Welcome to the AI Lab, a premier artificial intelligence research laboratory based at SDU university. Since our establishment in 2020, we have been at the forefront of AI research and innovation, dedicated to pushing the boundaries of technology and its applications in various industries.

History
Founded by Aigerim Bogyrbayeva, a visionary in the field of artificial intelligence, the AI Lab was established with the goal of merging theoretical research with practical applications. Our lab has grown from a small team of passionate researchers to a full-fledged research institution, collaborating with leading industries and academic institutions worldwide.

Mission
Our mission is to advance the understanding and implementation of artificial intelligence technologies to solve real-world problems. We aim to create AI solutions that are not only innovative and effective but also ethical and sustainable. Through our research, we strive to contribute to the broader field of AI and educate the next generation of AI professionals.

Goals
To lead groundbreaking research in artificial intelligence and machine learning.
To foster collaboration between academia, industry, and public sectors.
To develop AI technologies that benefit society in areas such as healthcare, environmental science, and public safety.
To promote the ethical use of AI through rigorous research and policy development.
Biographies of Key Personnel

Dr. Aigerim Bogyrbayeva – The founder of AI Lab, Aigerim, is a renowned figure in artificial intelligence with over 6 years of experience in the field. Her research focuses on application of RL models to solve classical Combinatorial optimization problems, and she has published numerous papers on the subject. Aigerim is a frequent speaker at international conferences and has received several awards for her contributions to AI research.

Current Projects
1. “Building Efficient Delivery Systems with Neural Combinatorial Optimization,” Funding for scientific and scientific and technical projects for 2023-2025 Ministry of Science and Higher Education of the Republic of Kazakhstan, AP19675614, amount $190,000, portion 100%
2. “Artificial Intelligence Solutions for Advanced Urban Logistics”, funding of young scientists for scientific and technical projects for 2023-2025 Ministry of Science and Higher Education of the Republic of Kazakhstan, AP19575607, amount $161,662
3. SDU Internal Research Funding 2023-2024, $2,500

Previous Projects
1. A start-up package from the Computer Science Department of SDU, $20,000
2. SDU Internal Research Funding 2022-2023, $2,500

Publications
A. Bogyrbayeva, M. Meraliyev*, T. Mustakhov*, and B. Dauletbayev*, “Machine Learning to Solve Vehicle Routing Problems:A Survey”, IEEE Transactions on Intelligent Transportation Systems, 2023, Accepted.
T. Mustakhov∗, Y. Akhmetbek∗ and A.Bogyrbayeva, “Deep Reinforcement Learning for Stochastic Dynamic Vehicle Routing Problem,” The 17th International Conference on Electronics Computer and Computation, accepted.

Under Revision Process
A. Bogyrbayeva, B. Dauletbayev* and M. Meraliyev*, “A Reinforcement Learnng Approach for Vehicle Routing Problems with Drone”.

At AI lab, we are committed to fostering a dynamic and innovative research environment that encourages growth and learning. We offer various opportunities for students, recent graduates, and professionals to join our cutting-edge research projects in the field of artificial intelligence. Here’s how you can get involved:

Research Participation

For Academics and Professionals:
We welcome collaborations with academics and industry professionals who are passionate about AI and its applications. Opportunities include joint research projects, co-authoring papers, and participating in symposiums and workshops hosted by our lab. If you are an expert in your field and interested in contributing to groundbreaking research, please contact us at [Contact Information].

For Students:
Students at SDU university and other universities can engage in research projects as part of their thesis or dissertation work. We provide access to state-of-the-art facilities, mentorship from leading AI researchers, and the opportunity to work on real-world problems. Interested students should apply through our annual research participation call or contact their academic advisor to explore available projects.

Internships

Summer Internship Program:
Our Summer Internship Program is designed for undergraduate and graduate students who wish to gain hands-on experience in AI research. Over 4 weeks, interns work closely with our team on ongoing projects, learning about AI techniques and contributing to innovative solutions. Applications for the summer program open in March each year.

Year-Round Internships:
We also offer part-time internships throughout the year, providing flexibility for students to balance their studies and research activities. These internships are an excellent way for students to apply their academic knowledge in a practical setting and develop professional skills under the guidance of experienced researchers.

