CV

Education

B.S. in Environmental Science, Lomonosov Moscow State University, Faculty of Soil Sciences, 2013 - 2017
M.S. in Environmental Science, Lomonosov Moscow State University, Faculty of Soil Sciences, 2017-2019
Ph.D in Data Science, Skoltech, CDISE, Moscow, 2023 (expected)

Research experience

Summer-Fall 2016: Research Assistant
- Laboratory of ecotoxicology, Department of Soil Science, MSU
- Duties included: analytical chemistry, bioassay
- Supervisor: Pukalchik Maria
- Project title: Variation of biological properties under the influence of humic substances and in conditions of model experiments with sod-podzolic soils contaminated with heavy metals
Fall-Winter 2018: Research Assistant
- Laboratory of chromatographic and mass spectrometric methods of water and soil analysis, Moscow State University
- Duties included: Mass-spectrometry, HPLC, GC-MS, ASE DIONIX
- Supervisor: Zavgorodnyaya Y.
- Project title: Determination of neonicotinoid pesticide residues in soil and water with solid phase extraction and HPLC-Q-TOF

Additional education

Mass spectrometry - semester course (Fall, 2018)
Python Programming - online course (2017)

Skills

Programming:
- Python(Numpy, Pandas, Plotly), R(ggplot2)
- Bash, Jekyll, HTML
- Docker, Singularity
- Slurm, HPC
Soil chemistry:
- Determination of organic pollutants
- Bioavailability of persistent organic pollutants in soils
- Heavy metall extraction
Analytical chemistry:
- Gas chromatography, HPLC,
- Mass spectrometry, ICP-MS, Q-TOF
- UV/Vis-spectroscopy, Solid-phase extraction (SPE)
Design:
- Adobe Illustrator, Photoshop
- Web: Tilda, WIX

Publications

Bayesian aggregation improves traditional single image crop classification approaches

Matvienko I., et al, 2020

Effect of Lignohumate on Soil Biological Activity on the Bali Island, Indonesia

Pozdnyakov L. A. et al., 2020

Sensitivity Analysis of Soil Parameters in Crop Model Supported with High-Throughput Computing

Gasanov M., et al, 2020

A New Multi-objective Approach to Optimize Irrigation Using a Crop Simulation Model and Weather History

Gasanov M., et al, 2021

Challenges in data-driven geospatial modeling for environmental research and practice

Koldasbayeva, D., Tregubova, P., Gasanov, M. et al. Challenges in data-driven geospatial modeling for environmental research and practice. Nat Commun 15, 10700 (2024). https://doi.org/10.1038/s41467-024-55240-8