Welcome

I am Mohammad Pasande, a researcher specializing in Optimization, Artificial Intelligence, and Control Theory. My focus lies in the rigorous exploration of mathematical complexities and the development of precise algorithmic solutions to address them.

About Me

I obtained my M.Sc. in Electrical Engineering (Control major) from the University of Tehran, Iran, and currently serve as a Research Assistant at CAVLab, under the supervision of Dr. Reshad Hosseini & Dr. Babak N. Araabi.

During my master’s studies, I dedicated my attention to the Optimization, Methodology of Machine Learning, and its applications in Deep Learning. With a strong interest in the applied mathematical aspect of problems, I have focused my research on solving problems employing numerical optimization. As a result, I have contributed to two major works—one on parameter estimation for Gaussian mixture models with a large number of components and the other on a Causal Discovery framework for the dynamical modeling of brain processes. These experiences have deepened my knowledge and reinforced my commitment to pushing the boundaries of applied mathematics and algorithmic problem-solving within the domains of Optimization, Machine Learning, and Control Theory.

During my undergraduate studies, I dedicated myself to enhancing my comprehension of control theory, with a particular focus on areas like Robust Control and System Identification. My bachelor’s thesis, completed under the guidance of Dr. Mehdi Rahmani, reflected my commitment to deepening my knowledge in these domains.

Throughout my academic years, I actively sought opportunities to bridge the gap between theory and practice in real-world problems. I participated in various projects, delving into areas such as time series forecasting, sensorless calibration, and the design of control routines. These experiences have not only strengthened my theoretical understanding but have also equipped me with practical skills.

Interests

Optimization, Machine Learning, Control Theory, Reinforcement Learning, Game Theory, Optimal Decision Making (Control,) and Causality.