In 2007, when I got my degree in Applied Mathematics from Moscow Institute of Physics and Technology (MIPT), artificial intelligence was still more of a concept than a working industry. It took over a decade of managing software development projects in finance, e-commerce and online gaming before I discovered my passion for data science and building machine learning systems with my own hands.

  • Tools: Python (numpy, pandas, sklearn, matplotlib), R (data.table, ggplot2, caret), SQL
  • Algorithms: prediction and classification, SVM, KNN, PCA, random forest, gradient boosting, ensembling, time series analysis, recommender systems, anomaly detection
  • Deep learning
    • CNN: image classification, object localization, object detection, landmark finding, face recognition
    • RNN: GRU, LSTM, word embeddings, attention models, trigger word detection
    • Technology: TensorFlow, Keras
  • Statistical inference: hypothesis testing, regression models

Some projects that I’ve done:

WordAlign

Word alignment for Russian-Chinese bitext using BERT contextualized embeddings and further fine-tuning on unique parallel corpus

SwiftPredict

Ngram text prediction model for mobile keyboards that can be trained from scratch on a laptop, occupies only 219 MB RAM and provides suggestions within 22 msec

Ships detection in satellite imagery

96.77% F1 score detecting ships in satellite imagery with imbalanced classes, using transfer learning with VGG16 and Inception v3 CNNs

Formal education:

  • Specialization in Data Science, Johns Hopkins University, 2021 (expected)
  • Specialization in Deep Learning, deeplearning.ai, 2020
  • Bachelor’s degree in Applied Mathematics, Moscow Institute of Physics and Technology (MIPT), 2007