How to Apply:
To apply for a research participation opportunity or an internship, please submit your CV, a cover letter, and a brief description of your research interests and goals to meraryslan.meraliyev@sdu.edu.kz. Ensure to specify the type of opportunity you are applying for and any relevant project preferences.

For more information on our research themes, ongoing projects, and application deadlines, please visit our website at [Lab’s Website URL].

Our lab offers an intellectually stimulating environment where you can:
–Work on impactful projects that advance the field of AI.
– Collaborate with leading experts in academia and industry.
– Access cutting-edge technology and resources. Gain valuable experience that can propel your future career in AI.
– We look forward to welcoming new talents to our team and together pushing the boundaries of what AI can achieve!

At AI Lab, we are committed to advancing the knowledge and application of artificial intelligence through a robust educational platform. Our lab offers a range of educational programs designed to cater to both novices and advanced learners in the fields of Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL). Here is what we offer:

Courses
Introductory Course on Machine Learning

Overview: This course provides a comprehensive introduction to the fundamental concepts of machine learning. Topics include supervised and unsupervised learning, classification algorithms, regression, and clustering.
Duration: 10 weeks
Target Audience: Beginners with basic programming knowledge.

Advanced Deep Learning
Overview: Dive deep into neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other advanced deep learning architectures. This course also covers techniques to train deep neural networks effectively.
Duration: 12 weeks
Target Audience: Intermediate learners with prior knowledge in ML.

Reinforcement Learning and its Applications
Overview: This advanced course focuses on the fundamentals of reinforcement learning, exploring dynamic programming, Monte Carlo methods, temporal difference learning, and policy gradient methods.
Duration: 8 weeks
Target Audience: Advanced learners and professionals.

Lectures and Workshops
Our lab hosts regular lectures and workshops led by our researchers as well as guest speakers from around the world. These sessions are designed to foster discussion on the latest AI research, breakthroughs, and ethical considerations in AI. Upcoming topics include:
– Ethics in AI
– The Future of AI in
– Healthcare AI for Research

Seminars
Our seminar series features weekly presentations on current AI research and emerging trends. These seminars are open to all and aim to encourage collaboration and knowledge sharing within the AI community. Some of our recent seminar topics include:
– “Navigating the Landscape of Neural Networks”
– “Challenges and Opportunities in Machine Learning”
– “Reinforcement Learning in Logistics”.

5 GPU based workstations

TEAM
– Bissenbay Dauletbayev is a Ph.D. student at SDU University, majoring in Computer Science. He earned his master’s in Computer Science at Boston University and bachelor’s in Information Systems at Kazakh-British Technical University. Bissenbay joined AI Lab at SDU in September 2021 and working on the project Deep Reinforcement Learning for Multiple Vehicles Routing Problem with Drones (mVRPD) which is his current research.
– Meraryslan Meraliyev is a Ph.D. student at SDU University, majoring in Computer Science. He earned his master’s in Computer Science at at SDU University. Meraryslan joined AI Lab at SDU in September 2021 and working on the project Deep Reinforcement Learning for Multiple Vehicles Routing Problem with Drones (mVRPD).
– Yernar Akhmetbek is a Computer Science M.S. student at SDU University. He earned his bachelor’s in Computer Science at SDU University. Yernar’s research interests inlcude machine learning and stochastic dynamic vehicle routing problems.

