Diversity, Equity & Inclusion

Vadim, 2023

The academic duties require high-quality teaching and organization of students' self-studies. It is important to remember students' varying backgrounds and to know that treating everyone the same way is undesirable. In self-study, it is vital to set reasonable goals for various activities. I believe the most productive way of study is creating groups of students and researchers with different levels and specializations. It motivates students to contribute and exchange the ideas and results of their research.

As a university professor, I am glad to work with students from different countries. In 2019-2020 I received the Segalovich prize for my impact on scientific society development in the CIS countries. This impact comes from my activity related to citizen science and student science. I designed the course "My first research paper" to join students and researchers of diverse ages and experiences working on a shared problem according to their roles as drivers, consultants, and experts. The course addresses mixed learning styles. The students write texts, run computational experiments, reason their research with theory, deliver talks and submit research results for publication in scientific journals.

Over the last fifteen years, our department has graduated several hundred students, most of whom have a doctoral degree. As a professor, I supervised over fifty MS, BS, and PhD students. All of my students now are successful researchers working in various countries. A significant part of them is delivering lectures and teaching students in universities.

A decent way to include the students and general audience in collaboration and competition is by running mathematical or engineering contests and hackathons. We have these activities in the educational process. For destinated courses, we set problems for several groups of students and allowed them to collaborate. Here are a few examples. "Development of Intelligent Systems" studies how to program AI systems in student groups, "Fundamental theorems of Machine learning" studies how to find the reasoning behind research statements, and "Dissemination of research results" teaches students how to explain a sophisticated scientific message to a wide audience.

In the future, I would like to develop teaching and organizational methods that involve students of various backgrounds in scientific research of high complexity. There are at least three welcomed forms:

1. A student is happy to study a scientific paper and verify a code or to program a computational experiment. It boosts the foundation of scientific reasoning and moves from pure science to its applications: the machine learning experiment assumes practical measurements.
2. With the teacher's help, a student creates a theoretical reasoning of an empirical model or method suggested in conference papers, thus impacting pure science.
3. A student can illustrate a sophisticated method with simple examples so that it can be easily understood and disseminated.