About Me

I’m Amirhossein Rezaei, a recent Physics graduate from Shahid Beheshti University. My peers often refer to me as the “adjustable wrench” due to my adaptability and eagerness to tackle diverse challenges in physics and computation.

Research Journey

My research experience began in my second year, collaborating with Dr. Siamak Sadat Gousheh on hypermagnetic fields and matter-antimatter asymmetry. Our work, published in Physical Review D, introduced me to the power of numerical methods in theoretical physics.

In my third year, I led a project on “A Deep Learning Framework for Collapse Prediction of Structures Using 1D-CNN for Earthquake Time Series Classification with joint Bayesian hyperparameter optimization and Neural Architecture Search.” This ongoing work, currently under review, reflects my interest in applying machine learning to complex physical systems.

Current Focus

My academic path led me to quantum information and computation in my fourth year. I’m currently working with Dr. S.M. Halataei in the Quantum Information and Computation Group at Shahid Beheshti University. Our research on fully connected Ising Hamiltonians has potential applications in optimization problems like Quadratic Unconstrained Binary Optimization (QUBO), with implications for solving NP-Hard problems such as k-SAT and the Traveling Salesman Problem.

Approach and Skills

I strive to approach problems from multiple perspectives, leveraging my background in physics and computational skills. My goal is to contribute meaningfully to the intersection of physics and computation, focusing on practical applications and real-world impact.

I’m committed to continuous learning and growth, always ready to explore new concepts and technologies. Whether it’s quantum mechanics, machine learning, or complex system optimization, I embrace the challenge of expanding my knowledge and skills.

Feel free to explore my projects and publications for more insight into my work and interests. I welcome feedback and collaboration opportunities.