NEWS AND EVENTS

– 13/07/2023 our lab has been awarded a funding of scientists for scientific and technical projects for 2023-2025 from Ministry of Science and Higher Education of the Republic of Kazakhstan, AP19675614, in amount $190,000 for the project titled “Building Efficient Delivery Systems with Neural Combinatorial Optimization.” Congratulations!
– 26/06/2023 Taukekhan succesfully defended his Masters Thesis titled “Reinforcment Learning Approaches for Combinatorial Optimization Problems.” Congratulations!
– 15/05/2023 the article by the lab memebers T. Mustakhov, Y. Akhmetbek and A.Bogyrbayeva titled “Deep Reinforcement Learning for Stochastic Dynamic Vehicle Routing Problem,” got accepted for presentation at the The 17th International Conference on Electronics Computer and Computation. Congratulations!
– 03/05/2023 our lab has been awarded a funding of young scientists for scientific and technical projects for 2023-2025 from Ministry of Science and Higher Education of the Republic of Kazakhstan, AP19575607, in amount 72,748,082 tenge ($161,662) for the project titled “Artificial Intelligence Solutions for Advanced Urban Logistics.” Congratulations!
– 03/04/2023 the first paper of Yernar and Taukekhan titled “ Stochastic Dynamic Vehicle Routing Problem: Survey” got published in SDU bulletin 2023 N2 (62). Congratulations!
– 14/03/2023-31/03/2023 AI Lab hosted the reserach internship for the masters students of the computer science department of SDU University.
– 05/02/2022-05/11/2022 AI Lab hosted the reserach internship for the masters students of the computer science department of the Satpayev University.
– 04/06/2022 Merarsylan and Taukekhan presented at the reseach seminar their reserach titled “Learning Methods for Routing Problems: A Survey”.
– 04/06/2022 Bissenbay and Merarsylan presented at the reseach seminar their reserach titled “A Reinforcement Learning Approach for Vehicle Routing Problems with Drone”.
– 03/02/2022 Dr. Aigerim presented at the reseach seminar her recent reserach titled “A Deep Reinforcement Learning Approach for Solving Travelling Salesman Problem with Drone”.
– 01/31/2022 our group was awarded the SDU Internal Research Grant in the amount of 1 mln tenge. We thank the SDU Sceince Department for their support!
– 11/25/2021 Dr. Aigerim served as a AI-track lead at the ICECCO 2021 conference.

CONTACTS

meraryslan.meraliyev@sdu.edu.kz

Laboratory is intended for research and practical activities, founded in order to improve the quality and volume of research work in the field of big data analysis.

The Distributed Systems and Computing Laboratory (DSCL) is a hub for cutting-edge research across a spectrum of fields crucial to modern computing. Led by a team of researchers, DSCL is actively engaged in work in Natural Language Processing (NLP), Machine Learning (ML), Data Mining, Internet of Things (IoT), and Cybersecurity. By delving into these areas, DSCL aims to innovate in algorithmic development, system design, and security protocols, contributing to the advancement of distributed computing theory and practical applications.
The Distributed Systems and Computing Laboratory (DSCL) is open to new members and researchers who are eager to contribute to cutting-edge research in distributed computing. Whether you’re a student seeking internship opportunities or a seasoned researcher looking for a collaborative environment to explore innovative ideas, DSCL welcomes individuals from diverse backgrounds and expertise levels to join their team.
The Distributed Systems and Computing Laboratory (DSCL) boasts modern equipment tailored for advanced research in distributed computing. This includes a Dell PowerEdge T340 Server, an HP Enterprise/Aruba Instant Switch for network management, a UPS Online CyberPower for power backup, a secure cabinet for equipment housing, monitors for data visualization, an air conditioner for optimal working conditions, and ergonomic tables and chairs for comfort. These resources ensure an efficient and conducive environment for researchers to push the boundaries of distributed computing.

TEAM
– Kamila Orynbekova
– Mukhtar Amirkumar
– Andrey Bogdanchikov
– Dauren Ayazbayev Assem
– Talasbek Selcuk Cankurt

NEWS AND EVENTS
The Article published in Scopus base indexed journal with 75th percentile : Defining Semantically Close Words of Kazakh Language with Distributed System Apache Spark.

