I'm Aleksei Trubin, a Data Scientist and Data Engineer with over 9 years of experience in data analysis, machine learning, and database management. I hold a Ph.D. from the Faculty of Forestry and Wood Sciences at the Czech University of Life Sciences Prague (CZU).

💡 Passionate about transforming complex data into actionable insights to drive data-driven decision-making across various industries.

• Programming & Data Analysis: Python, R, SQL (PostgreSQL, Oracle), PySpark
  • Python Libraries:
    • Data Manipulation: pandas, numpy, xarray, rioxarray
    • Geospatial Analysis: GDAL, shapely, geopandas, fiona, rasterio, ArcPy
    • Machine Learning: scikit-learn, scipy
    • Data Visualization: matplotlib, seaborn, plotly, dash
• Data Engineering & Big Data: Databricks, AWS (S3, Lambda), Azure Databricks, Docker, Linux
• Data Visualization: Power BI, Tableau, Mapbox, Leaflet
• GIS & Remote Sensing: QGIS, ESRI ArcGIS Pro, CloudCompare, GRASS GIS, Google Earth Engine
• Tools & Platforms: VSCode, Jupyter Notebook, Google Colab, GitHub, GitLab
• Project Management: Agile methodologies, Jira, Confluence

Developed a Jupyter notebook for generating trap and antiattractant placement strategies using 30-meter resolution satellite data. This project enhances pest management in forestry by optimizing trap placements, leveraging spatial analysis and automation.

Features:

• Customizable trap spacing and edge detection for strategic placement.
• Visualization maps for proposed placements.
• Automation scripts to streamline the workflow.

Created a workflow to process LiDAR .laz files for generating Canopy Height Models and producing shapefiles for trees and bushes. This enables accurate biomass estimation and vegetation analysis using CloudCompare and Python scripting.

Highlights:

• Processing LiDAR data using CloudCompare in silent mode.
• Differentiation between trees and bushes based on height data.
• Area calculation for biomass estimation.

Developed Jupyter notebooks for analyzing mangrove cover changes and assessing coastal flood risks. The project utilizes remote sensing data and spatial analysis to monitor environmental changes and support conservation efforts.

Key Components:

• Integration of multiple data sources for comprehensive analysis.
• Time series analysis of mangrove coverage from 2016 to 2020.
• Risk assessment models for coastal flooding.

June 2024 – Present
AT&T (US Telecommunications Company)

March 2021 – May 2024
Landviser (US Environmental Consulting)

Ph.D. in Applied Geoinformatics and Remote Sensing in Forestry
Czech University of Life Sciences Prague (CZU)
2019 – 2024

• Thesis: Link to Thesis

Certifications & Courses

• IBM Python Data Science Professional Certificate (2023)
• Ecological Data Processing (R Programming and Statistics Course) – CZU (2021)

• LinkedIn: linkedin.com/in/aleksei-trubin
• ResearchGate: researchgate.net/profile/Aleksei-Trubin
• Email: aleksei.trubin@example.com

Note: My GitHub repositories showcase some of my public projects. I'm continuously working on exciting data science and engineering projects, so stay tuned for updates!

• Forest Ecology and Management
  Detection of green attack and bark beetle susceptibility in Norway Spruce: Utilizing PlanetScope Multispectral Imagery for Tri-Stage spectral separability analysis

• Frontiers in Forests and Global Change
  Detection of susceptible Norway spruce to bark beetle attack using PlanetScope multispectral imagery

• Forest Ecology and Management
  Northernmost European spruce bark beetle Ips typographus outbreak: Modelling tree mortality using remote sensing and climate data