### Research Interest

Statistics, Machine Learning, Natural Language Processing

Dr. Assylbekov received the specialist degree in applied mathematics from M.V.Lomonosov Moscow State University, Ph.D. in mathematics from Hiroshima University, in 2007 and 2008 respectively. In October 2011 he joined Nazarbayev University as a teaching assistant, starting from June 2013 he was working as an instructor, and starting from January 2017 he works as an assistant professor. Currently, he is interested in developing deep neural networks for natural language processing tasks.

Assylbekov, Z., Jangeldin, A.

Squashed Shifted PMI Matrix: Bridging Word Embeddings and Hyperbolic Spaces

(2020) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12576 LNAI, pp. 336-346.

Assylbekov, Z., Takhanov, R.

Context vectors are reflections of word vectors in half the dimensions

(2020) IJCAI International Joint Conference on Artificial Intelligence, 2021-January, pp. 5115-5119.

Uteuliyeva, M., Zhumekenov, A., Takhanov, R., Assylbekov, Z., Castro, A.J., Kabdolov, O.

Fourier neural networks: A comparative study

(2020) Intelligent Data Analysis, 24 (5), pp. 1107-1120.

Assylbekov, Z., Takhanov, R.

Context vectors are reflections of word vectors in half the dimensions

(2019) Journal of Artificial Intelligence Research, 66, pp. 225-242.

Makazhanov, A., Myrzakhmetov, B., Assylbekov, Z.

Manual vs automatic bitext extraction

(2019) LREC 2018 – 11th International Conference on Language Resources and Evaluation, pp. 3834-3838.

Myngbay, A., Bexeitov, Y., Adilbayeva, A., Assylbekov, Z., Yevstratenko, B.P., Aitzhanova, R.M., Matkarimov, B., Adarichev, V.A., Kunz, J.

CTHRC1: A new candidate biomarker for improved rheumatoid arthritis diagnosis

(2019) Frontiers in Immunology, 10 (JUN), art. no. 1353, .

Assylbekov, Z., Takhanov, R.

Reusing weights in subword-Aware neural language models

(2018) NAACL HLT 2018 – 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies – Proceedings of the Conference, 1, pp. 1413-1423.

Takhanov, R., Assylbekov, Z.

Patterns Versus Characters in Subword-Aware Neural Language Modeling

(2017) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10635 LNCS, pp. 157-166.

Jangeldin, A., Assylbekov, Z.

Simple and accurate method for parallel web pages detection

(2017) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10191 LNAI, pp. 238-247.

Assylbekov, Z., Takhanov, R., Myrzakhmetov, B., Washington, J.N.

Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones

(2017) EMNLP 2017 – Conference on Empirical Methods in Natural Language Processing, Proceedings, pp. 1866-1872.

Assylbekov, Z., Melnykov, I., Bekishev, R., Baltabayeva, A., Bissengaliyeva, D., Mamlin, E.

Detecting value-added tax evasion by business entities of Kazakhstan

(2016) Smart Innovation, Systems and Technologies, 56, pp. 37-49.

Assylbekov, Z., Nurkas, A., Mouga, I.R.

A statistical model for measuring structural similarity between webpages

(2015) International Conference Recent Advances in Natural Language Processing, RANLP, 2015-January, pp. 24-31.

Asylbekov, Z.A., Zubov, V.N., Ulyanov, V.V.

On approximating some statistics of goodness-of-fit tests in the case of three-dimensional discrete data

(2011) Siberian Mathematical Journal, 52 (4), pp. 571-584.

Zhenisbek, A.

Convergence rate of multinomial goodness-of-fit statistics to chi-square distribution

(2010) Hiroshima Mathematical Journal, 40 (1), pp. 115-131.

BS

- MATH 161 – Calculus I
- MATH 162 – Calculus II
- MATH 310 – Applied Statistical Methods
- MATH 321 – Probability
- MATH 322 – Mathematical Statistics
- MATH 440 – Regression Analysis
- MATH 441 – Design of Experiments
- MATH 446 – Time Series Analysis
- MATH 449 – Statistical Programming

MS

- MATH 540 – Statistical Learning