Researchers

I am a school student in Kazakhstan interested in how language models solve mathematical problems, especially in algebra and geometry. My first research paper compared Qwen2.5-Instruct models with 1.5B, 3B and 7B parameters on 287 olympiad problems from Omni-MATH. I examined how performance varies across mathematical domains and difficulty levels, and whether missing diagrams explain geometry errors. Larger models improved much more in algebra than in geometry, and removing problems with missing diagrams did not close the gap. I would like to continue exploring mathematical reasoning, model evaluation and error analysis.

Adversarial Attacks in LLMs

actively co-authoring PhD doctors from Europe Universities in Project of OCD solution by AR(1) prediction and Math Probability Theory

Research on the latest methods for identifying preferences.

My research sits at the intersection of ML, LLMs and computer vision, with a focus on inference optimization (KV-cache compression, quantization) and ensemble methods for large-scale forecasting. In my recent paper (IJCISIM 2026, DOI: 10.70917/ijcisim-2026-4333) I showed analytically and empirically that geometric log-space ensembling is the MAPE-optimal aggregator for log-normal targets, reaching MAPE ≈ 4.77% on an 18,000-store retail forecasting challenge. Open directions: efficient LLM inference, vision-language models, and transferring multiplicative-error ensembling to new domains

Im open for everything, just hit me in tg. Be straight, say what you want.

My research sits at the intersection of machine learning and clinical medicine, focused on how AI can support doctors in real clinical environments

Interdisciplinary researcher and developer performing technical coursework and applied research in computer vision and generative models. Experienced in deep learning model fine-tuning, diffusion-based image generation.

recreating the Kazakh heritage and culture

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I have worked on Kazakh language models such as KazBERT(Eraly-ml/KazBERT) and others, recently i won shared task at SemEval workshop it was about how to detect ai in code and how to detect hidden ai code, system paper of our work was accepted and it will be published in ACLAnthology.

idk i research cool stuff

Research focus: any sort of evaluation methodologies for GenAI of any modality.

First-year master's student in Data Science at KBTU with a background in data engineering at the State Credit Bureau. Interested in machine learning research and in applying ML to large-scale financial and credit data. Planning PhD applications in Europe and looking for a research group to collaborate with.

Currently focused on AI in medicine, especially on detection abnormalities in medical images

Research and simulations of PDE problems by using Fourier Neural Operator's architecture of neural networks.

Machine Learning Engineer at tilmoch.ai, where I mostly work on NLP applications for Turkic languages. At tilmoch.ai, I've worked on Tahrirchi Editor, tilmoch.ai Translator and Misollar. We also contribute to the open-source Turkic NLP community with the biggest Uzbek text corpora (UzBooks and UzCrawl), the SOTA Uzbek encode models (tahrirchi-bert-base and tahrirchi-bert-small), Karakalpak and Southern Uzbek Machine Translation models and dataset. At the same time, I am a BSc. in AI student at MBZUAI, where I am digging deeper into fundamentals of AI Engineering.

2 year Master degree Student

Interested in geometry

Information Technologies graduate and researcher. Published research in modeling, forecasting, and processing multidisciplinary area of AI. Open to collaboration on projects and research in machine learning and AI — the best way to reach me is by email.

Researched on Kazakh NLP, open for NLP/RL collaborations

Have written courseworks about brain drain in Kazakhstan and its impact to economic growth

ACR on tote jazu, use of RL for better eval

My research focuses on the interplay between machine learning, optimization, and mathematics. I develop algorithmic and computational methods for solving challenging problems in machine learning theory, discrete mathematics, and theoretical computer science. A recurring theme of my work is the use of large-scale optimization and high-performance computing as tools for scientific discovery. My contributions span machine learning theory, kernel methods, natural language processing, graph algorithms, combinatorial optimization, and high-dimensional geometry. In recent years, I have become particularly interested in computational approaches to mathematical research, where modern optimization techniques and GPU-based computation can uncover structures that are difficult to access through traditional analytical methods alone. More broadly, I aim to build bridges between theoretical foundations and practical computation, developing methods that are both mathematically rigorous and computationally effective.