Researchers

Combining ontologies with LLMs to extract, enrich and use formalized knowledge

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

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

2 year Master degree Student

Currently building a multi-class affect detection pipeline that classifies student emotional states from behavioral and keystroke data across 243 students and 400+ features. Using student-level nested GroupKFold cross-validation to ensure leakage-safe evaluation, with Random Forest and XGBoost as primary models, and addressing feature selection stability via rank-averaging across folds.

Research areas: 1. Neuromophic Vision: event cameras, face and facial landmark detection, large-scale dataset creation 2. Remote Sensing: City Identification via Satellite data, City Sustainability prediciton via Satellite Data, Construction Sites Detection and Segmentation via Satellite data (Instance Segmentation)

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.

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.

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

recreating the Kazakh heritage and culture

My research focuses on large language models, reinforcement learning, and AI for education. I am particularly interested in post-training methods for LLMs, including PPO, DPO, and GRPO, as well as retrieval-augmented generation (RAG), efficient model training, and reasoning capabilities. My recent work explores Socratic tutoring with LLMs, reinforcement learning for improving educational dialogue systems, and scalable distributed training of foundation models. I also have experience building diffusion models, reinforcement learning agents, and high-performance AI systems from scratch in PyTorch. I am interested in collaborating on research related to LLM alignment, reasoning, AI agents, and open-source foundation models.

Evals, datasets

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

Working as a Research Assistant at Nazarbayev University.

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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.

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.

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 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.

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.