Dr. Sasan Azizian

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

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
Azad University, Tehran science and research

B.S. in Computer Engineering
University of Tehran

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.

• Artificial Intelligence
• Software Engineering
• Database Design and Optimization
• Machine Learning
• Data Analytics
• Bioinformatics
• Health Informatics
• Generative AI
• Software Architecture

AWS Certified Solutions Architect – Associate

Microsoft Certified: Azure AI Engineer Associate

Linux Professional Institute Certification (LPIC)

Cisco Certified Network Associate (CCNA)

Graduate Student Excellence in Mentoring Award, University of Nebraska–Lincoln (2023)

Graduate Research and Creative Activity (GRACA) Award, University of Nebraska at Omaha (2017)

1. TriORM: Workload-Aware Neural–Symbolic Multi-Objective Optimization for ORM Mapping Design. Proceedings of the 3rd ACM International Conference on AI-Powered Software (FSE-AIware), 2026.
2. Neural-Symbolic, Benchmark-Free Optimization for ORM Database Design. Proceedings of the 3rd ACM International Conference on AI-Powered Software (FSE-AIware), 2026.
3. Y-Map: A Multi-Encoder Y-Architecture for Predicting Performance-Aware Object-Relational Mappings. Proceedings of the IEEE/ACM 48th International Conference on Software Engineering, 2026.
4. Reframing Human-AI Collaboration and Digital Literacy in the Age of Generative AI. Proceedings of the Hawaii International Conference on System Sciences (HICSS-59), 2026.
5. DeepMiRBP: A Hybrid Model for Predicting microRNA-Protein Interactions Based on Transfer Learning and Cosine Similarity. BMC Bioinformatics, 25(1), 381, 2024.
6. Leveraging Machine Learning for Optimal Object-Relational Database Mapping in Software Systems. Proceedings of the 1st ACM International Conference on AI-Powered Software (AIware), 2024.
7. Discovery of Small RNA Sorting Determinants through Hybrid Deep Learning Model. 15th Annual RECOMB/ISCB Conference on Regulatory and Systems Genomics with DREAM Challenges, 2023.
8. Machine Learning and Similarity Network Approaches to Support Automatic Classification of Parkinson’s Disease Using Accelerometer-Based Gait Analysis. Proceedings of the Hawaii International Conference on System Sciences, 2019.
9. Identifying Personal Messages: A Step Towards Product/Service Review and Opinion Mining. 2017 International Conference on Computational Science and Computational Intelligence, 2017.
10. A Hierarchical Learning Model for Extracting Public Health Data from Social Media 2017

Research centers on the intersection of artificial intelligence, software engineering, and biomedical informatics.

Current work explores AI for software engineering, intelligent ORM and database design optimization, generative AI and trust, human–AI collaboration, and sensor-based health analytics.

Applies machine learning, deep learning, and neural-symbolic methods to both software and health-related data problems.

Professional memberships include ACM, ACM SIGSOFT, and IEEE. Teaching areas include software programming, software testing and quality, server-side development, databases, artificial intelligence, and Software Architecture.