Seyedshahabaddin Mirjalili

Shahab Mirjalili is an Assistant Professor in Fluid Mechanics in the Engineering Mechanics Department at KTH Royal Institute of Technology in Stockholm, Sweden. Shahab joined KTH in January 2025, where he is affiliated with the Swedish e-Science Research Center (SeRC), contributing to the SeRC Efficient Simulation Software Initiative, and is also a Faculty Member of Digital Futures at KTH. 

Before his appointment, he was a Research Associate (2022–2024) and a Postdoctoral Fellow (2019–2022) at Stanford University. He earned his PhD in Mechanical Engineering from Stanford in 2019, under the supervision of Prof. Ali Mani. His dissertation focused on developing numerical methods for simulating multiphase flows and their application to studying micro-bubble generation. He also holds an MS from Stanford University and a BS from Sharif University of Technology, both in Mechanical Engineering. In 2018, he received the Gallery of Fluid Motion Award from the American Physical Society Division of Fluid Dynamics.

Shahab's research lies in the intersection of fluid mechanics, scientific computing, and machine learning. His work aims to develop and use computational methods to provide a predictive understanding of complex flow problems, including multi-physics couplings and multiphase dynamics across various scales and Reynolds numbers. In this vein, he develops physically consistent models, robust numerical schemes, and high-performance computing (HPC) software that enable high-fidelity simulations of flows involving complex multi-physics effects. These developments build upon his novel work on modeling multiphase flows and his high-performance multiphase, multi-physics software. In addition to simulations, he uses asymptotic analyses and machine learning (ML) to construct reduced-order models (ROMs) that can be used for engineering analysis, control, design, and especially optimization. Shahab is interested in a wide range of applications involving impactful problems. In particular, he is passionate about improving the predictive understanding of multiphase flows in:

• Propulsion and energy conversion/storage

• Additive manufacturing processes

• Biophysical systems

• Environmental flows