Category: Novosibirsk State University

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Teacher Faculty of Economics Dmitry Markov, a professor at Novosibirsk State University, presented and conducted an educational program at Sirius Federal University, where he tested his original methodology for teaching the economics of engineering projects. The program was part of the Ural Project Engineering Campaign, organized by UrFU, where 20 teams of schoolchildren from the Sverdlovsk region developed their own technological solutions and prepared to present them to experts.

The methodology's core idea is simple, but quite unusual for an educational setting: before moving on to economic calculations, it's important to learn to think about it systemically. Participants in the program first understood the problem their engineering solution solved, who needed it, and what value it created. Only then did they move on to the economics—building the project's business logic and financial model for its implementation.

Particular attention was paid to the connection between the engineering nature of a product and its economics. Students learned to translate the physical characteristics of their solutions—resources, materials, energy, production time—into economic parameters: costs, investments, revenues, and cash flows. This approach helps them see the project not only as a technical idea but also as a future technological product capable of entering the market.

During the workshop, the project teams developed business models for their developments, then constructed economic models for the projects and presented them to experts. Many participants saw for the first time how engineering ideas are directly linked to economics and the market.

Following the program, representatives of the Ural Federal University confirmed the results of the methodology's pilot testing and signed a document approving its implementation. The document notes that the proposed approach helps develop students' holistic engineering and economic thinking and can be recommended for further use in engineering and interdisciplinary educational programs.

"We express our gratitude to Dmitry Markov for his active and professional participation in the Ural Project Shift," noted Nadezhda Terlyga, Deputy First Vice-Rector of UrFU. "His original method for immersing schoolchildren in the economic aspects of engineering projects proved highly popular among high school students. It allowed the participants to gain a deeper understanding of the connection between engineering solutions and economics and demonstrate impressive results, which our experts were able to see during the final project defenses."

According to Dmitry Markov, engineering education today is increasingly faced with a new challenge: connecting technological thinking with economics.

Engineering projects become true technological products only when they develop an understanding of the economics: who needs it, what value the solution creates, and how it can work in real life. This is precisely what we strive to teach our students.

I'd like to especially thank UrFU's leadership for their trust. And, of course, it's impossible not to admire the caliber of the school's projects. Among them are a smart medical bandage, IoT solutions, unmanned systems, and other developments. These are truly serious projects, and I was fortunate to be a part of their creation," shared Dmitry Markov.

The testing of this methodology marked an important step in the development of educational programs at the intersection of economics, engineering, and technological entrepreneurship—an area that is actively developing in Russian education today.

"We are currently working with the NSU Advanced Engineering School to develop new educational products for school audiences. These will focus not only on the engineering components of projects but also on marketing and assessing the economic impact of engineering solutions," said Dmitry Markov.

Please note: This information is raw content obtained directly from the source. It represents an accurate account of the source's assertions and does not necessarily reflect the position of MIL-OSI or its clients.

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Source: Novosibirsk State University –

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Sergey Ospichev, PhD in Physics and Mathematics, Deputy Director of the Mathematics Center in Akademgorodok, and Acting Head of the Department of Computer Science and ICT at the Specialized Scientific Center of Novosibirsk State University, discussed how artificial intelligence evolved from the fantasies of the past about thinking machines to today's large-scale language models. His lecture, "Artificial Intelligence: Origins and Evolution," was held as part of "Darwin Week"—a popular science marathon traditionally held at Novosibirsk State University in February. This year, the event was held for the first time on the new NSU campus.

From Golem to "Rent a Human"

Sergey Ospichev began his lecture with a quote from the film "Blade Runner," which, in his opinion, describes AI very well: "I don't think, I calculate, but the difference is already becoming unclear." He cited the definition of AI given by Chinese researcher YX Zhong back in 2006 in her article "A Cognitive Approach and AI Research": "Artificial intelligence is a branch of modern science and technology aimed, on the one hand, at exploring the secrets of the human mind and bestowing upon machines the advantages of human intelligence, and on the other, at enabling machines to perform functions as intelligently as they are capable of…"

Sergei Ospichev cited the earliest example of artificial intelligence, which existed, however, only as a fantasy of a "non-living" yet powerful assistant to humans. This was a clay giant, brought to life through Kabbalistic rituals. It was activated and deactivated by a magic word written on a scroll and placed in the idol's mouth. Upon receiving an order, it independently decided how to carry it out. It operated according to a predetermined program, a kind of machine operating from instructions. Back then, in the 17th century, humans gave orders to an artificial intelligence, albeit a primitive and fictitious one, but recently this has begun to change.

"A portal called 'Rent a Human' has appeared online, where neural networks can select a human to perform various tasks they couldn't do on their own: for example, photographing objects, delivering goods or receiving packages, or emotionally evaluating certain events or phenomena. While this platform is still experimental, a trend is emerging: AI is now beginning to manage people. Whether this is a good thing or not is still unknown, but this is the world we live in," said Sergey Ospichev.

First ancestors

Sergey Ospichev proposed examining the evolution of AI from the early 20th century. He discussed the ups and downs of this challenging path and analyzed the important milestones in this process.

The first to embark on this path was the German researcher David Hilbert (1862-1943), one of the most renowned mathematicians of the last century. The telegraph and railways became symbols of that time, and the prevailing mood was optimism and faith in science. Hilbert proposed the creation of a unified formal language of mathematics, based on simple arithmetic. This language was to presuppose the algorithmic decidability of all science. Why was this so necessary? With the advent of the telegraph, the world changed. Science instantly became international, and scientific knowledge became instantaneous. Scientists from different countries now had the opportunity to actively communicate with each other, exchange news, and organize international conferences, congresses, forums, and symposia. Therefore, mathematicians urgently needed a unified formal language understandable to all scientists.

An arithmometer is a desktop mechanical machine designed to accurately perform four arithmetic operations: addition, subtraction, multiplication, and division.

