AI Research Lab

Institute of Automation and Information Technologies, Satbayev University

1. Objective

The objective of AI Research Lab is to establish a leading scientific and educational laboratory in the field of artificial intelligence at Satbayev University, focused on conducting cutting-edge research, developing practice-oriented AI solutions, training qualified specialists, and implementing artificial intelligence technologies in priority sectors of Kazakhstan's economy and social sphere.

2. Tasks

1. To conduct fundamental and applied research in the fields of artificial intelligence, machine learning, large language models, computer vision, data analysis, and high-performance computing.
2. To develop new methods, algorithms, and software-hardware solutions in AI for scientific, educational, and industrial challenges.
3. To train students, master's students, doctoral students, and young researchers through engagement in real scientific and applied projects.
4. To develop and implement educational courses, modules, seminars, and workshops on artificial intelligence.
5. To create conditions for the completion of undergraduate, master's, and doctoral work at the laboratory.
6. To develop scientific cooperation with domestic and foreign universities, research centres, and industrial partners.
7. To participate in grant, programme-targeted, and commercial contract projects aimed at advancing AI technologies.
8. To create prototypes, pilot solutions, and intelligent systems with subsequent commercialisation and implementation.
9. To develop the university's scientific infrastructure in the fields of artificial intelligence, big data analysis, and computational technologies.
10. To contribute to Kazakhstan's digital transformation through the development of solutions for industry, education, media, governance, language technologies, and other areas.

3. Main areas of activity

3.1. Artificial intelligence and AI optimisation

• AI-based optimisation and heuristic methods;
• AI-guided and LLM-guided approaches for combinatorial optimisation;
• hybrid methods combining exact algorithms and AI models;
• multi-criteria and multimodal optimisation;
• AI-driven optimisation for Smart Cities, Industry 4.0, and complex system management.

3.2. Machine learning, deep learning, and Big Data

development of new machine and deep learning algorithms;

clustering, analysis, and interpretation of large datasets;

scalable solutions for streaming and distributed data;

intelligent analysis of large information arrays.

3.3. Computer vision and multimodal AI

• object recognition and image analysis;
• integration of text, images, and audio in multimodal AI systems;
• application of AI in robotics, autonomous systems, and industrial automation.

3.4. Language technologies and large language models

• natural language processing for multilingual systems;
• development of solutions for Kazakh, Russian, and other languages;
• text generation, automatic summarisation, morphological and syntactic analysis;
• semantic search and knowledge extraction using LLMs.

3.5. High-performance computing for AI

• acceleration of computations for AI models;
• parallel and distributed computing;
• use of GPU and HPC infrastructure;
• optimisation of computational operations for scalable AI systems.

3.6. Software engineering with AI and LLMs

• intelligent analysis of software code;
• vulnerability detection and static analysis;
• automation of technical documentation and reporting preparation;
• application of LLMs in software engineering tasks.

3.7. Educational and scientific-practical activities

• development of training courses and educational modules;
• conducting seminars, master classes, and scientific schools;
• training of scientific personnel;
• execution of applied and commercial contract projects in collaboration with industry.

4. Alignment with the UN Sustainable Development Goals (SDGs):

SDG 4, SDG 8, SDG 9, SDG 10, SDG 11, SDG 16, SDG 17