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

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)

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 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.

I am focused on deploying ML models on IoT devices

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

I research multimodal deep learning for mental-health assessment — detecting suicide risk, depression, and emotion from text, audio, and video using transformer ensembles and fine-tuned LLMs on real clinical and speech-emotion datasets.

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

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.

Research in biomedical AI, focusing on EEG representation learning, medical imaging, and multimodal EEG-to-fMRI translation using efficient deep learning architectures.

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.

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.