CONTACTS
Kaskelen, Abylaikhan str. 1/1, room No. G107/1-2

Introduction: Computer vision is a rapidly evolving field with the potential to revolutionize many aspects of technology and society, including autonomous vehicles, medical imaging, security and surveillance, human-computer interaction, and entertainment. In 2023, the Computer Vision Laboratory was founded in the Department of Computer Science at our university to promote cutting-edge research in computer vision. Mission: The mission of the Computer Vision Lab is to conduct cutting-edge research in the field of computer vision, making significant contributions to advancements in technology and society. Our research has the potential to revolutionize various applications including autonomous vehicles, medical imaging, and more, thereby shaping the future of these industries. Goals: The primary objectives of the Computer Vision Lab include: Object Detection and Recognition: Developing advanced algorithms for accurate object localization, classification, and tracking. Deep Learning for Computer Vision: Utilizing deep learning paradigms like CNNs, RNNs, and GANs to tackle complex vision tasks. Image Generation and Synthesis: At the forefront of innovation, we are creating groundbreaking techniques for image synthesis and manipulation for VR, gaming, and digital content. Our research is pushing the boundaries of what is possible in these fields, promising exciting new developments. Human-Computer Interaction: Enhancing interfaces for natural user interactions through vision-based gesture, expression, and gaze recognition. Image Processing with Adaptive Filters: Improving image quality through advanced adaptive filtering for tasks like denoising and enhancement. Biographies of Key Scientists: Cemil Turan, the head of our laboratory, is an associate professor at Suleyman Demirel University, where he has been a faculty member since 2008. With a bachelor’s degree in Electrical Engineering from Yildiz Technical University, Turkey, in 1995 and a Ph.D. in Electrical and Computer Engineering from Mevlana Rumi University, Turkey, in 2016, his expertise in Digital Signal and Image Processing is unparalleled. He has been working in the field of computer vision, focusing on object recognition recently. Ualikhan Sadyk earned a Bachelor of Technology (Second Class Hons.) in Radio Engineering, Electronics and Telecommunications From Karaganda State University, Kazakhstan, and a Master of Science in Computer Science from Suleyman Demirel University, Kazakhstan. He is currently in the second year of his Ph.D. program in Computer Science at SDU University. Ualikhan Sadyk currently holds the positions of Educational Program Coordinator of Computer Science and Senior Lecturer at the Department of Computer Science, SDU University, Kaskelen, Kazakhstan. His research interests encompass recommendation systems, image recognition, and computer vision. Rashid Baimukashev received a Bachelor of Sciences in Radio Engineering, Electronics, and Telecommunications from Almaty Institute of Power Engineering and Telecommunications, Kazakhstan, a Master of Sciences in Communications Engineering from RWTH-Aachen University, Germany, and is currently in a second-year Ph.D. program in Computer Science at SDU University, Kazakhstan. His research interests are in Image Processing and Network Security.

Current Projects: Banknote Recognition for Security in Banking Transactions. This project leverages deep learning and data analytics to enhance the accuracy and security of banking transactions by developing sophisticated banknote recognition algorithms. Innovative Methods in Image Processing and Facial Recognition Focused on using machine learning algorithms to improve security systems and create personalized services, enhancing the quality of life and maintaining a leading scientific position. Research Papers; Here are some of the key publications related to the projects conducted at your laboratory: Sadyk, U., Bozshina, A., Baimukashev, R., and Turan, C., 2024. “Applying Gray Level Co-occurrence Matrix features and Learning Vector Quantization for Kazakhstan Banknote Classification.” This paper discusses the application of specific image textural features and machine learning techniques for classifying banknotes, enhancing the robustness of financial security systems. Sadyk, U., Baimukashev, R., Bozshina, A. and Turan, C., 2024. “KZ-BD: Dataset of Kazakhstan banknotes with annotations.” Published in Data in Brief, this article introduces a comprehensive dataset of annotated Kazakhstan banknotes designed to aid in the development and testing of machine learning models for banknote recognition. Sadyk, U., Turan, C., and Baimukashev, R., 2023, June. “Overview of deep learning models for banknote recognition.” Presented at the 2023 17th International Conference on Electronics Computer and Computation (ICECCO), this paper provides a survey of various deep learning models applicable to the task of banknote recognition, outlining their effectiveness and areas for improvement.

Research Participation and Internship Opportunities Graduate and Undergraduate Research Opportunities: Students enrolled at the university can join the lab as part of their thesis or dissertation work. They can participate in ongoing projects like banknote recognition or human-computer interaction enhancements. Undergraduate students might have opportunities to assist in research through summer internships or part-time research assistant positions during the academic year. Postdoctoral Research Positions: Recently graduated PhDs might find opportunities to deepen their research experience in specialized areas such as deep learning for computer vision or adaptive image filtering. These positions often lead to significant professional growth and publication opportunities. Internships for External Candidates: The lab might offer internships that allow students from other academic institutions or early-career researchers to gain experience. These positions are typically hands-on, involving participants directly in the research projects using state-of-the-art technology and methodologies. Collaborative Research Projects: There may be opportunities for collaboration with industry partners, other academic institutions, or government research initiatives. These projects can provide practical experience and networking opportunities, crucial for professional development in the field of computer vision. How to Apply Interested candidates are usually encouraged to: Check the laboratory’s website or the university’s career services portal for postings about available positions. Contact the head of the lab, Professor Cemil Turan, directly with a detailed CV and a cover letter outlining their research interests and how they align with the lab’s objectives. Attend workshops or public lectures given by the lab to network with current members and learn more about their projects. These opportunities not only provide practical experience but also contribute to the advancement of the field of computer vision through innovative research.

