What did you like most about the program?

Most importantly, I was able to fill major gaps in areas where I had previously lacked knowledge, which would have been difficult to fill elsewhere. I acquired the necessary foundation to understand what to build on and where to delve deeper. I’ve even been able to implement some things directly in my work to see how they function in practice.

It’s great that all lectures remain accessible, and I can return to them even after graduation. This is a completely different level of knowledge transfer compared to when we were studying face-to-face; if you’re not in class and you don’t write everything down, then it’s lost to you forever. I still review the MSAI lectures when I need to clarify something or study more deeply to solve work tasks.

Another pleasant aspect was our study group. We still communicate and support each other as people with common interests and aspirations, as like-minded individuals. We had a real spirit of studentship and mutual support, despite the fact that the program was online and we mostly saw each other through a screen.

And of course, I am very grateful to the teachers—they always supported us throughout the learning process and helped us get started and carry out our own projects. I really liked my final project, which was focused on malfunctions based on audio data in autonomous vehicles. It was very interesting to delve into the topic, gain this valuable experience, and ultimately achieve a really good result in the form of a diploma.

Now I’m looking forward to more large-scale and interesting personal projects in the field—I'm striving towards that.

What aspects of the program or the study experience would you like to improve?

I would like more work on errors after major homework assignments, especially laboratory works. They were creative; there was no right or wrong answer—you do as you think, as you come up with. And because of this, I would have liked a more detailed analysis of the outcomes at the end, although I understand that it’s impossible to devote so much time to each student. But it could have been very useful for us.

What advice would you give yourself if you were just starting the program?

Stock up on patience and believe in yourself! And, of course—secure the support of loved ones because your master’s degree affects not just you but your entire family, really. Everyone has their own family responsibilities, and you need to be ready to either delegate them if you have to sit over your studies all weekend, for example, or be confident that they’ll be taken care of and that you’ll be supported.

Why did you decide to pursue a master’s degree and learn a new direction?

I wanted a technical education. I love mathematics, and my first degree was in economics, but it didn’t allow me to delve into the exact sciences. At some point, sitting at work, I realized that I liked optimizing and automating things and that many manufacturing and logistics processes could be made faster and more efficient with the help of artificial intelligence. That’s when I went looking for a suitable program and found MSAI.

How did MSAI help you choose the direction you would like to pursue in the near future?

There’s a little story connected with this as well. Last autumn, when I was thinking about my final project for the master’s program, I decided to attend a Yandex conference where a very talented and smart guy was speaking to the audience. He was the winner of several intellectual and technological competitions and clearly had a lot to tell the audience. But he had a problem — he stuttered, and almost no one listened to him because his speech was hard to perceive, and he felt even more uncomfortable because of it. Then I thought: "It would be great if people like him could freely share their ideas with a large audience and perform on stage despite their physical limitations." Some people cannot overcome stuttering for their entire lives, no matter how hard they try. And this defect can significantly hinder them both psychologically and socially.

Then I decided to approach my academic advisor and propose a project on voice cloning. The idea is that to correct someone’s speech, you just need to take their voice and create a technology that could mimic it by hiding the stuttering moments so that in the end, you get a clean, understandable, and logically structured statement. Such technologies are already used in some voice assistants to make their responses clearer and more logical, but there is still much to learn and develop in this area to ensure flawless cloning results.

Now I am thinking about how to properly apply all the knowledge I received during the program and while preparing my final project. They have sufficient commercial and inclusive potential, so I will be able to choose the area that seems most interesting to me.

What advice would you give yourself if you had just entered MSAI?

The main advice is to make the most of these two years. This is the time you need to dedicate to studying because it gives you the opportunity to grow as a specialist in a new field and acquire knowledge you probably wouldn’t get anywhere else.

How did the program help you reach new heights professionally?

I started working at Yandex while I was still studying. Initially, I joined a team that was developing "Drive"—a car-sharing service with many opportunities for implementing machine learning. Later, I switched to autonomous vehicles and now work on computer vision.

In this sense, I never questioned the relevance of the subjects taught at MSAI—I always saw their practical significance in what I am doing or planning to do. Even now, all these things that I acquired there prove useful for me daily, despite our program being very intense and the subjects diverse.

The most telling moment, I think, is that I work in my profession using what I know well. I was never told, "Forget everything you learned at university"—instead, I understand that I know everything necessary and feel confident professionally.

What would you like to improve looking back at your student experience after graduation?

I disliked that it was very tough to combine work and study. Who would like that? I would have preferred it to be the same but easy! But no, I was almost deprived of any free time because on one hand, I was mastering the basics, which I sometimes lacked, and on the other, I was digging deeper, wanting to reach things that would give me the maximum understanding of the field.

What advice would you give yourself if you were just starting the program?

None! Overall, I am very pleased with myself because I really spent a lot of time and effort mastering the program. The only thing I might say to myself is: "You will often feel uncomfortable because you don’t know something, and that’s okay. It will be worth it."

