Marat NURTAS holds a PhD in Mathematical and Computer Modeling (MCM) from the Kazakh-British Technical University (KBTU) and a Bachelor’s degree in Mathematics from Al-Farabi Kazakh National University. He currently serves as an Associate Professor at the International Information Technology University (IITU). Dr. Nurtas is a recipient of the prestigious international “Bolashak” scholarship, awarded by the President of the Republic of Kazakhstan. Starting from November , 2025, Dr. Nurtas has been appointed as a Visiting Professor at Zhongyuan University of Technology (Zhengzhou, China) under the "Zhongyuan Talent Program (Talent Introduction Series)" for the period 2026–2028, where he is conducting advanced research in the fields of artificial intelligence and computational modeling.
He is the Principal Investigator of a national research grant funded by the Ministry of Science and Higher Education of the Republic of Kazakhstan for 2024–2026, titled “Machine Learning and Deep Learning in Seismology: A Novel Approach for Earthquake Risk Prediction.”
Dr. Marat NURTAS also serves as an Associtae Editor for Numerical Simulation and Data Science at "Engineered Science Publisher" (USA), a Scopus-indexed publisher ranked in the first quartile (98th percentile) worldwide.
He has participated in several international collaborations, including the Erasmus+ ELBA project “Establishment of Training and Research Centers and Development of Courses on Big Data Mining in Central Asia” (Grant No. 610170-EPP-1-2019-1-ES-EPPKA2-CBHE-JP, 2019–2022). From 2021 to 2023, he led the AP09058367 project for young scientists titled “Numerical Modeling of the Stress-Strain State of the Earth’s Crust in Kazakhstan Using Long-Term GPS Measurements.”
His main scientific interests include machine learning, deep learning, numerical optimization, neural network training and compression, and Physics-Informed Neural Networks (PINNs) for solving partial differential equations and modeling geophysical processes.
From 2016 to 2025, Dr. Nurtas has authored and co-authored numerous peer-reviewed papers indexed in Web of Science, Scopus, and RSCI. Currently, he has a Hirsch index of 8 (Scopus) and 4 (Web of Science), with 41 publications in Scopus and 12 in Web of Science.
Identifiers:
- ORCID: https://orcid.org/0000-0003-4351-0185;
- Scopus Author ID: 57189710532;
- Researcher ID: JWP-4213-2024; AAW-7412-2020.
Here are some recent papers project published in journals included in the Web of Science and Scopus databases:
[1] M. Nurtas, A. Altaibek, A. Ydyrys, A. Vilayev and T. Nessipbay, Development of a Long Short-Term Memory (LSTM)-Based Statistical Model for Earthquake Forecasting in Central Asia, in IEEE Access, vol. 13, pp. 162304-162319, 2025, doi: 10.1109/ACCESS.2025.3610168 [Web of Science, JCI Category: Q2, IF: 3.9. Scopus, Quartiles: Q1, Percentile: 90%, CiteScore: 9.0, DOI: 10.1109/ACCESS.2025.3610168]
[2] Nurtas, M., Altaibek, A., Ydyrys, A., T. Nessipbay. Analyzing historical seismic data for region-specific earthquake prediction through deep neural networks. Springer Nature in Journal of Seismology (2025). Doi: 10.1007/s10950-025-10316-w [Web of Science, JCI Category: Q3, IF: 2.0. Scopus, Quartiles: Q2, Percentile: 61%, CiteScore: 3.3.]
[3] Altaibek A, Zhumabayev B, Sarsembayeva A, Nurtas M, Zakir D. Enhancing Geomagnetic Disturbance Predictions with Neural Networks: A Case Study on K-Index Classification. Atmosphere. 2025 Feb 25;16(3):267. [Web of Science, IF= 2.6. Scopus, Quartiles: Q2, Percentile: 69%, CiteScore 4.9] DOI: https://doi.org/10.3390/atmos16030267
[4] Altaibek A, Nurtas M, Zhantayev Z, Zhumabayev B, Kumarkhanova A. Classifying Seismic Events Linked to Solar Activity: A Retrospective LSTM Approach Using Proton Density. Atmosphere. 2024 Oct 27;15(11):1290. [Web of Science, IF= 2.6. Scopus, Quartiles: Q2, Percentile: 69%, CiteScore 4.9] DOI: https://doi.org/10.3390/atmos15111290
[5] R. Krasnozhonov and M. Nurtas*, Modeling the Propagation of Acoustic Waves in an Elastic Medium Using Physics-Informed Neural Networks, 2025 IEEE 5th International Conference on Smart Information Systems and Technologies (SIST), Astana, Kazakhstan, 2025, pp. 1-7, doi: 10.1109/SIST61657.2025.11139217. [IEEE conference paper in Scopus]
[6] M. Nurtas, A. Kumarkhanova, T. Nessipbay. Deep Learning-Based Earthquake Magnitude Estimation Using Seismic Waveform Images. IEEE 5th International Conference on Smart Information Systems and Technologies (SIST 2025), Astana, Kazakhstan. [IEEE conference paper in Scopus] doi: https://doi.org/10.1109/SIST61657.2025.11139211
[7] M. Nurtas, A. Altaibek, A. Kumarkhanova, T. Nessipbay. CNN-LSTM-Based Forecasting of Peak Ground Acceleration from Early Seismic Waveforms. IEEE International Conference on Artificial Intelligence, Computer, Data Science and Analysis (ACDSA 2025), Antalya, Turkey. [IEEE conference paper in Scopus] doi: https://doi.org/10.1109/ACDSA65407.2025.11165806
[8] M. Nurtas, F.Tokmukhamedov, A.Ydyrs , S. Nurakynov, B. Iskakov, A. Altaibek. Application of finite element method for solving seismoacoustic modeling problems in poroelastic composite media. Engineered Scince Publisher. ISSN: 2576-988X (Print Version) ISSN: 2576-9898 (Online Version), V. 26, Desember 2023. doi: https://dx.doi.org/10.30919/es1030. Scopus, Quartiles: Q1, Percentile: 98%, CiteScore 14.9.
[9] Marat Nurtas, Zhumabek Zhantaev, Aizhan Altaibek, Serik Nurakynov, Berik Iskakov and Aizhan Ydyrys. Predicting the likelihood of an earthquake by leveraging volumetric statistical data through machine learning techniques. Engineered Scince Publisher. ISSN: 2576-988X (Print Version) ISSN: 2576-9898 (Online Version), V. 26, Desember 2023. doi: https://dx.doi.org/10.30919/es1031. Scopus, Quartiles: Q1, Percentile: 98%, CiteScore 14.9.
[10] Altaibek A., Nurtas M., Nurakynov S., Kaken A. A Study on Deep Learning-Based Predictive Modeling of Vegetation Dynamics in Kazakhstan through the Integration of CNNs, RNNs, and Satellite Imagery for Ecological Monitoring //Engineering, Technology & Applied Science Research. – 2025. – V.15(4). – P.24705-24714. (Scopus: Quartiles: Q2, Percentile: 56%, CiteScore: 2.9, SJR 0.332) DOI: https://doi.org/10.48084/etasr.11188
[11] Nurtas M., Zhantaev Z., Altaibek A. Earthquake time-series forecast in Kazakhstan territory: Forecasting accuracy with SARIMAX //Procedia Computer Science. – 2024. – V. 23. – P. 353-358. (Scopus: Quartiles: Q2, Percentile: 62%, CiteScore: 4.1, SJR 0.471) DOI: https://10.1016/j.procs.2023.12.216
ETC.