About
Hello, dear visitor!
My name is Gleb Karpov and I am a researcher in the field of applied mathematics, and university lecturer in related fields. In short: applied mathematics — how to make numbers run efficiently on a computer. In more detail: I have worked on numerical linear algebra, fast matrix factorizations, uncertainty quantification, and surrogate modeling of computationally expensive models. Nowadays I focus on machine learning for telecommunications and signal processing.
In parallel, since 2021 I have been teaching at HSE University. Now I give lectures on probability & statistics and linear algebra in flagship programs at the Graduate School of Business and in a Master’s program at the Faculty of Computer Science, both in russian and english.
Teaching has been with me since my first year of university: for many years I taught at a summer school for high-school students. Along the way I realized I can build clear emotionally engaging stories without the dry, passion-free style of the post-Soviet lecturers I once listened to. I try to give lectures with energy — as if I were on a theatrical stage — so a lecture is not a lullaby, but something that sparks emotion and curiosity. I especially enjoy lecturing in English.
But having said all that, I do not want teaching to be my main activity. It feels like becoming the geography teacher who has never left their village. I am moving toward teaching as an art, as a craft, as a creative outlet, and simply as a way to do something good for the living world.
In my free time I enjoy tons of music and singing, and a moderate-adrenaline outdoor activities: snowboarding in winter, and road cycling in summer.
I am endlessly fascinated by the world — and by how abstract ideas that live in our minds take shape and actually work in reality. I take great pleasure in the history of science and philosophy: the aesthetic thrill of watching centuries of thought lead to where things stand today.
Pale Blue Dot admirer.
Professional Interests
- Fast matrix computations: methods and low-level code
- Tensor methods for Machine Learning
- ML for Signal Processing and Communications
- Applied Statistics
- Modern Math education technologies