"At the beginning of the last century, many believed that science would solve all problems, and that a good adding machine would enable one to perform any calculation and achieve great achievements in mathematics, physics, and other sciences. David Hilbert was no exception, proposing to formalize mathematics. However, the Austrian logician, mathematician, and philosopher of mathematics, Kurt Gödel (1906-1978), entered the picture with his incompleteness theorem, according to which any algorithmically decidable theory that extends arithmetic is incomplete. He argued that it is impossible to formalize mathematics based on arithmetic and using algorithmic methods. An 'artificial' mathematician cannot replace living intelligence. For us scientists, on the one hand, this is very sad, because we will never see an automated mathematician, but on the other, it is wonderful, because we will always have work to do," explained Sergei Ospichev.

A Turing machine is an abstract computing machine, a mathematical model of computation, proposed by the eminent British mathematician Alan Turing (1912–1954) in 1936 to formalize the concept of an algorithm. It is considered the foundation of computability theory and is used to formally define which problems can be solved using algorithms.

A key discovery during this early period of AI was the Turing machine. This scientist shifted discussions of algorithms from philosophy to engineering. During World War II, the idea of Turing's abstract machine was combined with the idea of breaking the German Enigma encryption machine, which was then actively used to transmit secret messages. Ultimately, Alan Turing developed the Bombe, a code-breaking machine that earned him a place in history as the Enigma breaker and the founder of AI.

"The Turing machine became the ancestor of modern computers, but its creator also formulated the Entscheidungsproblem (decidability problem), proving that not all computations can be performed by computers—there are algorithms that cannot be written in any programming language. This poses a complex problem: on the one hand, an engineering approach is used, creating complex adding machines and computing machines, while on the other, scientists are well aware that not all problems can be solved with these tools. I like to call this 'computability schizophrenia,'" said Sergei Ospichev.

At the start

The term "artificial intelligence" emerged in 1956 at a Dartmouth seminar. This seminar is considered the beginning of AI development. A surprising situation arose here: not a single paper was published following the seminar, yet many of its participants became widely recognized as the "founding fathers" of AI. Important events in the background: the Cold War and the start of the space race. There was talk in the scientific community that computing power would not be sufficient to launch satellites into space.

Humanity has already invented computers and confidently uses them. The era of microchips has not yet arrived. "Smart machines" are still weak and gigantic in size—one of the fastest computers occupies 280 square meters and weighs 25 tons. It is only suitable for simple arithmetic calculations. A new method of calculation must be adopted, accelerated, and optimized. At a Dartmouth seminar, American mathematician John McCarthy (1927–) coined the term "artificial intelligence." He would later invent the Lisp programming language, become the founder of functional programming, and receive the Turing Award for his enormous contribution to artificial intelligence research.

Under the ban

Another crucial link in the evolution of AI was the invention of American psychologist and neurophysiologist Frank Rosenblatt (1928-1971) of Cornell University (USA). He designed and built the first numerical computer, the Mark I, which could recognize some handwritten letters of the English alphabet. Crucially, the computer learned all this on its own. The Mark I became the first neural network built in hardware. Naturally, the invention was a resounding success, spurring the need to study perceptrons and create increasingly complex neural networks.

The Rosenblatt perceptron (1957–1960) is one of the first artificial neural network models, simulating the brain's perception process. It consists of sensory (S), associative (A), and reactive (R) elements, operating as a linear binary classifier with a threshold activation function. It is based on learning with weight correction.

However, the euphoria was short-lived. A few years later, the book "Perceptrons" by MIT AI scientist Marvin Minsky (1927-2016) and mathematician Seymour Papert (1928-2016) was published. In it, the authors argued that "…increasing the size of a perceptron does not improve its ability to solve complex problems." Thus, Minsky was likely trying to attract attention (and funding) to his work, but the result was unexpected: interest in neural networks waned, funding for research ceased, the term "AI" itself was banned, and Minsky earned the nickname "Neural Network Killer." Thus, due to the rivalry between the two organizations, AI development stalled for decades.

Too complicated!

Sergey Ospichev surprised the audience when he said that the first multilayer neural networks appeared in the 1970s. Since neural networks were tacitly banned and even mentioning them was discouraged, let alone pursuing research in this area, the expert system relied on logical rules.

Logical programming languages are becoming increasingly popular. This isn't surprising: since, as Marvin Minsky wrote in his book, we can't train a system because it doesn't work, we have to write all the rules ourselves. The first very complex expert systems are emerging. One of them, MYCIN, is a medical expert system initially created at Stanford and designed to diagnose infectious diseases (meningitis, sepsis) and recommend antibiotics. It used a rule base (about 600) and backward inference, demonstrating accuracy on par with expert doctors and even higher. True, it was only 2.6% higher, but still. By comparison, it suggested acceptable therapy in 65% of cases, while doctors did so in 62.5% of cases. This system raised the first questions about AI ethics, but it never found application due to the complexity of data entry, as the patient had to answer approximately 200 questions before the system could make a treatment decision. At best, data entry took half an hour or more, said Sergei Ospichev.

Generation V

The 1980s were marked by a technological boom in Japan and the advent of microprocessors. Japan was dominating the computing market. The flow of data was growing, and computing power to process it was becoming insufficient.

The advent of microprocessors changed the world of computers—they became smaller and more powerful. They now weighed 5 kg instead of 28 tons. True, they were expensive, and not everyone could afford a personal computer at home, but it was a major step forward.

Seeking to maintain technological leadership, in 1982 the Japanese government initiated a massive 11-year program with funding of 50 billion yen ($500 million). Other countries later joined the race. A breakthrough in applied AI was expected, but the bets were placed on technologies that were already obsolete at the outset: supercomputers with hardware capable of distributed computing. The term "AI" remains taboo: instead, scientific papers use terms such as "data processing," "automated image analysis," "automated approach to formula processing," and so on. Imperative languages began to flourish, while logical ones began to lose ground.

Dark blue thaw

In the 1990s, personal computers became ubiquitous, and the World Wide Web grew exponentially. A new certainty arose: machines were smarter than humans! Confirmation of this appeared in 1997 and was widely publicized. A sensation: the IBM supercomputer Deep Blue defeated world champion Garry Kasparov for the first time in a six-game classical match, with a score of 3.5–2.5. This historic event marked the first victory of artificial intelligence over a reigning champion, marking a new era in chess and the development of AI technologies.

