Rate My Professor Seyedamin Pouriyeh

Seyedamin Pouriyeh is an Associate Professor of Information Technology in the Department of Information Technology at Kennesaw State University, part of the College of Computing and Software Engineering. He earned his Ph.D. in Computer Science from the University of Georgia in August 2018 under the supervision of Dr. Gong Cheng and his M.Sc. in Information Technology Engineering, specializing in e-commerce, from Shiraz University in January 2009. Pouriyeh's research centers on federated machine learning, cybersecurity, and health informatics. His work addresses privacy-preserving techniques for IoT security, intrusion detection systems, AI applications in healthcare diagnostics, and secure communication in distributed learning environments. With over 7,299 citations on Google Scholar, his contributions have garnered significant recognition in these fields.

Pouriyeh has authored numerous peer-reviewed publications in leading journals and conferences. Key works include 'Federated-Learning-Based Anomaly Detection for IoT Security Attacks' (2021), 'A Survey on Security and Privacy of Federated Learning' (2020), 'Secure Smart Communication Efficiency in Federated Learning: Achievements and Challenges' (2022), 'A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques' (2017), and 'Ontology Summarization: Graph-Based Methods and Beyond' (2019). More recent publications feature 'AI-Guided Binding Mechanisms and Molecular Dynamics for MERS-CoV' (2026), 'Enhancing Alzheimer’s Diagnosis Through Spontaneous Speech Recognition: Deep Learning Approach with Data Augmentation' (2025), and 'Privacy-Preserving Federated Learning-Based Intrusion Detection System for IoHT Devices' (2024). At Kennesaw State University, he teaches courses such as IT4883 and actively engages students through his research lab, focusing on machine learning and cybersecurity projects. His expertise spans blockchain for secure systems, deep learning in protein structure prediction, and anomaly detection in smart healthcare.