About Me

Mohammadreza Ghorvei

I hold a master’s degree in electrical engineering (Control) with a GPA of 4/4 from Tarbiat Modares University, which is one of the top 5 universities and the best graduate university in Iran. I am advised by Dr. Mohammad TH Beheshti and Dr. Amin Ramezani. My research is focused on deep transfer learning for bearing fault diagnosis under changing working conditions.

My background comes from control engineering, I completed my undergraduate program in electrical engineering (control) at the Hamedan University of Technology. I am supervised by Dr. Hadi Delavari. My bachelor's thesis involved designing and producing an injection pump to control injection flow rates.

Academic Experience

As a deep learning and machine learning enthusiast, I'm particularly interested in their applications to real-world problems. While pursuing my master's degree, I gained experience processing image and signal data. As part of the first studies, I applied intelligent models to medical images in an attempt to predict diseases like covid-19 and breast cancer. Furthermore, based on my master's thesis, I mainly focused on signal processing for unsupervised fault diagnosis. Several topics have been covered in my research in this field. The study covers unsupervised fault diagnosis for bearing and gearbox components, as well as synthetic to real frameworks in bearing fault diagnosis. Lastly, I would like to note that in these researches, I investigated transfer learning, domain adaptation, graph neural networks, and signal and image feature extraction methods to be used in proposed models.

Research Interest in Deep Learning

• Domain Adaptation

• Graph Neural Networks

• Physics-informed neural networks in digital twins (Hybrid Learning)