"Of course, this was very important for AI companies—it was a wonderful opportunity for them to emerge from the shadows and develop AI openly: publish articles about their research in journals, open departments at universities, implement their developments, and apply for funding. True, there were theories that this victory was the result of a coding error that caused the computer to make an unconventional move that determined the outcome of the game. But on the other hand, Deep Blue opened up AI to society, and people realized that AI was possible, that it was something big, important, and that it would change our lives. By today's standards, Deep Blue was a very weak computer, with very little artificial intelligence, and it didn't yet have thinking, but rather computation, but it was certainly one of the most important steps in modern AI," shared Sergey Ospichev.

Video cards – a second life

Multilayer neural networks were further developed by developments not originally intended for serious tasks—gaming video cards. They made it possible to overcome the insufficient computing power of the computers of the time for the necessary calculations.

The market was oversaturated with video cards—they were being produced in far greater numbers than gamers of the time needed, and they were much more expensive than they could afford. Furthermore, these video cards were much more powerful than the games of the time. Then, technology was developed that allowed them to be used for computing. Nvidia, the company that manufactured them, began donating these video cards to various universities for free, so that scientists could try them out in solving their own problems. In 2012, Ilya Sutskever, Geoffrey Hinton, and Alex Krizhevsky, the developers of the AlexNet convolutional neural network, also received them. By combining two video cards and obtaining 6 GB of video memory, they were able to win a major image processing competition. In creating their neural network, they outperformed classic machine learning algorithms developed 5-7 years earlier, demonstrating the superiority of the GPU—a specialized electronic chip for parallel data processing, graphics rendering, and acceleration of complex calculations. They succeeded in setting off a chain reaction that led to the popularity of deep learning today. Neural networks were rehabilitated," said Sergey Ospichev.

Three Horsemen of AI

Today, the development of neural networks is driven by three AI horsemen: arXiv, the largest free open archive (repository) of electronic preprints of scientific articles, transformers, and a chatbot based on the Generative Pretrained Transformer (GPT).

ArXiv is a preprint database containing 2.5 million articles, over 30,000 downloads per month, and 200 AI articles per day.

"Machine learning science is advancing very rapidly, and decisions to publish articles in scientific journals are made over a fairly long period of time—a year or two. Within two years, an article in machine learning will have disappeared from the world of machine learning—it will have lost its relevance and novelty. On this resource, you can immediately post your article so that colleagues can read it, discuss it, start using it, and share recommendations without waiting for official publication. Articles appear here instantly, making ArXiv one of the main hubs of machine learning today," explained Sergey Ospichev.

The second "horseman of AI" is Transformers—the next generation of neural networks, a kind of bridge between AlexNet and modern GPT systems. They enable deep learning for text processing. Next to them is the "third horseman," ChatGPT—a chatbot based on a generative pre-trained Transformer, which already receives billions of queries per year. GPT allows us to quickly and efficiently process texts, translate them from one language to another, search for data, generate sentences from them, and so on. It appeared in 2020, and its "successors" were subsequently developed, which are now our constant assistants.

What a twist!

And yet, no matter how tempting it may be to embrace AI, one cannot trust it completely. Whatever it does must be verified by natural intelligence. For example, after his lecture, Sergey Ospichev admitted that several opening quotes were generated by an AI neural network. The phrase in question was not found in the film "Blade Runner." And the photo of the Chinese researcher who outlined her vision of AI in a scientific paper cited in the lecture was also generated by the DeepSeek neural network.

Please note: This information is raw content obtained directly from the source. It represents an accurate account of the source's assertions and does not necessarily reflect the position of MIL-OSI or its clients.

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Source: Novosibirsk State University –

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From February 28 to March 1, the DANO National Data Analysis Olympiad hackathon took place at Novosibirsk State University. The HSE and T-Bank co-hosted the Olympiad. It was the first time it was held at NSU and was part of the final round of the 33rd International Schoolchildren's Economic Festival "Sibiriada. Step into a Dream."

Siberiada, held since 1994, is the oldest economics Olympiad in Russia and the only one of its kind beyond the Urals. In 2026, the festival was held in the Novosibirsk Region from February 27 to March 2, attracting over 550 students in grades 7-11. Its main goal is to create conditions for identifying gifted students, their intellectual development, and career guidance in economics education.

Since 2009, the festival's Economics Olympiad has been included in the list of all-Russian Olympiads for schoolchildren. Winners and runners-up receive preferential admission to universities, and NSU's Faculty of Economics (FE) can be enrolled without entrance examinations. NSU is a co-organizer of the festival and is responsible for the development, implementation, and review of the Economics Olympiad. The methodology committee and jury are primarily comprised of NSU Faculty of Economics faculty.

As noted by the organizer of the DANO hackathon, Associate Professor of the Department of Management Faculty of Economics NSU's Elena Limanova, who participated in the competition, said schoolchildren could try their hand at being data analysts.

"The Olympiad gives participants the opportunity to create their own research using big data. Why is data analysis so important today? Firstly, the world around us is changing rapidly and often unpredictably, so making important decisions based on past experience and knowledge can be quite risky. Secondly, we are surrounded by data today—where we are, how the weather is changing, how many times and when the classroom door was opened today, and so on ad infinitum. This data reflects what is happening now. And understanding what is happening and why is becoming crucial for making decisions that will work in a constantly changing world. When we talk about decision-making, we are primarily talking about management decisions that must be made in business and economic relations. In management, they talk about a revolution in data-driven management—management based on data, as opposed to, for example, the HiPPO (Highest Paid Person's Opinion) approach, where decisions are made by the most authoritative leader. This is changing both management and the professional world, bringing data analysts into the arena," she noted.

This year, about a hundred schoolchildren attended the hackathon, working with datasets and solving research problems.

