Position: Computer Vision Engineer
Category: Data Science
English level: upper
Years of experience: 1.5

**Latest Pet-project:**

Steel Defect Segmentation:

During this project (data from Kaggle) it was interesting for me to explore training opportunities with a small dataset with unbalanced TP/TN classes for segmentation.
I went through different encoders for UNet and explored impact of different segmentation losses for unbalanced data on training process. 

Results can be found on my GitHub.
Data Scientist with 1.5-year commercial experience in Computer Vision field and classical ML, including image segmentation, sensible fine-tuning tasks, EDA and building different architectures from scratch.

Also, I have some theoretical knowledge in GANs (hobby field).

**EMPLOYMENT HISTORY**:

 - Computer Vision Engineer at Under NDA, Kharkiv (September 2020 — August 2021)

Worked on a mobile camera app (B2C product, under NDA).
The main task was to implement several architectures from papers and to find the ones that can be fine-tuned best with the highest Wasserstein metric value (optimal backbone and custom head) for the definite CV task.

The models should have been optimized and worked in real time. (frame flow from iPhone camera)

 - Computer Vision Intern at Quantum, Kharkiv (June 2020 — August 2020)

Gathered experience in image segmentation of soil erosion from satellite data.
The tasks were to prepare data from Sentinel-2 and to find the best architecture for the task.

Higher education:
- V.N. Karazin Kharkiv National University, faculty of Mathematics and Computer Science