Translation. Region: Russian Federal
Source: State University “Higher School of Economics” –
An important disclaimer is at the bottom of this article.
Anastasia Malashina defended her dissertation on a topic related to cryptographic methods of information protection, and is now engaged in applied projects in the field of strategic analytics. In an interview with the Young Scientists of the Higher School of Economics project, she spoke about the difficulties she encountered on the way to her degree, what cryptography is, and why large language models will not replace human intelligence.
How I got started in science
In high school, I became interested in mathematics and was going to enroll in the corresponding theoretical direction, but my set of exams limited my choice to specialties related to applied mathematics. At the Higher School of Economics, I passed the applied mathematics and computer security program, but ultimately chose the second direction, although I did not initially think about information security. After completing my specialist program, I decided to enroll in graduate school right away. A higher education diploma was not enough for me; I wanted to get an academic degree.
What I researched
My dissertation was related to cryptographic methods of information protection. I was offered a narrow direction related to keyless reading. I started working on this topic in my final years of the specialist program, then continued in graduate school and defended my dissertation on it.
I liked the topic because it allows for an interdisciplinary approach: mathematical methods of cryptanalysis are combined with the study of natural language in text form.
Methods of mathematical linguistics are not included in information security programs. And a terminological barrier is formed: linguists and cryptographers use completely different terminology to describe the same language models. In my work, I tried to reduce this methodological gap.
As part of my dissertation, I worked on applying the information-theoretical approach to the analysis of algorithmic methods of information protection. Imagine that you are decrypting an intercepted message or its individual parts, going through all possible variants. How can you single out from the chaotic combinations of symbols those that may be variants of the original text? To do this, you need to take into account the statistical features inherent in the text in natural language, which you can try to approximate and formalize, for example, in terms of probability theory and mathematical statistics.
What is cryptography
This is the science of mathematical methods of protecting information. For example, correspondence in WhatsApp is encrypted using cryptographic algorithms. The basis of the electronic digital signature, which is formed, for example, on "Gosuslugi", is also cryptographic schemes.
In the USSR, cryptography was a completely closed discipline, the word was not even mentioned in the open press. Later, cryptography was partially opened, but many studies remain closed. As a result, some areas of research in open science may appear out of context.
The problem of narrow topics
My work was carried out in conditions of an artificial methodological vacuum. Without the possibility of comparing my research with previous results.
The problem became more acute when trying to publish articles. I encountered a huge number of rejections. The list of journals is limited to lists, and they practically do not have narrow-profile publications in the field of cryptography, etc. Generalists did not understand the practical significance and relevance, and therefore could not objectively review. Paradoxically, preparing the research was much easier for me than publishing the necessary scientific articles on the topic of the dissertation.
What qualities are important for a researcher?
I once heard an opinion that one of the most important qualities for a scientific researcher is the ability to quickly take criticism into account and bring the work to a level where it meets the requirements. At the department seminar at the end of April last year, many comments were made about my work. The committee believed that I would not have time to correct everything before autumn. However, I revised the manuscript in a month, and even added a number of new experiments, the idea for which came to me during the work. And, contrary to expectations, I went to the pre-defense already in June.
I am also still surprised how I managed to publish my articles in the required journals and meet the defense criteria for articles. All my main articles on the dissertation were published without co-authors.
If I hadn't become a researcher
I realized myself in the academic track the way I wanted. Now my professional activity is not directly related to scientific research. I see many prospects for myself in other areas, new interesting projects.
What I do
Strategic consulting and technological analytics. I like the project format without being tied to daily routine tasks. When you conduct analytical research, you have to be creative and come up with new formats. In some ways, it really reminds me of doing science, when you don’t have a ready-made methodology within the framework of the task and you work in conditions of uncertainty of the result.
In science, you develop a methodology for research, prove statements, conduct experiments, but sometimes you come to unexpected conclusions. And you think about what to do with it, because a negative result in such studies is also important. And this creative principle that is present in science is what initially attracted me.
What is the difference between analytical research and scientific research?
There are a number of requirements for scientific research, it is aimed at obtaining new fundamental knowledge, testing hypotheses, discovering patterns. A dissertation must necessarily contain a certain contribution to the development of some area of knowledge. Science seeks truth. The results are recorded in the form of scientific articles, and subsequently in the form of dissertations, monographs, etc.
Analytics is applied research that answers specific practical questions. Here, data is transformed into solutions. For example, if we are talking about strategic consulting, we answer questions about what is happening, why, and how to act. The results of business analytics can take various forms depending on the project duration and customer requirements: a report, digest, white paper, etc.
But there is another very interesting format – popular science texts. This is express analysis, designed for a wide audience. Without delving into the topic of a specific technology, everyone can understand what trends are currently emerging in science and business and how they will affect our everyday life.
Why does an analyst need a broad outlook?
If you write about the latest trends in technology, it is important to be aware of scientific achievements in various fields. It is clear that a person without a specialized education in the subject area will not understand the fundamental things that are happening there now. But you need to understand in general terms in order to quickly navigate.
The big topic now is large language models (LLM). New scenarios for their use appear daily, they increase the efficiency of business and science. However, LLMs have almost reached their limit. They are already trained on a huge array of texts written by people, and increasing the data will lead to only minor improvements.
A cat that jumps from the floor to the shelf does not know Newton's theory, but it makes its jump absolutely accurately. It relies on its empirical experience. Both humans and animals have the ability to proprioception. Language models do not. They do not understand our world. And texts will not fix the situation here.
Do I get burnout?
There is no burnout as such. But when I took up the dissertation after finishing my postgraduate studies, in order to bring the manuscript to a holistic form and start moving towards pre-defense, I experienced psychological resistance for a long time. Because when you constantly have to face subjective criticism and cope with problems alone, apathy appears. But the energy of unfinished business (the well-known Zeigarnik effect) weighs more heavily. This became the motivation to finally finish the dissertation.
What are my hobbies besides science?
Recently I have become interested in interior design and started playing tennis.
What was the last thing I read?
Les Miserables by Victor Hugo and The Ladies' Paradise by Emile Zola.
Advice to young scientists
Think in advance about the prospects of the research and how the topic fits into the current agenda. I know that young researchers in other disciplines often face the problem that the topic they choose has already been sufficiently well researched. But in my case, the advice would be this: do not take narrow topics about which little is known.
There is no point in starting a study if its practical significance is not obvious. The issue is not only about successfully defending the dissertation. The study can be commercialized, attract funding, and promoted in popular science formats.
Accordingly, you need to understand how well-known your future topic is in the expert community. It is desirable that not only your supervisor is interested in it, but also at least a few other people at the university. It is very important that a postgraduate student, in the process of preparing his work, can seek advice from various specialists and receive an objective assessment, because one person's view becomes blurred.
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