Tatyana Bogomolova, Dean of the Faculty of Economics at NSU, addressed the participants with a welcoming speech:

"You're not in a random location for this event. Akademgorodok is where science truly lives."

The dean also recalled the university's scientific traditions and the contribution of Nobel laureate Leonid Kantorovich, who was at the forefront of the creation of the NSU Faculty of Economics.

For two days, the students worked with real data. Andrey Kostin, Head of the NSU Department of Economic Informatics, explained the assignment to the participants. The teams were tasked with analyzing a Novosibirsk housing market database and determining which factors most influence the price per square meter of an apartment.

Participants were provided with a database of over 13,000 apartments in Novosibirsk for 2021. The students analyzed parameters such as city district, property type, area, number of rooms, number of floors, building material, year of construction, presence of gas and heating, and other characteristics. However, the data required additional processing: some values were incomplete, and some parameters contained errors.

The hackathon concluded with the defense of team solutions. Participants presented their analysis results to the jury and answered questions. For many students, this was their first experience working with big data and solving analytical problems as a team.

Hackathon participant Alexey Shemetov said he became interested in data analysis about six months ago and decided to try his hand at the team competition.

"The most challenging part for the team was assigning roles. We didn't have a strong programmer, so we had difficulties with the mathematical model," a participant noted.

However, it was the teamwork and intensive format that became the most interesting for him.

"Because the final day of the hackathon was where we did the most work. And the teamwork itself was also very engaging," added Alexey Shemetov.

The hackathon became part of the larger educational space "Sibiriada," where schoolchildren not only compete but also learn about modern trends in economics and data analytics. Such events help participants better understand how analytical methods work in real-world business and economic problems.

Material prepared by: Ekaterina Mukovozchik, NSU press service

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Researchers from NSU and a team of scientists from Volgograd State Technical University (VolSTU), led by Doctor of Technical Sciences and Professor Viktor Kablov, have developed a water-swelling elastomer composition for the manufacture of sealing elements for packer equipment as part of the Competence Center's program "Technologies for Modeling and Developing New Functional Materials with Predetermined Properties" (CNFM) at Novosibirsk State University, which is being implemented with financial support from the NTI Foundation.

Packer equipment is a downhole device (seal) that, when installed, seals the annular space between the casing (or openhole wall) and the wellbore assembly, separating intervals—wellbore sections by depth—that are considered separate wellbore operating zones. This prevents interlayer crossflows, isolates individual inflow and injection zones, and ensures wellbore operation according to a specified pattern, withstanding pressure fluctuations and exposure to aggressive environments.

"Ordinary rubber doesn't swell in water, but we were faced with the challenge of creating packer rubbers that could be effectively used as a seal in oil and gas wells under high pressure. The presence of salt in the drilling fluid complicated the creation of such a material. Our development involves introducing swelling polymers into the material, which expand very well when exposed to liquid, but don't readily integrate with rubber. We needed to find modifying additives to overcome this incompatibility," explained Viktor Kablov.

The water-swelling elastomer composition is based on nitrile butadiene rubber and includes sulfur as a vulcanizing agent, Altax as a mercaptan vulcanization accelerator, and zinc oxide and stearin as vulcanization activators. Carbon black is used as a filler, along with sodium carboxymethyl cellulose as a water-swelling agent and a polymer modifying material that improves component compatibility.

"The key part of our development was selecting a durable base. The matrix was based on rubber, into which we introduced particles of water-swelling polymers capable of absorbing water or aqueous solutions. The particles expand in volume, increasing the volume and contact pressure of the sealing element, which is critical for sealing. To increase the speed and uniformity of penetration of the aqueous phase into the material, fibers are added to the composition, forming capillary channels for mass transfer," explained Viktor Kablov.

When selecting components and determining their proportions, the scientists used several neural networks. One of them, Deep Seek, generated an optimal prediction for the composition of the material with the specified properties and a number of useful recommendations for improving its properties. Next, they applied a program for modeling the behavior of composite materials, previously developed as part of the project "Computer-aided materials science of multicomponent nanostructured elastomeric materials with specified properties for extreme operating conditions."

"This program—a digital assistant for elastomer developers—is part of the Center of Excellence's program, 'Technologies for Modeling and Developing New Functional Materials with Predetermined Properties,' implemented at Novosibirsk State University and supported by the NTI Foundation. Together with the Center of Excellence, we have created a distributed research and technology center equipped not only with a wide range of testing equipment available at NSU, Volgograd State Technical University, and its branch, the Volga Polytechnic Institute, but also with technological equipment enabling the production of pilot batches of materials and products. To handle complex software, we have created a powerful computing cluster that enables the use of software products with artificial intelligence modules, including remote collaboration with our colleagues in other cities," explained Viktor Kablov.

The new polymer material has successfully passed laboratory testing in various operating environments simulating drilling fluids and on model seals. Our industrial partner, Intov-Elast LLC, one of the leading manufacturers of packer devices in Russia, has expressed interest in the development. Currently, rig testing of packer devices using the rubbers developed by the scientists is underway at the partner's and its customers' testing facilities.

Material prepared by: Elena Panfilo, NSU press service

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Dear women!

On behalf of the male staff of the university and on my own behalf, I congratulate you on International Women's Day!

Today, women are achieving success in every professional field, but their role in education is especially important and valuable.

Thanks to your significant participation, NSU maintains its high position among Russian universities, attracting applicants from all over the country.

You not only teach students, but also create the special atmosphere that distinguishes our university. You work in laboratories, invent new methods, technologies, and approaches, make scientific discoveries—your role in our lives cannot be overstated. You are a source of strength, kindness, love, and inspiration. Your aspirations, inner strength, and versatility never cease to inspire admiration.

I wish that every day of yours be filled with joy, success, and new achievements, and that this spring brings you new opportunities, bright ideas, and the successful implementation of all your plans.

Happy March 8th!

