TRAPPIST-1e, and the Pluto–Charon system.

She also works on numerical methods for mean-field games, particularly price formation problems. More recently, her research has expanded toward control theory, with a focus on realization theory, model reduction, and system identification, including their connections with mathematical rheology and nonlinear dynamical systems.

Naif is a PhD student in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), working under the supervision of Prof. Alessandro Astolfi in the area of systems and control.

His research interests center on nonlinear, robust, adaptive, and distributed control, particularly for autonomous, robotic, and networked dynamical systems operating under uncertainty, disturbances, and communication constraints.

Naif's background spans electrical engineering, robotics, autonomous systems, mathematical modeling, simulation, and experimental implementation. He has worked with classical and modern control methods including PID control, state-space methods, LQR, stability analysis, and multi-agent control. His technical experience also includes MATLAB/Simulink, Python, ROS 2, and Gazebo.

He is particularly interested in research that connects rigorous control-theoretic results with realistic physical systems, including autonomous vehicles, robotic platforms, and interconnected agents. His broader research goal is to develop control methodologies that remain reliable when models are uncertain, environments change, disturbances are present, or information is distributed across multiple systems.

Naif is a control systems researcher interested in developing rigorous control methods for physically meaningful applications. His work focuses on the analysis and control of autonomous, robotic, and networked dynamical systems, particularly in the presence of uncertainty, disturbances, and communication constraints.

Hajid Alsubaie's research interests include model reduction for deterministic, stochastic, and nonlinear dynamical systems. 

His theoretical work focuses on model-free, decentralized multi-agent reinforcement learning in discounted Markov potential games, where agents learn from local bandit feedback without a common prior or explicit communication. Value-function learning and policy updates run on two timescales via asynchronous actor-critic methods. The aim is large-scale asymptotic and convergence analysis under weaker assumptions, with robustness and safety cast as mathematical constraints that yield provable guarantees.