Introduction to Research Laboratories of the Computer Engineering Department
1- Representation Learning Laboratory (Supervisor: Dr. Akhlaghian)
The Representation Learning Laboratory at the University of Kurdistan's Computer Department focuses on developing advanced machine learning methods for extracting meaningful, robust, and generalizable features and representations from data. Our research includes deep neural networks, autoencoders, matrix factorization, graph representation, and multi-modal learning, exploring both classical methods and emerging paradigms. Our goal is to provide efficient, interpretable, and scalable models for data analysis, prediction, and real-world machine learning applications. By combining theoretical studies, algorithm design, and experimental tests, the laboratory strives to create methods that provide both deep understanding of data and high practical performance. We also pay special attention to investigating the robustness and generalizability of models when faced with complex and diverse data.
Algebraic Machine Learning (AML) Group
The Algebraic Machine Learning (AML) Group is part of the Representation Learning Laboratory in the Computer Engineering Department at the University of Kurdistan. Algebraic machine learning is a principled framework that, utilizing algebraic concepts and model theory, models data and prior knowledge in the form of structured representations. The research of this group is built upon linear algebra, geometry, probabilistic modeling, and deep learning, with a special focus on matrix factorization and representation learning methods.
The main research axes of the group include:
• Learning compact, interpretable, and low-dimensional representations for data analysis and visualization
• Investigating the trade-off between robustness and accuracy through theoretical and experimental studies
• Designing algorithms for noisy, incomplete, low-sample, and weakly labeled data
• Utilizing multi-modal and multi-view data from heterogeneous information sources
2- Graph Machine Learning Research Laboratory - Supervisor: Dr. Alireza Abdollahpouri
The Graph Machine Learning Research Laboratory at the University of Kurdistan is dedicated to advancing the field of artificial intelligence through innovative research on structured graph data and deep learning techniques. Our interdisciplinary team focuses on developing novel algorithms and models that leverage the complex relationships inherent in graph data to solve real-world problems in various domains such as social networks, bioinformatics, recommender systems, and more. Committed to excellence in theoretical foundations and practical applications, we strive to push the boundaries of machine learning by exploring new methods that improve accuracy, scalability, and interpretability.
3- Language, Vision, and Generative AI Laboratory - Supervisor: Dr. Fatemeh Daneshfar
The Generative and Multi-modal AI Laboratory, aiming to develop and advance modern research in the field of artificial intelligence, focuses on multi-modal learning and generative models, and particularly studies and develops Large Language Models (LLMs) and Vision-Language Models (VLMs). The research of this laboratory investigates methods by which intelligent systems can simultaneously understand, generate, and reason about text and visual data, providing a more natural interaction between humans and machines. By developing novel architectures, advanced training strategies, and evaluation frameworks for foundation models, the laboratory addresses important challenges in areas such as multi-modal reasoning, content generation, visual understanding, and human-centered AI. In line with the university's scientific mission, the laboratory is committed to expanding interdisciplinary collaborations, training students, and providing impactful research in the AI scientific community.
4- Social and Biological Networks Analysis Laboratory (SBNA) - Supervisor: Dr. Sadegh Soleimani
The Social and Biological Networks Analysis Laboratory (SBNA) aims to develop and apply network analysis-based approaches to study and solve complex problems in social, biological, and medical domains. The main focus of this laboratory is on modeling relationships between entities and extracting hidden patterns from complex data through graph theory, network analysis, and data-driven methods. SBNA research axes include bipartite network analysis, network-based time series analysis, and the application of advanced network methods in real social and biological data. In addition to generating theoretical knowledge, this laboratory focuses on applied research, publishing scientific articles, and interdisciplinary collaborations in the field of network analysis and complex data.
