Researchers

AI researcher and machine learning engineer focused on practical AI systems for real-world applications. My interests include AI for finance and neuroscience, applied machine learning, computer vision, healthcare AI, and optimization. I actively participate in research competitions, including Kaggle and NeurIPS, and achieved Kaggle Competitions Master status with a top-14 global ranking. Beyond research, I contribute to AI olympiads, education, and community building.

Life Sciences, AlfaCenter.ai

Machine Learning, AI, Startups, Publications

Я занимаюсь оптимизацией, машинным (федеративным) обучением и теорией обучения. Открыт к коллаборациям!

Evals, datasets

My current research focuses on the fundamental aspects of learning, specifically exploring benign overfitting, long-tailed distributions, and the hardness of learning.

Causal representation learning and foundation models for neuroscience

PhD researcher at KTH working on neuromorphic event cameras and low-latency sensor fusion for real-time human-robot collaboration. My work combines deep learning and spiking neural networks for depth estimation and object tracking, using a 7-DOF robotic arm and event cameras as the primary experimental platform.

I work on information elicitation in goal oriented dialogues. I'm also interested in latent reasoning, knowledge representation and neural memory for LLMs.

I am focused on deploying ML models on IoT devices

My primary area of interest is Uncertainty Quantification for LLMs. In very simple terms, I am trying to understand how we can determine when a model knows - and doesn't know - what it is talking about. I also have some interest in LLM safety and trustworthiness, but more so as a casual learner than a practitioner.

My current research focus is centered on Applied Machine Learning, Deep Learning, and Multimodal AI, with a strong interest in document understanding, OCR, vision-language models, and LLM-based systems. I am currently exploring synthetic data generation for low-resource Turkic languages, especially for OCR and document recognition tasks. This includes building realistic multilingual document datasets, designing generation pipelines, and preparing data for fine-tuning modern OCR/VLM models. Another important direction of my work is the development of practical AI systems based on RAG, LLM agents, and multimodal reasoning. I am interested in turning research ideas into usable AI tools for real-world domains such as document processing, legal technology, education, and urban surveillance.

Архитектура ИИ моделей и способы их обучения

I work on CV and NLP for Kazakh and other low-resoruce languages.

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.

I am a Ph.D. researcher in Computer Science at SDU University, working on NLP, machine learning, and AI-generated text detection. My research focuses on detecting AI-generated and AI-obfuscated text using transformer models, multilingual NLP, and contextual representations.

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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.

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.

My research interests include computer vision, multimodal AI, medical image analysis, object detection, vision-language models, and interpretable machine learning. I am particularly interested in developing robust AI systems for real-world applications and healthcare.

Developing supervised and unsupervised methods to classify guppy fish social behavior within population.

Code-Switching aware STT KK-RU-EN Code-Switching audio dataset OCR for Kazakh Hand-Written Text