What motivates you to focus on mathematical modeling, more so in such a field as biology?
Mathematical modeling is really interesting, especially as it stimulates one’s inclusive development. So as to successfully solve its tasks, one has to not just possess specific knowledge related to this field, but also know programming, and be well-versed in different subjects of mathematics, as well as have a keen understanding of the field he’s creating models for. For instance, I now work in epidemiology. We analyze statistical data that has to do with the dynamics of disease distribution, expand it and correct faults using programming and mathematical statistics. Then, we speculate on the laws of disease distribution, which implies having knowledge of this specific field, and create models that make use of differential measurements or the probability theory. Studying these models allows us to predict the dynamics of certain processes and solve optimization tasks.
In practice, this means that by successfully modeling flu outbreaks, we can predict the amount of infected for the next year. Also, we can use the models to analyze the mechanisms of the virus transmission, though that would imply adjusting them according to real and relevant data. For instance, the intensity of transmission can depend on such factors as the intensity of social interactions, weather conditions, the virus strain and other.
In what fields does mathematical modeling become more and more relevant?
Speaking of the popular trends in IT, that would be Data Science, a lot of companies in Russia and abroad seek talented specialists in this field. If what I hear from colleagues is true, several year back there were lots of job offers for Data Science specialists in St. Petersburg, and any programmer could easily find a position. Yet, the situation has changed, and now specialization in some field that has to do with Data Science is, if not a prerequisite, then a great competitive advantage when job hunting.
One can say that Data Science is a field adjacent to prognostic modeling, so a specialist in mathematical modeling can effectively analyze Big Data. The very classical mathematical modeling is, in a sense, a way of solving data processing tasks, and has a wide range of applications. For instance, our colleagues from the Institute of Translational Medicine use it to optimize the process of receiving patients with heart attacks: their goal is to minimize the time they spend in the hospital prior to the surgery. By checking where the patients lose most time, the specialists can advise to increase the amount of staff performing particular tasks.
Can we expect any new mathematical modeling methods to appear soon?
We use different approaches to mathematical modeling depending on the quality and quantity of data. For instance, if there’s few data, the classical methods are the most effective. Yet, as the amount grows, we can effectively use things like machine learning and neural networks. In some fields of science, sociology, for instance, the scope of knowledge is sometimes not enough to create even classical models, as it is not even clear which assumptions we can make. Yet, one can’t stop the progress, and eventually, there’ll be enough data and mathematical models will become more relevant in these fields. Some of the more promising applications are Smart City technologies, where one can make use of the data on the citizens’ behavior. The Urban Informatics laboratory I work for focuses on this subject.
You got your PhD in Omsk. Why did you come to work at ITMO? What are the benefits the university has to offer?
I started to take part in the eScience Research Institute’s science schools in 2009, when I was still a PhD student. Then, I learned about ITMO’s research in the field of mathematical epidemiology and got to know one of its authors, Sergei Ivanov, with whom I work now. I also took part in the Institute’s schools in 2010, 2012 and 2014, and was offered to develop my own project here.
For me, going to a different city, a different university was a great opportunity to learn how other research teams work, meet other scientists from my field and start new collaborations with epidemiology specialists. In Omsk, I mostly worked with mathematicians, so working with more practical applications was most rewarding. What’s more, here in St. Petersburg there are many medical research institutions I could make contact with. Yet, I still keep in touch with my Omsk colleagues and get consultations from them.
Staff members of the eScience Research Institute
What are your future plans? Which fields do you plan to develop in?
I really want to get a grant for solving some task that has to do with mathematical epidemiology, so as to start creating a corresponding research team at our laboratory. I’m also interested in establishing some collaboration with foreign researchers. Also, I plan to continue working with different medical institutions. For instance, the Research Institute of Influenza has a very interesting task they’ve asked to help solve: they want to predict the virus strain’s evolutionary development. Each year, the virus mutates, which hinders its treatment. One can model its evolution based on different biological data, its genetic development “tree”, for instance. Also, I want to continue studying the disease distribution among the population and learn how social aspects – like the use of public transportation – affect it.
Apart from research, you also take part in such events as ScienceSlam and the like. What motivates you to do that?
I believe that promoting science is very important. Otherwise, people may think that scientists are otherworldly fellows who do incomprehensible things that do not affect our lives, so why spend tax money on them? Explaining why we need different research is a must.
Vasiliy Leonenko at Science Slam
If you had the opportunity to participate in the I am a Professional competition when you were a student, would you? Why?
Any competition is a chance to assess your level of training as compared to other young specialists in your field. You can be a good student, get great marks, but eventually learn that you can’t apply your knowledge to practice. Competitions are a great way to learn more about your training, as they make you solve non-standard tasks, think creatively, apply your skills. For instance, I teach the Continuous Mathematical Models course, followed by many strong students who easily solve differential equations. Yet, when I ask to use them to create a mathematical model, many can’t understand how it’s done. Thus, I believe that everyone should use any opportunity to assess their strong and weak points.
When you participate in such competitions, you exchange experience with others. And this is a skill that is most essential for research or any other creative work. In science, it allows you to follow relevant scientific news and avoid doing research that has already been done by others, as well as learn about the novelties that might help you with your work.
So, does grabbing any opportunity make sense? As you might know, sometimes students apply for each and every program just because winning a grant is cool.
Surely not, one has to assess whether this or that opportunity can really help them reach their goals. For instance, one of my students won a grant to study in Liege. For him, that was a new experience, yet his specialty didn’t match the one he pursued here. If you want to broaden your horizons, getting a new specialty is great. Yet, if you are sure about your current choice, it is better to develop in this direction. Surely, planning your life years ahead may be hard; what I’m talking about is seeing the big picture of your future path that would stimulate you to make effort and follow certain decisions.