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On March 1, the application process began for the competitive selection process for the Presidential Management Training Program in the Novosibirsk Region for the 2026/2027 academic year. The program is funded by federal and regional budgets. Novosibirsk State University is one of three universities implementing this project. A press conference to launch the program selection process was held yesterday at the TASS press center. The event was attended by Deputy Governor of the Novosibirsk Region Valentina Dudnikova, Deputy Minister of Education of the region Svetlana Malina, Acting Rector of NSTU Anatoly Batayev, Rector of NSUEM Pavel Novgorodov, and Director of the NSU Center for Continuing Education Vera Markova.

The Presidential Management Training Program is one of the key state projects for developing a modern management corps for the Russian economy. It has been implemented in the Novosibirsk Region since 1998, and during this time, approximately 2,000 specialists of various levels and from various sectors of the national economy have been trained.

"The program is aimed at executives and promising managers ready to develop businesses, implement modern management solutions, and execute organizational and territorial development projects. Its development in the Novosibirsk Region is based on the integration of education, science, business, and government. Its implementation in the region has produced high-quality and targeted results: for example, reduced production costs, scaling up production, winning major contracts, and improving the quality of the urban environment. Graduates of the program create jobs, offer innovative solutions, and make social services more accessible. Taken together, these multifaceted efforts create the synergistic effect that drives the region's economy forward," emphasized Valentina Dudnikova.

The program consists of three stages: university studies, internships at Russian and international companies, and post-program work. Through training and networking with experienced managers, students exchange best practices, develop relevant competencies, and explore strategic solutions and modern management methods. Interactive classes are taught by renowned academic experts and business leaders, including program alumni.

To participate in the competitive selection, you must have a higher education, at least 5 years of total work experience, at least 2 years of management experience, and participation in the implementation of an organization development project.

NSU has been involved in this project since its launch in 1998. Vera Markova, Director of the NSU Center for Continuing Education and Professor in the Department of Management at the NSU Faculty of Economics, has been the head of the presidential management training program throughout this time.

"Our goal as an educational center is to foster modern, systemic management thinking. This new thinking is shaped by project-based learning. New projects that emerged during our program include the use of unmanned aerial vehicles and digitalization for farms, and the creation of a logistics platform for the transport of lean cargo—flowers, vegetables, and so on. We help build a community of managers and leaders who are committed to the development of their organizations, businesses, and regions. During the training, experiences and practices are shared, and new ideas and projects are born," explained Vera Markova.

When the program launched 28 years ago, only managers from business backgrounds were accepted. Now, the scope has expanded—you can manage a government organization, a medical facility, a university, or a college. For example, the Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences operates a genetic technology center, which five graduates of the NSU program helped create. Among the graduates are also members of the Legislative Assembly and representatives of many companies in AkademPark.

The application period for the program will be open until April 10, 2026. Detailed information on the application process is available at Center for Continuing Education of NSU.

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Alexander Pilipenko, PhD, Head of the Inter-Institute Laboratory of Molecular Paleogenetics and Paleogenomics at the Siberian Branch of the Russian Academy of Sciences, explained how human evolution unfolded over millions of years and discussed the common ancestors of humans and apes. The audience learned about the divergence of their evolutionary paths and the changes Homo sapiens underwent over millions of years of development during his lecture, "Through the Pages of Human Evolutionary History," which took place as part of Darwin Week, a popular science marathon traditionally held at NSU in February. This year, the event was held for the first time on the new NSU campus. Pilipenko helped the audience understand the main stages of human evolution, from our primitive ancestors to modern humans.

Our distant relatives

Among the large groups of mammals, the largest order we are most closely related to are rodents and lagomorphs from the group Glira. Together with them, we, as primates, are grouped together in the group Euarchontoglira.

"It's a good relationship, because rodents are currently one of the most evolutionarily successful groups of mammals. They are true champions in terms of species count, total biomass, diversity, and reproducibility, and they have populated most of the planet. But we also have closer relatives—tree shrews and colugos. Unlike rodents, they can't boast of any particular achievements in territorial expansion or species diversity, but they have nonetheless survived to the present day, albeit with a very small number of species that have mastered a narrow ecological niche. The order of primates, to which we belong, comprises 380 species, which also inhabit a fairly narrow range. All, that is, with the exception of one—humans, who millions of years ago decided to stand out from the crowd and achieve every possible and impossible evolutionary success," explained Alexander Pilipenko.

Our most distant relatives are lemurs and lorises, which belong to the group of strepsirrhine monkeys. All others, including modern humans, belong to the group of swan-rhines. This is a fairly diverse group, and the first of the swan-rhines to branch off from our evolutionary path were the tarsiers, approximately 60 million years ago! From there, "nose-based evolution" continued. Forty million years ago, the common-rhynchus monkeys diverged from the monkeys of today's Old World. Another 15 million years later, our first distant ancestors diverged from this group. Gibbons emerged from this group 18 million years ago. Our common ancestor with the great apes existed on the planet 14 million years ago. The evolutionary paths of the ancestors of humans and gorillas diverged 8-10 million years ago, and those of humans and chimpanzees diverged 6-8 million years ago. They then evolved independently and in slightly different directions. Some evolved into modern chimpanzees and bonobos, while others evolved into hominids, including the genus Homo. Each group followed its own long evolutionary path. But while noses were the original ancestors, the tail has now fallen victim to progress.

Where did the tail go?

How is it that the most evolutionarily successful group of great apes, including us, can't boast a beautiful and functional tail? And at what point in our evolution did we lose it? As Alexander Pilipenko explained, approximately 20-25 million years ago, a single mutation occurred in one of our genes, causing the protein encoding the gene responsible for tail formation to suddenly begin to lose a small portion. This regulatory gene dramatically destabilizes the development of the part of the spine responsible for tail development. And as soon as a certain variant of this gene arises, tail development in apes and even other mammals studied is dramatically destabilized. In some individuals, for some reason, the tail continues to develop, in others it becomes underdeveloped, and in others it disappears altogether. In other words, this "broken" gene didn't immediately make our ancestors tailless. But eventually, subsequent mutations and evolutionary natural selection completed the process, and this trait became permanently fixed—we lost our tail, and none of our closest relatives regained it for 20 million years. Somehow, the absence of a tail proved so evolutionarily advantageous that it became permanently fixed at the genetic level.

