Nowadays, several current researches take interest in opportunities provided by large amounts of data. Hence, there is a strong interest in deriving methods to approach large-dimensional problems. In these cases, classical methods fail, and thus modern theories must be developed.
Among several mathematical themes part of the high-dimensional research area one could mention random matrix theory, multivariate statistical analysis, probability theory.
The antieigenvector is the most turned vector by a positive definite matrix operator Σ with a connected antieigenvalue which is the cosine of the maximal turning angle.
Similarly, to the eigenvalue theory, all the antieigenvalues may be ordered as a spectrum from the smallest to the largest. That is, the second antieigenvector is the second most “turned” vector orthogonal to the first ones, and so on.
In her research, she will consider the knowledge of antieigenvalues of a random matrix and their probability distributions as an approach in statistical inference.
She is experienced in mathematical and statistical modeling, and has analytical skills. She is a strong education professional with a Master's degree in Technomathematics focused in data driven modeling from Lappeenranta University of Technology, Finland.
Member of organisations such as
- the World Economic Global Shapers (Kigali hub),
- the Rwandan Organization of Women in Science and Engineering (RAWISE), and
- the Organization for Women in Science for the Developing World (OWSD).