Developing ML solutions and performing advanced statistical analysis for a high load performance-oriented demand side platform.Environment and Tools: Python, Machine Learning, XGBoost, scikit-learn, AWS.

Applying Statistical and Machine Learning techniques in the mining industry: haul tracks transportation optimization, applying different ML techniques to predict blast-induced rock movements, advanced statistical analysis, etc.Environment and Tools: Python, JupyterLab, Statistical Analysis, Random Forest, LightGBM, GCP.

Developing a Product Search Engine for an on-line market place. The engine was built by using the probabilistic relevance model with the Okapi BM25 relevance function in the core. An artificial intelligence algorithm that ranges products according to the similarities with a given user query was developed.Environment and Tools: Python, JupyterLab, Information Retrieval, Probabilistic Relevance, Okapi BM25, AWS.

1) Developing the so-called “Frequency Recommendation System” for the purpose of email-marketing based on the state-of-the-art machine learning algorithms (random forest, XGBoost, etc.). Such kind of systems a central to the contemporary email-marketing systems, since it is crucial to predict the most accurate click-through rate (CTR) as possible to ensure the subscribers have the best experience. The experience in developing of the system is summarised in series of blog articles (blog eSputnik.com).Environment and Tools: R, RStudio, Python, Feature Engineering, Ensemble Learning, XGBoost, Random Forest, MS SQL Server.2) Developing a configuration module of a Shiny oriented BI system. The main task was to develop a connection between Shiny API and MongoDB to customize the process of interaction with the BI system.Environment and Tools: R, RStudio, Shiny, MongoDB, Data Visualization.3) Developing several BI systems for the purpose of email-marketing based on the Google cloud technologies (Data Studio + BigQuery). The BI systems visualize several email-marketing KPI’s and help to understand better the behaviour of different groups of subscribers.Environment and Tools: R, RStudio, GCP, Google Data Studio, BigQuery, ETL, Feature Engineering, Machine Learning/Deep Learning.

Exploratory Data Analysis and Visualizations for an insurance company. Analysis of claims and bills, developing a model for counting claims resolution time and its visualization. Cohort analysis and visualization of customers behaviour.Environment and Tools: R, RStudio, PostgreSQL, Data Visualization

Taught lectures, and conducted practical exercises and labs in general physics.

Taught lectures, and conducted practical exercises and labs in such disciplines: general mathematics, theory of probability and statistics.

Areas of research work:1) non-linear mathematic physics;2) group analysis of partial differential equations;3) boundary value problems of the Stefan type.Google scholar: https://scholar.google.com/citations?user=t6BboGAAAAAJ&hl=enScopus: https://www.scopus.com/authid/detail.uri?authorId=35172995100

Taught lectures, and conducted practical exercises and labs in such disciplines: general mathematics, theory of probability and statistics, theory of optimisation.