"Missing link

Several million years passed between the time chimpanzees diverged from their common ancestor with ancient hominids and the emergence of modern humans. It was during this period that scientists of the past searched for the so-called "missing link" between humans and their ape-like ancestors. Modern researchers no longer seek proof of human evolution; they seek evidence that allows them to understand the evolutionary history of humans in greater detail. Alexander Pilipenko explained why.

— Previously, paleontologists, having found another creature that was somewhat similar to our ancestor, at first tried to integrate it into a direct line between some very primitive predecessor of man and modern people due to the presence of certain progressive traits. As such findings accumulated, it became even more clear that human evolution had indeed occurred. Currently, a large number of forms with intermediate meanings and with a mosaic combination of progressive and, on the contrary, primitive features among paleontological finds many times overlaps the necessary minimum that was necessary at the initial stage to prove this fact. Now scientists have to decide how to correctly group the discovered creatures and find a place for each of them on the evolutionary tree connecting primitive ancestors and modern people. Assessing their place in human evolution, scientists primarily pay attention to three classes of morphological features: changes in the body associated with possible adaptation to upright posture (spine, pelvic and femoral bones, structure of the foot), the skull and its brain part (structure, size, volume), as well as structural features of the hand, which should indicate that a person is becoming more and more capable of performing fine manipulations with his hands. It has been established that the ancestral home of humanity is Africa, and most of the creatures found that belong to this stage of evolution were found on this continent, the scientist said. 

Our "pre-human" ancestors

Alexander Pilipenko listed some of the main ancient human ancestors whose remains were discovered by paleontologists.

Sahelanthropus is chronologically close to the last common ancestor of humans and apes. This hominid, who lived approximately 7 million years ago, possessed a number of advanced traits that were already associated with the beginnings of adaptation to an active upright posture during locomotion. This hominid was not yet fully bipedal. Nothing is known about the structure of its arm and hand. Despite this hominid having already begun to adapt to bipedalism, its brain size remained the same as that of chimpanzees and their close ancestors. No obvious differences were found in brain structure either. Alexander Pilipenko explained that this mosaic of advanced and archaic traits was characteristic of virtually all creatures that lived over the next 2-3 million years.

One of the earliest, relatively well-studied groups of our ancestors is the Ardipithecus. They existed over 4 million years ago. They remained as small as Sahelanthropus (approximately 120 cm tall). But they already showed clear signs of further adaptation to bipedalism, with changes affecting their hands, enabling them to perform more complex and subtle movements. This is evidenced by a unique find—skeletal fragments of a female, which paleontologists have named Ardi. It is considered one of the most complete skeletons of early hominids: most of the skull, teeth, pelvic bones, and limb bones are preserved. This allows scientists to conclude that the brain size of this human ancestor remains the same as it was 2 million years ago. Despite the changes toward bipedalism, the lower limbs still retain a completely ape-like structure, suitable only for tree climbing but not well suited for upright walking. However, a rigid arch is already beginning to develop in the foot, which, however, is still far from what formed in our closest ancestors.

A more advanced group of these early creatures are the australopithecines. Numerous species of australopithecines lived between 4 and 1 million years ago. It is believed that early humans evolved from them. Among them, there is also a "star"—a female named Lucy by scientists. Her skeleton is 40% preserved. Alexander Pilipenko noted that such finds are very rare and are of such high scientific value that scientists study them in great detail and comprehensively. Lucy was much better adapted to upright walking than Ardi. Her brain size, compared to Ardi, was significantly larger, primarily due to the parietal lobe. This is presumably related to upright walking and fine hand movements, for which Lucy was much better anatomically adapted. The hyoid bone, responsible for the development of the potentially complex vocal signaling system we call speech, was still in a state close to that of apes. In other words, australopithecines had not yet developed even primitive speech. However, they were already confidently walking on two legs—this was revealed by the astonishing discovery of the "Laetoli Tracks" in Tanzania, East Africa. This was a set of footprints of two individuals—an adult and a juvenile—left in volcanic ash 3.5 million years ago.

Another famous Australopithecus, nicknamed Harry, differs significantly from Lucy, who belonged to the early Australopithecus, while Harry belonged to the later Australopithecus, living contemporaneously with primitive representatives of the genus Homo. Surprisingly, primitive stone tools were discovered near Harry's remains, but it is still unknown whether they were related to him or were accidentally introduced. If this mystery is solved, it will become clear whether Harry was the first "non-human" capable of making stone tools. For now, most scientists are confident that this is not the case.

But Australopithecus weren't the only ones who shared the planet with early humans. The Paranthropus, apes of higher primates, also lived out their final days. For several hundred thousand years, they shared the same habitats with early Homo. They looked completely different from other "pre-human" human ancestors. Due to their specialization on coarse plant foods, their jaws and teeth underwent modifications.

Early humans

Alexander Pilipenko also spoke about early representatives of the genus Homo, who encountered their "pre-human" ancestors.

Homo habilis (2.4-1.4 million years ago) possessed an important skill unavailable to earlier hominids. They were capable of producing stone tools reliably using a specific technology. Importantly, they did this with the help of other tools. This is precisely what constitutes full reproduction. A chimpanzee can use a stick to knock down a fruit hanging high on a tree branch, but they would not be able to use a sharpened stone to shape the stick.

External changes were also significant. Compared to their pre-human ancestors, Homo habilis' brain volume increased from 350-400 to 600-700 cubic centimeters, and in some individuals, up to 800, yet their height remained the same—120 cm. The brain regions responsible for speech generation began to rapidly develop, but the structure of the larynx remained primitive. These creatures did not yet possess a fully developed, complex speech system.