5- Distributed Computing Systems Laboratory (DCS LAB) - Supervisor: Dr. Saadon Azizi
The Distributed Computing Systems Research Laboratory focuses on research and development in the design, analysis, and implementation of distributed, intelligent, and scalable software systems. With a research-oriented and problem-oriented approach, this laboratory studies fundamental and advanced challenges of modern computing; challenges that lie at the intersection of software engineering, distributed systems, and optimization. DCS's vision is to provide innovative solutions based on precise models that can guarantee the efficiency, robustness, and self-adaptability of systems in heterogeneous and dynamic environments. The research activities of this laboratory are particularly focused on the use of artificial intelligence and machine learning in distributed computing systems, employing these methods for intelligent decision-making, prediction, and optimization of system performance. An important part of the research at DCS is dedicated to resource management in complex and multi-layered infrastructures, where combinatorial optimization algorithms play a key role in solving scheduling, service deployment, and resource allocation problems. This research is conducted in the context of modern edge/fog/cloud computing architectures as well as serverless computing, with special attention given to their interaction with the Internet of Things as one of the main drivers of future distributed systems.
The main research focuses of the laboratory are summarized as follows:
• Edge, Fog, and Cloud Computing and hybrid architectures
• Serverless Computing and function and service management
• Internet of Things and its integration with distributed infrastructures
• Resource management and scheduling in distributed and heterogeneous infrastructures
• Artificial Intelligence and machine learning in distributed computing systems
• Combinatorial optimization algorithms for solving software system problems
6- Intelligent Systems Research Laboratory (ISLAB) - Supervisor: Dr. Roojiar Pir Mohammadiani
The Intelligent Systems Research Laboratory focuses on the design and development of advanced artificial intelligence systems for understanding, analyzing, and supporting decision-making in organizations and complex networks. This research group operates within the framework of the Representation Learning Laboratory and, by combining scattered organizational data, structured and unstructured documents, and multi-layer network modeling, strives to transform decision-making from raw data to executable operational knowledge. The core of our activity includes decision intelligence, Retrieval-Augmented Generation (RAG) and LLM-based knowledge integration systems, and network resilience analysis, which have applicability in industrial, organizational, and infrastructural environments. This laboratory focuses on developing decision support tools with human-machine interaction, designing intelligent assistants with documented and explainable responses, and analyzing complex energy and resource networks. Our research axes include multi-layer and heterogeneous network modeling, graph analysis and identification of critical nodes, RAG algorithms, time series prediction, and decision optimization at operational and strategic levels. The laboratory's activities are designed both for producing reputable scientific articles and providing practical solutions for industry, government, and organizational projects.
Key research axes:
• Aggregation and processing of scattered organizational and network data
• Development of intelligent RAG systems and LLM-based assistants for documented responses
• Multi-layer network modeling and resilience analysis of complex systems
• Time series prediction and optimization of operational decisions
7- Internet of Things Research and Educational Laboratory (IoT LAB) – Supervisor: Dr. Saadon Azizi
The Internet of Things Laboratory at the University of Kurdistan aims to develop research, education, and innovation in one of the most influential contemporary information technology paradigms. IoT, as a key platform for implementing intelligent systems, plays a pivotal role in the transformation of digital, industrial, and urban infrastructures. This laboratory was established in May 2018 under the supervision of Dr. Saadon Azizi and, by providing a specialized environment, has made it possible for interested students and researchers to conduct educational, research, and experimental activities. Utilizing various hardware equipment and laboratory infrastructures, this laboratory provides a suitable platform for designing, implementing, and evaluating IoT-based systems, with a special focus on integrating IoT with technologies such as edge/fog computing, cloud computing, and data analysis. Laboratory activities are defined with a problem-oriented and applied approach, striving to establish an effective link between theoretical concepts, real system development, and industrial needs.
The laboratory's objectives include:
• Holding specialized workshops and courses in the field of IoT and related technologies
• Cultivating, guiding, and supporting creative and innovative student ideas
• Implementing applied and fundamental research projects and plans
• Holding specialized seminars and meetings with the presence of prominent professors and researchers
• Defining and supervising undergraduate, master's, and doctoral projects and theses in the fields of IoT, edge computing, cloud computing, and big data
Laboratory facilities:
• Various electronic and development boards
• A diverse collection of widely used sensors and actuators
• Short-range and long-range communication equipment
• Basic electronic components and peripherals needed for system implementation
Areas of applied and research interest:
• Smart cities
• Smart health
• Smart agriculture
• Smart homes and buildings
• Environmental monitoring
• Smart energy grids