Why did you make a decision to enter MSAI and develop new skills there?

Actually, since the pandemic I had a lot of free time because traveling was a completely closed option for me and I didn’t have to work in the Infosys office in Chennai. And I thought: "Okay, why not use this spare time in a fruitful manner, why not study something?". That’s why I started looking for some online courses and programs.

At the beginning I didn’t have the aim to get a master’s degree in Computer Science but slowly I started learning some basics in data science and machine learning and understood that it was worth studying on a full time basis. MSAI caught my attention on Mastersportal and I decided to enter MSAI basically because of three things.

The first reason is the MIPT brand, it has a great name in the global academic environment. Even now after graduation it helps me with my PhD application because everyone understands that the master program in MIPT is not a joke.

The second one is the MSAI curriculum that I really liked. It was quite structured and flexible in comparison with other programs and courses.

And the last one is the affordable cost of the program. I just saved some money from my work and invested it in education because it is the greatest investment that you can ever make. Education is something that nobody can take away from you.

How was studying on MSAI for you?

First of all, I was never an alien in the program. Although I don’t know Russian, the professors have made sure that they always communicated in English when I was in the webinar room.

Concerning studies, I had to take some additional courses in basic math and programming in Python before MSAI because they are essential for mastering machine learning. However, those courses were not enough because MIPT has a high standard of knowledge which is, on the one hand, great for students but, on the other hand, could be quite difficult. It was not easy for me too but professors were really helpful, so I could always ask any doubts, get a response and a helping hand. Besides, there were a bunch of different projects and assignments that helped to understand even more and go deep in the field.

What else did you like about the program or among things related to it?

The fact that the curriculum is structured so that at the beginning students could get acquainted with basics of machine learning, software development with Python and optimization in the first year and only then have a deep dive into applications of deep learning, computer vision, NLP and so on. So, after three semesters of the program students can feel that they come out with much knowledge of computer science and it enables them to make an informed decision about their thesis topic. So, I think it’s really appreciable that you don’t jump from subject to subject, but gradually go deeper into each one.

Besides, I liked the possibility of taking some more courses in order to master something on an advanced level. It wasn’t compulsory for everyone, in my case, for example, I took only digital signal processing (DSP) as an elective and this course was included in my diploma.

And one more cool thing is the fact that we got some bonus lectures because of dynamic changes in the IT industry. In the third semester, for example, we had three more lectures because it was time when Chat GPT came out. Just in order to know that we were not missing the news our professor gave us some extra classes dedicated to large language models (LLM). It was not a part of the curriculum and he could have told us "okay, learn it yourselves if you want", but he did not. It was really appreciable!

What did you find most useful professionally and personally from your studies?

The most beneficial disciplines for me were Machine Learning (ML) and Natural Language Processing (NLP). I was so inspired by NLP that I even wrote my thesis on it.

Now, I want to implement the results in my organization to simplify the entire information search and processing process: the system should understand the query, find the answer, and provide it within seconds. This will help us move towards a data-driven management approach, where most management decisions are supported by data. This type of management is talked about a lot, but nobody really practices it in reality.

Moreover, thanks to the skills I acquired, I received a career and financial promotion at work and gained some personal qualities such as incredible resilience and perseverance, which really help in my job. If you’ve spent two years in a master’s program working on something that inspires you, the impossible seems much less daunting.

What did you like most about the program or related aspects?

Apart from the obvious things like acquiring knowledge, competencies, and skills, I would highlight the quality of teaching and the sense of community.

The teachers included experienced theorists who have worked at PhysTech for years and young practitioners working directly in the industry. They always had something to share with us because computer vision or robotics is not just their interest; it’s their daily routine.

We had a great group of highly motivated and ambitious people who we still keep in touch with. There were only 15 or 16 of us, but we created a special, friendly student atmosphere, chatting and calling each other to explain complex topics. It was wonderful! One of my warmest memories from the master’s program is linked to my classmates. We had a very tough exam in the first semester, and before going to it, we wrote good luck wishes to each other in our chat, and afterwards — congratulations on passing. It’s a unique feeling of studentship when you stand at the door of the examination room waiting for your turn. It couldn’t be interrupted even online. We genuinely rejoiced for those who passed the test and hoped for the best for each other.

What would you describe as your motto during your studies and in life in general?

If I were to mention something applicable to both PhysTech and life in general, it would be the famous phrase, "Knowledge is never superfluous." This is true, but studying should also be fun. It can be challenging, exhausting, and confusing, etc., but if you’re not enjoying it, it’s also pointless. So it definitely should be fun!

Why did you decide to pursue a master’s degree and learn a new direction?

Yes, after 7 years of studying and working as a developer in Germany, I decided to change my field of activity and obtain another degree. I needed a structured program that would give me all the necessary knowledge in the areas that interested me and keep me disciplined in the process of learning something new. The internet is full of various materials, but it’s easy to get lost and start learning everything at once. That’s why I was purposefully looking for a master’s program.

Why did you choose MSAI, and what did you like about the program and then in the study?