Courses The lab offer specialized undergraduate and graduate courses in topics such as: Introduction to Computer Vision: Basics of image processing, object detection, and computer vision algorithms. Advanced Computer Vision: Deep dives into modern techniques like deep learning for computer vision, including hands-on projects involving CNNs, RNNs, and GANs. Machine Learning for Image Processing: Focused on applying machine learning techniques to tasks such as image classification, recognition, and generation. Human-Computer Interaction: Coursework that covers the development of systems for interpreting human gestures, facial expressions, and other forms of non-verbal communication. Lectures Regularly scheduled lectures might be part of the lab’s activities in the future, featuring: Guest Lectures: Invitations to external experts in computer vision and related fields to discuss recent advancements or case studies. Faculty Lectures: Presentations by your lab’s scientists on their current research, methodologies, and findings. These can help students and other faculty members stay abreast of the latest developments in the field. Seminars The laboratory might host weekly or monthly seminars where: Research Findings: Students and researchers present their work, discuss their methodologies, and receive feedback. Work-in-Progress Sessions: These sessions allow participants to discuss ongoing research and troubleshoot issues collaboratively. Technology Demonstrations: Practical demonstrations of new software, tools, or hardware being used or developed in the lab. Workshops Hands-on workshops can provide practical training, such as Deep Learning Tools, which are workshops on using TensorFlow or PyTorch for computer vision tasks. Data Annotation and Processing: Training on preparing datasets for computer vision applications, including labeling and annotating techniques. Specialized Software and Hardware Training: Given the lab’s need for high-performance computing and specific tools like OpenCV, sessions might focus on effectively using these resources. These educational programs are designed to bolster participants’ academic knowledge and provide them with practical skills directly applicable to real-world problems in computer vision. These offerings might be detailed on the laboratory’s website, in university course catalogs, or through academic advising resources.

Technical Equipment in the Computer Vision Research Laboratory Given the nature of your research objectives, the Computer Vision Research Laboratory is equipped with advanced technical gear and tools necessary for high-performance tasks in image processing, deep learning, and object recognition. Here’s an overview of the typical equipment: High-Performance Workstations: CPUs and GPUs: Powerful processors and graphics processing units are essential for handling large datasets and running complex machine learning models. Your lab likely uses top-tier GPUs designed for deep learning, such as NVIDIA’s Tesla or Quadro series. RAM and Storage: High-capacity RAM and fast storage solutions (SSDs) to manage large-scale image databases and facilitate rapid data retrieval and processing. Software and Development Tools: Deep Learning Frameworks: Tools like TensorFlow, PyTorch, and Keras for building and training deep learning models. Image Processing Libraries: OpenCV for basic to advanced image manipulation, along with other specialized libraries for image analysis. Programming Environments: Integrated development environments (IDEs) for languages like Python, C++, and MATLAB, which are commonly used in computer vision research. Specialized Imaging Equipment: High-Resolution Cameras and Sensors: For capturing detailed images and videos required for analysis. 3D Scanners: For creating detailed 3D models of objects, which are essential in object recognition and human-computer interaction studies. Networking and Data Security Infrastructure: Robust networking solutions to facilitate large data transfers and collaborations, both internally and with external partners. Advanced security protocols to protect sensitive data, especially important in projects involving personal or financial information. Modern Technologies and Methods Applied Your laboratory employs a range of modern technologies and methodologies to stay at the forefront of computer vision research: Deep Learning: Utilizing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) for tasks such as image recognition, object detection, and image synthesis. Object Detection and Recognition Techniques: Techniques like YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and Faster R-CNN for real-time object detection. Advanced algorithms for object tracking and scene recognition, essential in autonomous vehicle research and surveillance applications. Image Generation and Synthesis: Employing GANs for generating realistic images for use in virtual reality and gaming. Techniques like style transfer and image-to-image translation to create visually appealing designs and effects. Adaptive Filtering for Image Processing: Developing and applying adaptive filters to enhance image quality, including denoising, deblurring, and color correction, adapting the filters based on local image features. Human-Computer Interaction: Gesture recognition, facial expression analysis, and gaze tracking technologies to create intuitive user interfaces for augmented and virtual reality environments.

