I specialize in Computer Vision, LLMs, NLP, classic ML, and tensor methods — turning complex data into intelligent solutions.
I am a Machine Learning Engineer with over 4 years of experience in developing and deploying end-to-end ML solutions.
My passion lies in applying state-of-the-art algorithms to solve real-world problems in medicine, HR analytics, and multimodal data analysis.
Currently, I am pursuing a Master's degree in Applied Mathematics and Computer Science at Tomsk Polytechnic University, balancing my studies with work and research.
Here are the key areas I'm currently working on.
You can find more projects in my repositories.
Developing and implementing ML algorithms for automated analysis of medical images (CT scans).
Technologies: PyTorch, UNet, Attention UNet, OpenCV, Radiomics.
The software is state-registered (Rospatent No. 2025610317).
Applying tensor decomposition to reduce dimensionality and analyze complex spatio-temporal data.
Technologies: Python, NumPy, TensorLy, Reinforcement Learning.
Creating a web service to monitor and forecast salaries based on data from the HeadHunter API.
Technologies: Flask, FastAPI, Playwright, Docker, Celery, Redis.
- EPIFAT — Module for Automatic Segmentation of Epicardial Adipose Tissue on Cardiac CT Images.
Certificate of State Registration of Computer Program No. 2025610317, 2025. - Tensor-Based Modal Decomposition for Reservoir Study Optimization, Conference Paper, 2025.
- Automatic Image Segmentation and Quantitative Assessment, XXI Int. Conf. “Perspectives of Fundamental Sciences Development”, 2024.
- Radiomic Analysis of Cardiac MRI Images in Cine Mode, Digital Diagnostics Journal, 2024.
- Beam Parameters Restoration at the NICA Accelerator Complex, START, JINR, 2023.
Here you can see my GitHub activity. The data is updated automatically.