Bio & Experience

My name is Naeem Shahabi Sani, and I am a PhD candidate in Computer Science at the University of Oklahoma. I work under the supervision of Dr. Lan (advisor) and Dr. Hougen (co-advisor). Since 2016, I have been deeply interested in bio-inspired optimization algorithms. Throughout the years, this passion has developed into expertise, particularly in the application of bio-inspired algorithms to feature engineering, dimension reduction, recommendation systems, and other complex machine learning methods. I hold my bachelor's degree in Computer Engineering-Software and my master's in Artificial Intelligence and Robotics from Azad University (Science and Research Branch, Tehran, Iran). During this period, I discovered the profound potential of nature-inspired methods for solving complex problems. My research has included work on multi-objective ant colony optimization for community detection, as well as developing new approaches to trust-based recommender systems.

Today, my work focuses on optimizing the performance of classifications and robustness by creating and enhancing advanced bio-inspired methods, and by exploring the role of randomized algorithms in machine learning. I’m extremely excited about the future of this field, and I’m motivated to help develop solutions that help researchers and professionals address real-world data challenges more effectively.

Research Interests

• Bio-inspired optimization algorithms
• Feature engineering
• Swarm Intelligence
• Randomized algorithms
• Machine learning
• Active learning

Active Researches

• A Dragonfly and Ants at Work: A Hybrid Bio-Inspired Framework for Active Learning in Community Detection
  Status: Paper in progress (January 2025)
  Abstract: I introduce a bio-inspired hybrid approach that combines Ant Colony Optimization and the Dragonfly algorithm for feature selection and community detection in complex networks. By integrating an active learning component, the framework reduces labeling costs while enhancing model robustness. Preliminary results on benchmark datasets (e.g., Zachary’s Karate Club) demonstrate improved modularity, labeling efficiency, and stability compared to baseline methods.

Projects

• Probabilistic Random Forest (PRF) — An uncertainty‑aware Random Forest implementation that handles measurement uncertainties in both input features and labels, making it significantly more robust in real-world scenarios where data inherently contains noise.
• Job-Shop Accounting Database System — Full‑stack manufacturing tracker using Java + Azure SQL that takes you from customer order to cost accounting and job close‑out.

Selected Publications

Contact

For inquiries, please email me at shahabi@ou.edu. I look forward to connecting!