About Me

My academic journey began at Sharif University of Technology (SUT), where I earned my B.Sc. in Mechanical Engineering. During my undergraduate studies, I developed a strong interest in computational fluid dynamics (CFD), inspired by the intricate interplay between programming, mathematical modeling, and physical principles. This passion guided me to Northern Arizona University (NAU) for my M.Sc. in Mechanical Engineering, where I explored interdisciplinary research at the intersection of mechanics and biology.

At NAU, I focused on cardiovascular and respiratory flow modeling, where I used advanced computational tools to uncover new insights into particle dynamics and mass transport. My work resulted in several high-impact publications and collaborations with leading institutions, igniting my enthusiasm for leveraging computational approaches to solve complex problems.

Currently, I am pursuing a Ph.D. at the University of Michigan under the guidance of Dr. Aaron Towne. My research lies at the intersection of fluid dynamics, modal analysis, and high-performance computing, with a particular focus on developing scalable algorithms for analyzing dynamical systems. I designed RSVD-Δt, an efficient algorithm that computes resolvent modes with linear scalability, addressing key challenges in fluid flow modeling. This algorithm uses parallelized computing libraries (PETSc and SLEPc) and has been extended to perform harmonic resolvent analysis, enabling analysis of periodic flows. This work integrates mathematical rigor, algorithmic efficiency, and computational precision to address longstanding challenges in fluid dynamics.

Through my research, I have developed a deeper appreciation for the power of data-driven modeling, optimization, and statistical inference in uncovering patterns and solving real-world problems. My doctoral work has been guided by principles of optimization, from minimizing computational costs in large-scale simulations to leveraging data for model validation and enhancement. Additionally, my graduate coursework in machine learning, Bayesian modeling, and numerical methods has equipped me with a robust foundation for applying statistical tools and data-driven techniques to complex systems.

My ultimate goal is to bridge the gap between computational mechanics and data science, contributing to both foundational research and practical applications. I am particularly excited about opportunities to explore interdisciplinary challenges where machine learning, optimization, and physics-based modeling converge, whether in fluid mechanics, biomechanics, or broader domains.

Looking forward, I aim to contribute to groundbreaking research that leverages advanced algorithms and data-driven insights to tackle critical scientific and engineering challenges.