My current research centers on computational oncology, where I develop Bayesian models to reconstruct clonal structures from single-cell DNA/RNA sequencing data. By combining variational inference, stochastic processes, and topological data analysis, I aim to uncover the hidden evolutionary dynamics of tumors and support more precise cancer diagnostics.
With a background in nonlinear dynamics and infinite-dimensional systems, I bring a deep theoretical foundation to complex, high-dimensional data problems. While my earlier work focused on seismic hazard assessment, I now apply similar mathematical frameworks to biological systems, exploring the intersection of probabilistic modeling, optimization, and biomedical data analysis.
My research interests are in applied probability and statistics, with a focus on statistical seismology and cell biology.
Advanced knowledge of 3 programming languages Python(SciPy/Numpy/Matplotlib/ (PyTorch, Scikit-learn, Pandas), C/C++) and related fields (SQL, Network/CISCO, LinuxOC/Bash scripting, HTML5, CSS3, Docker, Git, SVN etc.). Bayesian statistics, applied statistics, optimization, machine learning, deep learning, stochastic processes, algorithms and data structures, concurrent programming, distributed programming - are the things everyday I'm working with and in what I'm improving my skills constantly.
I discovered a love for pure mathematics during my bachelor studies in applied mathematics. So now I self-study pure mathematics as a hobby!
My interests include:
- Applied statistics
- Computational statistics
- Bayesian networks
- Probabilistic graphical models
I am always on the lookout for new projects to work on and new people to collaborate with. Do check out my repositories and feel free to reach out if you would like to work on any of my existing projects or if you think that I would be a good fit in your project.
- Armenian 🇦🇲
- Russian 🇷🇺
- English 🇬🇧