TEAM
Cemil Turan, head of lab. Rashid Baimukashev, researcher. Ualikhan Sadyk, researcher.

NEWS AND EVENTS
The good news is that fortunately we will be given a larger laboratory in the new building next year.

CONTACTS
rashid.baimukashev@sdu.edu.kz

RESOURCES FOR STUDENTS
Stutent practices are conducted every end of the educational year on Computer Vision Basics

GALLERY

The laboratory was created in 2022.
The IoT laboratory serves as a hub for exploring and experimenting with Internet of Things technologies. It’s a dedicated space where researchers, students, and professionals collaborate to develop and test IoT solutions. Within this environment, individuals can work on projects, conduct experiments, and learn about sensor networks, data analytics, communication protocols, and system integration related to IoT. The laboratory facilitates hands-on learning experiences and workshops to educate individuals about the potential applications and challenges of IoT. Ultimately, it aims to foster innovation and knowledge exchange, advancing the understanding and adoption of IoT technologies in various fields.

Educational Programs
Introduction to IoT, Biometrics, Sensors and Mobile networks, IoT 1: things and networking, Data Science for IoT, IoT Data Management and Analytics, IoT in Industry, FPGA programming

Laboratory Equipment and Technologies
1. Ubiquiti UniFi Dream Machine Pro Server
2. Base station Vega BS-1.2
3. IoT sensor search device, LM-1 12800 mAh, LoRaWAN
4. VEGA base station
5. Vega Smart-MS0101, Infrared Motion Sensor (Frequency 868RU)
6. Light sensor Vega Smart UM0101
7. Smart HS-0101 temperature and humidity sensor LoRaWAN Vega
8. Antenna 868-01-A10
9. DJI Mini 2 Fly More Combo Drone
10. 3D printer
11. FPGA platforms
12. Microcontrollers: Arduino, STM32, PIC, ESP
13. Raspberry Pi (0, 4) + camera module 14. Digital oscilloscope

The laboratory was created in 2011. The electronics training laboratory performs laboratory work on general professional and special subjects led by the department. The purpose of the laboratory is to create conditions for conducting classes at the appropriate technical and informational level. Methodological provision of laboratory and practical work of students. The laboratory is equipped with stands for designing circuits and studying them. Stands allow you to study electrical circuits, electronic elements and the operation of logic circuits. Additionally, by using microcontrollers, students gain valuable skills in programming, circuit design, and system integration.

Educational Programs
Electronics, Digital design, Low-level architechture, Advanced computer architecture

Laboratory Equipment and Technologies
1. Basic electronics training set (Yıldırım Elektronik training set) DC & AC – 17 modules.
2. Design of digital circuits laboratory work modules – 11 modules.
3. PIC 16F877 Microcontroller Education Sets
4. Oscilloscopes.
5. Power blocks DC Power Supply 30V 10A.
6. Multimeters.
7. Welding stations.
8. Arduino Uno, nano microcontrollers.
9. Raspberry Pi + camera
10. ESP8266
11. ESP32 + LoRa
12. 35 different sensors
13. Wireless transmitter STX882 + RF receiver, 433 MHz
14. KIT for learning and assembling SMD components
15. Kits for learning welding.

Team
Binara Imankulova

Contacts
Head of laboratory: binara.Imankulova@sdu.edu.kz

The Physics Laboratory was founded in 2012. The Physics Laboratory performs laboratory work in general professional and special disciplines supervised by the department. The general purpose of the laboratory is to create conditions for conducting classes at the technical and informational level for conducting experimental work. Providing experimental and practical work of students with various methods. Familiarization with electrical measuring devices and determination of instrument readings. The laboratory is equipped with stands for drawing up diagrams and studying them. The stands allow you to study electrical circuits, electronic elements and the operation of logic circuits. In addition, with the help of the device, students receive special support for the practical part of the work, determining the cost, sensitivity of the part and building a graduated graph for the ammeter. For example, an ammeter, voltmeter, additional roughness. In addition, he gets acquainted with the performance of various laboratory work. In the physics laboratory, students can independently study and master the experiment.

