Master's Thesis in the Pierre Bost Laboratory

• Developed spatial statistical models for 3D imaging datasets, revealing sampling-induced biases
• Worked on mathematical foundations of 2D sampling; built simulations to model several tissue types

Bachelor's Thesis in the Sergey Rasin Laboratory

• Engineered a dCas9-CTCF fusion construct and targeted it to a predefined genomic locus, modulating both the epigenetic landscape and local 3D genome topology
• Publication: Ectopic recruitment of the CTCF N-terminal domain with two proximal zinc-finger domains as a tool for 3D genome engineering. Int. J. Mol. Sci. 2025. DOI: 10.3390/ijms26157446

Programming

• R language: ggplot2, tidyverse, DeSeq2, dplyr
• Python language: Numpy, Pandas, Matplotlib, Seaborn, Sklearn, PyTorch
• Linux, command line (bash, awk, pipelines), git
• Website layout: HTML, CSS, JavaScript
• Basics in C++, databases (SQL), algorithms

Machine Learning

Github page: github.com/gregothebyteknight

• Classical methods: regression, classification, kNN, random forests, tuning, bayesian methods
• Neural networks: MLP, CNN, RNN, LSTM, Transformers, AE, GNN
• Knowledge of mathematics, physics and statistics from university and beyond

Bioinformatics

• Data analysis: bulk & single-cell RNA-seq, spatial transcriptomics, Hi-C, ChIP-seq, NGS
• Databases: PDB, UniProt, NCBI
• Command line tools and work on servers
• Profound biological education (from invertebrates to physiology), to understand the data

Molecular Biology

• Basic laboratory skills
• Hi-C, ChIP-seq & qPCR, cell culture management, cell sorting

• English – C1 (IELTS 7.5, Master’s in English)
• German – A2 (B1 Deutsche Welle certificate)
• French – B1 proficiency level (ENS-PSL language courses, 1.5 years in Paris)

MSc in Bioinformatics

BSc in Molecular Biology

• Deep Learning course (NN & Computer Vision) at MIPT School of Applied Mathematics & CS
• Profound two-year higher mathematics and one-year physics courses. Year+ in biophysics and physics of protein