Dr. Amin Golzari Oskouei is a dedicated researcher and educator with a strong Artificial Intelligence and Robotics background. He earned his Ph.D. in Artificial Intelligence and Robotics from Tabriz University, Iran, where he focused on developing novel approaches to deep clustering. His doctoral thesis, titled “Efficient Deep Clustering with the Weight of Representations and the Help of Neighbors,” addresses critical challenges in existing deep clustering methods, such as feature drift and the uniform weight assumption on generated representations. Dr. Golzari Oskouei’s innovative solutions involve an automatic local representation weighting strategy, leveraging both sample and neighbor information to enhance representation learning. His research demonstrates a keen understanding of the intricacies of deep learning models for clustering, leading to the development of a more efficient and effective model compared to state-of-the-art methods.
Before his Ph.D, Dr. Golzari Oskouei completed a Master’s degree in Information Technology Engineering, during which he significantly contributed to the improvement of the Fuzzy C-Means Clustering Algorithm. His master’s thesis, “Improving Fuzzy C-Means Clustering Algorithm by Assigning Local Feature Weights and Cluster Weights,” showcased his expertise in addressing feature importance and initialization sensitivity issues in the widely used FCM algorithm. With a proven track record of academic excellence, Dr. Golzari Oskouei has received several honors, including being the first-ranked student in both his Bachelor’s and Ph.D. programs. Currently, he serves as a lecturer at the Urmia University of Technology and Azarbaijan Shahid Madani University, contributing to the education and development of future professionals in Artificial Intelligence and Machine Learning.
In addition to his academic pursuits, Dr. Golzari Oskouei has authored numerous publications in prestigious journals, including “Chaos, Solitons & Fractals” and “Information Sciences.” His research contributions extend to conferences, workshops, and even translations of relevant books in the field, highlighting his commitment to disseminating knowledge and advancing the field of machine learning and pattern recognition.
