Position: Computer Vision Researcher
Category: Data Science
English level: intermediate
Years of experience: 0.5

As a Computer Science student, I have been among the top 10 students two times following the results of four sessions. I took 3rd place in the math faculty olympiad and the KPI OPEN contest with my teammates in 2020. Unfortunately, I don't have any Kaggle medals for now, but I do my best to get the first one.
First of all, it is worth mentioning that I started my ML journey from the famous ML course and DL specialization by Andrew Ng. Furthermore, I had a deep dive into the Computer Vision area with UMich CV for the DL class.
	To make my dream of being a CV Engineer come true, I have tried myself in various Kaggle competitions. Working on "Flower Classification with TPUs.", "RSNA Pneumonia Detection" and "Generative Dog Images" I have learned how to organize my code in such a way to make iterative updates easily. I had acquainted with modern image classification (ResNeXt, MobileNet, ViT, and NFNet), object detection (Faster-RCNN, FPN, YOLO, DETR), segmentation  (U-Net, Mask-RCNN), and generative model (VAE, DCGAN, WGAN) architectures. To interpret models' behavior and understand what features models are learning during training, I have applied such techniques as Saliency Maps and GradCAM. Moreover, Kaggle has provided me with the opportunity to practice with GPU and TPU training.
	Later, during the data analysis coursework at the university, all that practical knowledge came in handy. My groupmate and I developed the model to cope with the task of product matching. This project taught me how to work in a team.
	Additionally, I have experience with frontend and backend development, which I have gained working on a simple blog at university. I have also studied Git, and some DevOps stuff, including creating multi-stage Docker builds for the Node app, setting up Traefik as reverse-proxy, and developing a CI/CD pipeline using GitHub Actions.