Research
My group develops scalable algorithms for PDE simulation by combining randomized numerical linear algebra with hierarchical structure and performance-aware implementation. I’m especially interested in methods that are both theoretically grounded and practical on modern architectures.
- Randomized compression and sketching
- Hierarchical matrix methods and fast solvers
- Preconditioning and scalable implementations
- Fast solvers for large-scale PDEs
- Robust approximations with error control
- GPU-friendly kernels and parallel scalability
Prospective Students
I’m looking for PhD students (and strong MS/undergraduates) interested in scientific computing, numerical linear algebra, and high-performance computing. If you’re interested, please email me with your CV and a short note about your background and what kinds of problems you’d like to work on.
- Linear algebra, numerical analysis, PDEs
- Some programming experience (Python/C/C++)
- Interest in computation and algorithms
- Read one paper you’re excited about and tell me why
- (Optional) share code, GitHub, or a writing sample
News
Recent updates
- Mar 2026 Serving on the program committee for ISC 2026 in the Algorithms & Performance area/track.
- Dec 2025 New preprint: “A fast spectral overlapping domain decomposition method”.
- Aug 2025 Joined the faculty at The Ohio State University. Go Bucks!
- Jun 2025 Plenary talk at the Householder Symposium XXII on hierarchical and randomized solvers.
Selected Publications
Full list: Google Scholar
Contact
Email: yesypenko.1@osu.edu
Office: MW 740
You can also find my CV and publication list via the links above.