The central creature in human evolution is Homo erectus (1.8 million to 143,000 years ago). This creature is characterized by a rapid increase in brain volume—from 850 to 1,200 cubic centimeters. This represents a completely different stage of development, as 1,200 cubic centimeters represents the lower limit of normal brain volume for living humans. Their height and body weight, however, remain the same as those of Homo habilis. Thus, a rapid increase in the ratio of brain volume and mass to body weight and size is noticeable. But the most significant achievement of Homo erectus is that they were the first members of the genus Homo to reliably expand beyond Africa and subsequently disperse across the planet.

Alexander Pilipenko spoke in detail about the development of Homo sapiens, who emerged approximately 300,000 years ago in Africa, evolving from Homo heidelbergensis. He then migrated out of Africa, gradually interbreeding with other human species, which, since the first and second waves of Homo erectus migration, have undergone their own evolutionary journeys. The scientist also explained how the populations of the continents, in all their diversity, formed. Particular attention was paid to the unique discoveries made in Denisova Cave (Altai Krai), which have changed our understanding of ancient human history. Here, in 1994, the remains of an extinct and previously unknown human species were discovered. This species not only coexisted with Neanderthals, but also had offspring, and the genes of these ancient creatures are still present in modern humans.

Please note: This information is raw content obtained directly from the source. It represents an accurate account of the source's assertions and does not necessarily reflect the position of MIL-OSI or its clients.

Translation. Region: Russian Federation –

Source: Novosibirsk State University –

An important disclaimer is at the bottom of this article.

The Potanin Foundation has announced the results of its 2026 scholarship competition. 1,000 students from 71 universities have been named winners. Compared to last year, the number of finalists and winners among NSU students has increased: while in the 2024/2025 academic year there were 53 finalists and 27 winners, in the 2025/2026 academic year there were 63 and 34, respectively. Novosibirsk State University is now among the top 10 Russian universities in terms of the number of winners.

Among the winners, there are most students with Faculty of Natural Sciences (8 people) and Faculty of Physics (7 people). Also among the scholarship recipients are representatives Humanitarian Institute (6), Mechanics and Mathematics (5), Economic (3), Geological and geophysical faculties (2), Institute of Medicine and Medical Technologies (2), and also Institute of Philosophy and Law (1).

Starting in February of this year and until their graduation, the winning students will receive a Vladimir Potanin scholarship of 30,000 rubles per month. For each student, this amount represents a valuable investment. For example, Elina Surkova, a second-year master's student at the Faculty of Natural Sciences, plans to upgrade her laptop and use the remaining funds for her future graduate studies.

"I thought this scholarship would be a good financial support. I didn't apply in my first year of master's program because I didn't think I had enough articles and achievements. But then, seeing that my classmates had won, I decided to give it a try. I advise you not to put off preparing until the last minute and be sure to carefully study the requirements. Try to understand what kind of winner they're looking for and identify these characteristics in yourself. It seems simple, but in reality, you need to seriously reflect to give a comprehensive answer. Be smart—prepare in advance and don't be afraid to always try!" Elina shared.

The Vladimir Potanin Scholarship Competition consists of two stages: a qualifying round and a final round. The first stage is written, requiring applicants to write several essays about their practical experience and life strategy, their research and project work, and their volunteer activities. The final round has been held online for the past several years. It includes business games, interviews, case studies, and other tasks.

"For the interview, all the students were grouped into groups of 10, and it lasted about 4-5 hours. Since I have a bachelor's degree in journalism, I have good soft skills, so this stage wasn't a problem for me. The foundation representatives are looking specifically at your personal qualities; they want to see your leadership qualities, your entrepreneurial spirit, and your willingness to take on responsibility. About 2,300 students made it to the second stage, and everyone wanted to win. But everyone has a chance. The main thing is to believe in yourself and not worry," said Alina Iskhakova, a first-year Master's student in Journalism at the Humanities Institute.

Darya Zheltikova, a first-year master's student at the Faculty of Natural Sciences, previously applied for various scholarships, including the enhanced state scholarship for scientific research, the Novosibirsk Region Governor's scholarship, and others. She notes that the process for collecting documents and achievements for the Potanin scholarship competition's selection round is similar to those for which she has previously applied. It differs from other scholarships by requiring her to solve cases with other finalists during the final round.

"My general advice for all students is to participate more in scholarship competitions like these, even if at first glance it seems like your experience and achievements aren't enough to pass the qualifying rounds or win. If you have the opportunity to participate, why not? You certainly have nothing to lose, and at best, you'll gain. When there are written rounds like these with a large number of essays, in addition to the usual writing guidelines, I would advise you to be sure to evaluate the integrity of your writing after completing the application, as the essay topics are intertwined and create a complete picture of you. During the rounds involving case studies and other assignments, I would advise you not to dwell on possible failure while completing them (if such thoughts arise, of course). It's better to focus on the tasks ahead. The assessment at such rounds can be quite subjective, and you can't be certain of a negative outcome in advance. And, of course, I encourage you to never give up and always believe in yourself and your abilities," advised Daria.

The full list of this year's scholarship recipients:

Faculty of Natural Sciences

1. Allayarova Elina Ravilievna

2. Zheltikova Daria Yaroslavovna

3. Zueva Alexandra Sergeevna

4. Lukin Alexander Dmitrievich

5. Makarova Aelita-Louise Alekseevna

6. Safonova Alena Alekseevna

7. Surkova Elina Sergeevna

8. Tarhova Anna Romanovna

Faculty of Physics

1. Kuznetsova Lada Sergeevna

2. Novikova Sofya Vladimirovna

3. Pudova Sofia Sergeevna

4. Rudnev Daniil Nikolaevich

5. Smirnov Nikita Igorevich

6. Turlo Vadim Sergeevich

7. Yartseva Maria Andreevna

Humanitarian Institute

1. Iskhakova Alina Maksimovna

2. Melnikova Ksenia Alexandrovna

3. Morozova Yesenia Shamilevna

4. Shpakova Ksenia Yuryevna

5. Yudin Ivan Alexandrovich

6. Yumina Anna Vladislavovna

Faculty of Mechanics and Mathematics

1. Agarkov Georgy Igorevich

2. Emelyanov Maxim Vyacheslavovich

3. Manaev Alexey Andreevich

4. Timofeev Gleb Vadimovich

5. Chutkov Denis Sergeevich

Faculty of Economics

1. Gorbunova Sofia Konstantinovna

2. Silantyeva Arina Rodionovna

3. Chuyko Eduard Alexandrovich

Faculty of Geology and Geophysics

1. Smyshlyaeva Alina Konstantinovna

2. Chernoskutova Elizaveta Alekseevna

Institute of Medicine and Medical Technologies

1. Burova Tatyana Sergeevna

2. Sycheva Alina Artemovna

Institute of Philosophy and Law

1. Kostornov Denis Alekseevich

We congratulate the winners and wish them success in their studies and research activities!

