Dr. Sasan Azizian

• Computer Science
• Software Engineering
• Artificial Intelligence
• Data Science

Ph.D. in Computer Science (Artificial Intelligence)
University of Nebraska-Lincoln

M.S. in Computer Science
University of Nebraska at Omaha

M.S. in Computer Engineering
Tehran science and research

B.S. in Computer Engineering

Over a decade of combined academic and industry experience in software engineering, artificial intelligence, and data science. Former software engineer and researcher with expertise in building intelligent systems and mentoring students across computer science and business analytics disciplines.

• Software Engineer
• Deep Learning for microRNA-protein interaction prediction
• Object-Relational Mapping Optimization
• AI Applications in Healthcare and Wearables
• Generative AI and Trust in Technology
• Artificial Intelligence
• Machine Learning
• Software Architecture
• Deep Learning
• Health Informatics

S. Azizian and J. Cui, “DeepMiRBP: A Hybrid Model for Predicting microRNA-Protein Interactions Based on Transfer Learning and Cosine Similarity,” BMC Bioinformatics, vol. 25, no. 1, 2024, pp. 381.

S. Azizian, E. Rastegari, and H. Bagheri, “Leveraging Machine Learning for Optimal Object-Relational Database Mapping in Software Systems,” in Proceedings of the 1st ACM International Conference on AI-Powered Software, 2024.

S. Azizian and J. Cui, “Discovery of Small RNA Sorting Determinants Through Hybrid Deep Learning Model,” in RECOMB/ISCB Conference on Regulatory and Systems Genomics with DREAM Challenges, 2023.

S. Azizian, E. Rastegari, B. Ricks, and M. Hall, “Identifying Personal Messages: A Step Toward Product/Service Review and Opinion Mining,” in Proceedings of the International Conference on Computing and Information Technology, 2017.

E. Rastegari, S. Azizian, and H. Ali, “Machine Learning and Similarity Network Approaches to Support Automatic Classification of Parkinson’s Disease Using Accelerometer-Based Gait Analysis,” in Proceedings of the International Conference on Bioinformatics & Computational Biology (BIOCOMP), 2016.

My research focuses on the intersection of artificial intelligence, bioinformatics, and software engineering. I apply deep learning and transfer learning techniques to analyze RNA-protein interactions and model post-transcriptional gene regulation. In software engineering, I explore architecture-driven approaches to optimize object-relational mappings based on performance and scalability. I am also actively investigating the role of generative AI in education, user trust in AI systems, and the use of sensor-based technologies in health informatics.