RESEARCH
1. University Improving the Problem of In-depth learning in the Electrodynamics Department // ЖОО электродинамика бөлімін тереңдете оқыту мәселесін жетілдіру Абай атындағы Қазақ ұлттық педагогикалық университеті. «Хабаршы». -2018. -№1(61). Б.181-186.
2. Methodology for solving problems of electrodynamics using vectors in the course of physics// Физика курсында векторларды қолданып электродинамикадан мәселелерді шешудің методологиясы Абай атындағы Қазақ ұлттық педагогикалық университеті. «Хабаршы». -2023.- №2(402). Б.134-147
3. Сomputer-aided methods of physical calculations at a higher education institution // Компьютерные методы физических расчетов в высшем учебном заведении. «М.Тынышбаев атындағы Қазақ көлік және коммуникациялар академиясының Хабаршысы» ғылыми журналы -2023. – желтоқсан.
4. The Development of Education Methods of Electricity and Magnetism Discipline in Higher Educational Institutions // Жоғары оқу орындарында Электр және магнетизм дисциплинасының оқу әдістемеліктерін дамыту. Conference ICECCO. -2018. -P. 14. Conference ICECCO. -2018. -P. 14. ISBN 978-1-7281-0133-0
5. General characteristics of teaching methods in electrodynamics // Электродинамика пәнін оқыту әдістерінің жалпы шешімдері. “URALIntellects.r.o” XVI Халықаралық ғылыми-тәжірибелік конференциясының материалдары. -Прага, 2018. «АСП-Интер». -Тюмень, Россия. Б.65-67.

The Development of Education Methods of Electricity and Magnetism Discipline in Higher Educational Institutions // Жоғары оқу орындарында Электр және магнетизм дисциплинасының оқу әдістемеліктерін дамыту. Conference ICECCO. -2018. -P. 14.

EDUCATIONAL PROGRAMS
CSS 112, CSS 251, INF 106, MAT 151, CSS 108, MAT 156, CSS 256, CSS 341

LABORATORY EQUIPMENT AND TECHNOLOGIES
Set of educational and laboratory equipment “Pendulum with variable G” UP6251, Set of educational and laboratory equipment “Mechanics-1” UP6186, Set of educational and laboratory equipment “Physical and mathematical pendulum” UP6256, Set of educational and laboratory equipment “Mechanics-2” UP-6187, Set of educational and laboratory equipment “Moment of inertia” UP6247, COILS IN AC, CAPACITORS IN AC, ANALYSIS OF THE RC SERIAL CIRCUIT FOR AC CURRENT, ANALYSIS OF THE PARALLEL RL CIRCUIT FOR AC CURRENT, THE RELATION OF MAGNET POLES

TEAM
Kalieva Asem Abayevna

CONTACT
assem.kaliyeva@sdu.edu.kz

RESOURCES FOR STUDENTS
1. Raymond A. Serway, John W. Jewett, Jr. Physics Edition for Scientists and Engineers with Modern Physics – Ninth Edition – 2013 – p. 1622.
2. https://phet.colorado.edu/en/simulations/charges-and-fields
3. https://phet.colorado.edu/en/simulations/capacitor-lab-basics
4. https://phet.colorado.edu/en/simulations/circuit-construction-kit-dc-virtual-lab
5. https://phet.colorado.edu/en/simulations/circuit-construction-kit-dc
6. https://phet.colorado.edu/en/simulations/faradays-law
7. Physics Laboratory works Compiler Slyunyayeva N.V. Almaty-2006 P-98.

GALLERY

The RedHat lab is aimed at training for RedHat Linux Administration Certification like RHSA etc. It was started as RedHat Academy by facilitation of RedHat company. We have 21 computers with RedHat Linux.

The goal of the Cisco Networking Academy Lab is to strengthen students’ knowledge by conducting laboratory works on real equipment, as well as forming a center for conducting applied research on network technologies. The Cisco Networking Academy Lab conducts practical classes in the subjects Computer networks 1, Computer networks 2, Network security on network equipment from Cisco, and it is also planned to conduct research work on-network technologies.

The laboratory is an official authorized academic partner of Autodesk. Laboratory is designed to work on projects for 2D and 3D modeling, animation, design of projects using augmented and virtual reality, prototyping projects, using all the necessary tools for the full cycle of prototyping, from idea to implementation: Licensed modeling software, Laser cutting, 3D printers, etc.

The main task of the laboratory is to conduct practical and laboratory classes in Data Analysis on specialized stands. The lab has worked with SAS Institute for 3 years. Data Science laboratory uses DataCamp For The Classroom.