Projects

HSE University, a first-year course project.

  • CV: Math Recognition, OCR, Detection; NLP: RAG
  • Technologies: ultralytics, detectron, Roboflow, PyTorch, transformers, FAISS
  • A synthetic dataset was built for detection; YOLO and FasterRCNN were trained
  • A ViT was trained for math recognition, a pretrained model used for text
OCR, RAG 2026

BIOCAD: instance segmentation when labelled data is scarce.

  • CV: Instance Segmentation (Few-Shot / One-Shot / Small Data)
  • Technologies: transformers, detectron, PyTorch, OpenCV
  • Compared approaches: Supervised Learning, Zero-Shot, Few-Shot, Transfer Learning
Computer Vision

The Alabuga hackathon.

  • NLP: Sentiment Analysis on Named Entity Recognition
  • Technologies: natasha, transformers
  • Named entities recognised with a pretrained model from natasha
  • Two BERT models fine-tuned with masking entity: neutral vs non-neutral and positive vs negative
  • catboost tested as a baseline: text_features, word2vec, TF-IDF
NLP

Content moderation for the Pulse social network

T-Bank: multilabel text classification.

  • NLP: Multilabel Text Classification
  • Technologies: keras, catboost, sklearn, fasttext
  • Tested CNN, RNN and catboost with fasttext embeddings (catboost + TF-IDF as a baseline)
  • Focal Loss tried against class imbalance
NLP Demo ↗

Gender balance in company management

The DANO olympiad: an analytical study.

  • Technologies: pandas, numpy, scipy, sklearn, matplotlib, seaborn
  • Cleaned the dataset of outliers and missing values
  • Hypotheses tested with robust bootstrap methods: permutation test and Bootstrap Confidence Interval
Аналитика Demo ↗

Leaders of Digital Transformation and Itelma: a web service.

  • Time Series
  • Technologies: catboost, numpy, pandas, sklearn, scipy
  • Preprocessing pipeline: anomaly detection, interpolation of gaps, denoising
  • System for detecting key events and predicting complications (catboost, RandomForest)
Time Series 2025

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