Material prepared by: Varvara Frolkina, NSU press service

Please note: This information is raw content obtained directly from the source. It represents an accurate account of the source's assertions and does not necessarily reflect the position of MIL-OSI or its clients.

Translation. Region: Russian Federation –

Source: Novosibirsk State University –

An important disclaimer is at the bottom of this article.

On March 1, 2026, the Open Mathematical Championship of Siberia was held in Novosibirsk. Novosibirsk State University served as the venue. The championship brought together over 250 participants aged 3 to 16. This year, children and parents from various Russian cities—Moscow, Krasnoobsk, Chekhov, Agninsky, Krasnoyarsk, Irkutsk, Blagoveshchensk, and Novosibirsk—came to NSU.

The competition was held according to the standards of international independent championships. All participants received NSU certificates for their portfolios.

Participants competed in four educational areas:

Mental arithmetic (5–17 years)

The mental arithmetic competition consisted of five disciplines: abacus, mental arithmetic, oral arithmetic in Russian, oral arithmetic in English, and a flashcard competition. Participants demonstrated their skills with a specialized abacus, the speed of written calculations, the ability to perceive numerical information by ear in two languages, and the ability to mentally calculate numbers appearing on a screen.

Creative Math by I❤️Maths (Ages 3–8)

In the Creative Mathematics category, the youngest participants—children aged 3 to 7—solved problems involving counting, logic, and spatial reasoning in a game format, allowing them to unlock their potential without feeling intimidated by the subject.

Nonverbal intelligence (Oxford

As part of the Oxford program

Singapore Math (7–13 years)

The Singapore Mathematics program has traditionally generated high interest. Compared to last year, the number of participants has increased. The methodology focuses on developing critical thinking and a deep understanding of mathematical concepts, and is approximately two years ahead of the school curriculum. The most successful students annually receive the opportunity to represent Russia at international competitions in Singapore.

The Siberian Open Mathematical Championship was held at NSU for the umpteenth time, and each year we see not only new and interested participants but also many familiar faces. Many of our graduates are among the parents! They once sat in these classrooms as students, and now they bring their children here. Our graduates are our pride, and we are incredibly happy to see them again at NSU.

Our current students played an active role in the championship. They enthusiastically helped the young guests navigate the university, infused everyone with their energy, and demonstrated by example what it means to be part of the NSU family. It's incredibly rewarding for us to see how students engage in university life and help create that warm atmosphere.

And, of course, looking at the young participants, aged 3 to 16, solving mental arithmetic or Singapore math problems, I knew: this is our next generation! It's a great joy for the university to open its doors free of charge for such events. This is our investment in the future: so that even now, while solving their first serious problems in the classroom, children feel part of the larger academic world.

We sincerely hope that in a few years, both today's participants and the children of our graduates will tell their parents, "I want to study here too!" We look forward to seeing you at NSU again, this time as applicants. Thank you to the organizers and our wonderful student volunteers for this bridge between generations and for the high level of the championship! Holding such competitions within the university is essential so that children can experience the NSU atmosphere from an early age, experience the university environment, and consider our university as a place to pursue higher education," noted the Deputy Dean. Faculty of Economics of NSU Naimjon Ibragimov.

The significance of the championship

"We've been organizing championships for over 10 years and see how they become children's first step into the world of greater knowledge. These events help participants open up, believe in themselves, learn to cope with anxiety, and rejoice in a well-deserved victory when they receive the trophy and medal on the big stage," said Yulia, the championship's lead organizer.

The awards ceremony took place in Akademgorodok's Technopark and served as a spectacular finale to the championship. On the main stage, the young mathematicians received their well-deserved medals and trophies, along with the applause of their parents, teachers, and guests.

The organizer of the Open Mathematical Championship of Siberia was the center for additional education – the Ein school

You can find out more about the school at social networks

Please note: This information is raw content obtained directly from the source. It represents an accurate account of the source's assertions and does not necessarily reflect the position of MIL-OSI or its clients.

Translation. Region: Russian Federation –

Source: Novosibirsk State University –

An important disclaimer is at the bottom of this article.

The Novosibirsk Region Higher Education Institutions Track and Field Cup has concluded. Students from 14 universities competed in individual events across various distances.

NSU athletes won 10 medals:

1st place: Alexey Chviruk (MMF) in the 1500 m run

2nd place: Anastasia Osmushkina (IMMT) in the 800 m race, Darya Zavalishina (MMF) in the 1500 m race, Arseniy Podosinnikov (FF) in the 3000 m race

3rd place: Maxim Fetisov (FF) in the 3000 m run Anna Eliseeva (EF) in the 1500 m run Daria Zavalishina (MMF), Anna Eliseeva (EF), Alla Kuznetsova and Anastasia Osmushkina (IMMT) – in the 4 x 400 m relay

Also playing for the NSU team were: Kira Antonova, Daria Bogoley, Maria Stepanenkova and Nikita Bosak (MMF), Adriaens Rudans and Artem Perelygin (FF), Danil Poryadin and Vitalina Kiseleva (FEN), Miron Gaskov and Nikita Tropin (FIT), Nikita Alekseev (GGF)

We congratulate our athletes and their coach, Anton Mamekov, on their excellent results and wish them continued success in sports and studies!

Please note: This information is raw content obtained directly from the source. It represents an accurate account of the source's assertions and does not necessarily reflect the position of MIL-OSI or its clients.