Through my career I have had the opportunity to develop excellent skills in teaching as a lecturer, a hands-on activity supervisor and as a graduate student scientific adviser.

As a lecturer I have been invited to teach at several summer and winter schools. My main contribution as a lecturer was at the international summer school in the National Technical University of Ukraine “Kyiv Polytechnic Institute” (NTUU KPI), where I was given the highest student evaluation among the lecturers, three times. In 2011, I organized a new specialization (about 10 short courses 2-5 lectures each) in the summer school entitled “Neuroscience” where I also taught several courses and supervised hands-on activity. Many researchers all around the globe contributed lectures or short courses to this specialization, including such honored professors as John Rinzel, Péter Érdi, Tatyana I. Aksenova, Gennady Cymbalyuk, Witali L. Dunin-Barkowski, Romain Brette and many others. For many students this summer school acted as a springboard into Neuroscience: some of them became students in master or Ph.D. neuroscience programs in Ukraine, Europe, and the US. Read recommendation letter from rector (Chancellor) NTUU KPI

In 2015, I contributed to the Special Topic Course ANAT 280 – Computational Neuroscience at LSUHSC as a lecturer on the following topics: "Spatial Neural Models, Cable Equation and Dendritic Integration" and “E/I Balance – Oscillatory Variable of Population Activity”. My lectures were well received by students.

In November 2018, I was invited by the Computational Biology Student Association at Tulane University (COMBaT) to give a seminar about my research topic. This was specifically challenging, because it required discussion of deep and complex concepts with an unprepared audience i.e., students. In this presentation I focused on the importance of our findings for Neuroscience, rather than scholastic mathematical proofs and definitions. Presentation of most of our results in simple graphical form helped the students to gain an intuitive understanding of studied neural networks. It should be noted that this simple presentation triggered students’ interest which resulted in a barrage of questions and led to active discussion after the seminar. During the discussion, the most mathematically and biophysically advanced students revisited pure mathematical derivations hidden behind simple graphical presentations and were able to appreciate both mathematical and neuroscience sides of our research.

Previous to my current position, I held the post of a principal investigator (PI) and head of lab in one of the leading Russian Universities (equivalent to a tenure-track position in the US). Six undergraduate and master’s students graduated as Bio- and Medical physicists under my mentorship from 2005 to 2011. My first Ph.D. student, Viacheslav Vasilkov graduated in May, 2012 with a Ph.D. in Biophysics (he is currently a Postdoctoral researcher at Oldenburg University, Germany); my second Ph.D. student, Irina Ischenko, graduated in June, 2015*with a Ph.D. in Physiology (after graduation she worked as Postdoctoral researcher at the University of Eastern Finland).

In classes, I like to show how the specific theory, model or method I am discussing with the students was developed. From my experience I know that students appreciate more fully the outcome of the scientific discovery – be it a theory, a model or a methodology, if they understand the challenges that the scientist experienced during its conception. Therefore, for any topic I usually present the historical context and challenge students to try and enter into the shoes of the scientist to see how they approached the problem. Every scientific discovery is an exciting moment for any researcher. However, common wisdom notes that a discovery doesn’t start from word “Eureka!” but from words “Hm, it’s funny!” (or however that sounds in the scientist’s mother tongue). Therefore, my goal in the classroom is to guide students from the words “Hm, it’s funny!” towards the exciting moment when you understand how and what the answer is of a puzzling problem. The main strategy is: first, involve students to research the historical context; second, develop skills and knowledge while we work on the specific problem; and finally, develop the sense of a coherent theory, a well-defined model or a validated method when we arrive at the final result. The purpose of this strategy is to develop both intuition as well as formal skills needed for rigorous quantitative science.

When I teach technical topics, for example in classes where students are introduced to popular software for modeling or in programming classes, I prefer to give very short lectures and focus on hands-on activity. I specifically design tasks for each topic. For example, in my programming classes, students would work on a task to help develop their understanding of the logical structure of algorithms, develop a sense of an optimized, tested, bug-free software and understand the importance of a clear coding style.

While I am fully qualified to teach general and technical courses, including aspects of computer science, mathematics and biophysics, I would like to develop and teach courses deeply connected with quantitative modeling in neuroscience and more generally related to computation, coding and information processing in neurons and neural networks.

For further specialization, students will be encouraged to participate in ongoing research projects in my lab and to develop excellent skills and knowledge in theoretical and computational neuroscience by being a part of the research team. I train students in the lab differently than in classes. To participate in research a student has to develop additional knowledge and skills through revision and understanding of relevant literature. Students start with support from lab members to clarify difficult topics. As a student becomes more immersed in the subject matter and they gain specific knowledge allowing them to contribute into a particular research project. At this point a student works with lab members on code, mathematical formulations and data analysis which comprise an ongoing project. Students will be encouraged to participate in several projects over few years to develop skills and knowledge in different fields of computational neuroscience.

In summary, my approach to teaching is to show the soul and meaning of scientific research and quantitative study, to help students mature their skills in scientific thought and to develop required knowledge and necessary toolbox for future careers in academia and industry.