Full Professor of Computer Engineering | Artificial Intelligence Researcher | Visiting Researcher at InfAI Dresden

I am a Full Professor in the Department of Computer Engineering at Shahid Bahonar University of Kerman, Iran, and currently a Visiting Researcher at the Institute for Applied Computer Science (InfAI), Dresden, Germany.

I received my B.Sc. in Computer Engineering from Shiraz University in 2001, followed by M.Sc. and Ph.D. degrees in Artificial Intelligence from the same university in 2004 and 2008, respectively. I have been a faculty member at Shahid Bahonar University of Kerman since 2008.

My research focuses on Artificial Intelligence, Machine Learning, Fuzzy Systems, Foundation Models, Large Language Models, and Intelligent Information Systems. I have authored or co-authored approximately 180 research papers in international journals and conference proceedings.

• 🤖 Artificial Intelligence & Machine Learning
• 🧠 Artificial General Intelligence and Cognitive approaches in AI.
• 🧠 Foundation Models & Large Language Models (LLMs)
• 🔍 Retrieval-Augmented Generation (RAG) and Self-RAG
• 📊 Deep Learning & Time-Series Forecasting
• 🐝 Artificial Intelligence for Honey Bee Research
• 🧩 Explainable and Intelligent Information Systems
• 🌐 Fuzzy Methods and Fuzzy Systems

Brief description of the project, dataset, model, or research contribution.

I have published approximately 180 papers in international journals and conference proceedings.

Selected research areas include:

• Allo-Self-RAG: Fuzzy aggregation of internal and external critique signals for improved Self-RAG evaluation

• MG-CRAG: Fusion of Multi-Granular Retrieval Evaluators in Corrective RAG with Weakly Supervised Fine-Tuning

• Augmenting RAG with Nonnegative Matrix Factorization-Driven Semantic Chunking in Embedding Space

• PFE-SELF-RAG: Balancing Self-RAG Evaluation Metrics via Pareto Efficiency

• Optimizing Prompts Efficiently with Iterative Determinantal Point Processes

• Impact of Converting Graphs into Spanning Trees on Node and Graph Classification in Graph Neural Network

• A Similarity Measure Based on Subspace Distance for Spectral Clustering

• QuickCharNet: An Efficient URL Classification Framework for Enhanced Search Engine Optimization

• Generating Modern Persian Carpet Map by Style-Transfer

• Evaluating Traditional versus Ensemble Machine Learning Methods for Predicting Missing Data of Daily PM10 Concentration

• Beyond Words: A Heterogeneous Graph Representation of Text via Graph Neural Networks for Classification

• A Hybrid Method of Self-Supervised Graph Embedding, Siamese Networks, and Transformers for Sentiment Analysis in Persian Language

• DINO-CXR: A Self-Supervised Method Based on Vision Transformer for Chest X-Ray Classification

• Low-Redundant Unsupervised Feature Selection Based on Data Structure Learning and Feature Orthogonalization

• Deep Metric Learning with Soft Orthogonal Proxies

• SELF-VS: Self-Supervised Encoding Learning for Video Summarization

• A Novel Framework Based on the Multi-Label Classification for Dynamic Selection of Classifiers

• A Hybrid Method of Self-Supervised Graph Embedding, Siamese Networks and Transformers for Sentiment Analysis in Persian Language

• Image-Text Connection: Exploring the Expansion of the Diversity Within Joint Feature Space Similarity Scores

For the complete and most up-to-date list of publications and citation information, please visit my:

Google Scholar Profile

Currently, I am particularly interested in:

• Foundation models and LLM-based intelligent systems
• Artificial General Intelligence and Cognitive approaches in AI.
• Advanced RAG architectures
• Time-series forecasting with deep learning and foundation models
• AI applications in biological and behavioral research
• Benchmarking and evaluation of AI models
• Fuzzy approaches for modern AI systems

• 🌐 GitHub: MahdiEftekhari
• Linkedin: MahdiEftekhari
• 🔬 ORCID: 0000-0002-0381-8225
• 📚 Google Scholar: https://scholar.google.com/
• 🏛️ University: Shahid Bahonar University of Kerman

⭐ Feel free to explore my repositories and research projects.