A big advantage of MSAI for me was that it’s an online program, so there was no need to move and radically change my life for the sake of studying. I continued to work remotely for a German company, combining work and education. It saved me a lot of time which I could use to study at my own pace: sometimes revisiting lectures several times, and sometimes fast-forwarding what I already knew.

The learning process was very flexible. If I ever lacked some special knowledge and needed to catch up, I could take additional courses at PhysTech independently. If it was a common problem among classmates, we could write that we needed another course on a particular discipline, and they would set it up for the next semester. It’s a great university practice because it’s normal when students don’t understand something or can’t do something. You just need to know how to work with it.

What phrase could describe your path at PhysTech and your life motto?

"As long as the credits haven’t rolled, there’s a chance to change everything." Several times during the program, I couldn’t solve a difficult task that had to be submitted by midnight. An hour before the deadline, I thought, "Well, that’s it! It’s hardly worth trying anymore"—but despite this, I didn’t give up and tried to solve it until the end. And amazingly, in the last minutes before the deadline, the brain starts working differently, and everything suddenly works out.

So my advice to everyone, not only regarding studies but also life in general — if you want to change something, do it! Want a second master’s degree? Great, go for it! A third one? Excellent, go ahead! As long as you’re breathing, there’s a chance to change everything and try.

How was studying on MSAI for you?

I will never forget the first lecture: we were learning coding and the professor told us to open the terminal and Jupyter Notebook. All of these words were new to me! What is a terminal? What is Jupyter? I knew that Jupyter was a planet and that’s all! I have never coded in Python, so my nightmare started there. But I’m really thankful to the professors because they answered every silly question in a clear way and took it seriously even if the answer seemed obvious to them. They helped me a lot.

To be honest, in the beginning of the program I thought that if it is online you can cheat but, believe me, you just can’t. Studying online seemed to me much more difficult than studying in person. You need to study and there is no other way to go through the program and only you are responsible for it.

How did the program affect your current professional and scientific interests from your personal point of view?

Actually, I was accepted for my PhD because of MSAI. My supervisor here is Russian and I contacted him during my studies in MIPT. And he immediately answered me and agreed to have a zoom meeting! I don’t think he would do that for a random guy but I was an MIPT student and studied on MSAI, so.

Besides, because of the program I could change my professional direction and continue to develop in computer science as a PhD student. I suppose machine learning was one of the most useful disciplines for me. I had a lot of gaps in basic things but the professor helped me mind them and make the final project. So, in the 4th semester I was doing two things at the same time: improving my coding skills and doing my final project.

I really don’t think that if I had had some problems with programming at the end of the program somewhere else, professors would have helped me with it by sending me some articles and videos on YouTube. They would have said something like: "Just do your project". But there it was not like that. Everybody cared about my understanding of the subject.

My current PhD project is dedicated to decision rules. So far, I have analyzed some theoretical aspects of the project and now I have at least two or three more years to make the project and finish my PhD.

What advice would you give yourself if you have just entered MSAI?

Personally, these months before the beginning of the program I thought: "If you don’t know something on the high level, you still can handle it and learn what is necessary during the course". Unfortunately, I was wrong. That’s why I would advise myself to learn a lot of Python stuff before the course because the sufficient level of Python for MSAI is at least intermediate. Mine was zero, so, my time was divided between the main course and programming on Python. If you are good at coding before the program, it might be much more understandable and efficient for you.

However, at the same time professors could organize some extra classes for those students whose background was not enough to understand a new subject, especially if there were many people like that.

What was your motto during the program and generally in life?

You know, when you are watching some educational videos on YouTube their titles usually include something like "From Zero to Hero"? I think this motto is for me! In the beginning of the program I was zero at least in computer science and now I’m doing my PhD in this field. It’s incredible!

Why did you decide to join the MSAI program and learn a new direction?

I had been struggling with a work project for a long time and couldn’t figure out how to approach it, no matter how many articles I read online or how much information I dug through. Standard methods didn’t work; there wasn’t enough data to solve the problem. Then it occurred to me that I could try to solve it using statistical methods, and for that, I had to start programming a lot. Suddenly, I discovered that there were many existing, proven methods for solving my problem, and I was essentially reinventing the wheel because I simply lacked knowledge. That’s when I realized it was time to refresh my skills and learn something new.

I didn’t change the main direction of my work—it was important for me to acquire new skills and use them in my job. In this sense, the MSAI program gave me exactly what I came for—methods of machine learning and data analysis that have already been useful and continue to be useful in my work. Moreover, I knew a lot about MIPT since the times when I studied at the Physics Department at Novosibirsk State University, where many teachers were the first graduates of PhysTech. I had no doubts about choosing a university and was glad to have the opportunity to go through the program there.

What did you like most about the program or related to it?

First of all, I liked the selection of materials and what we were generally doing. Along with classic statistical methods, we studied many new developments that had appeared recently, and they were included in the program, which is important for such a dynamic field as artificial intelligence. The program was very organic: I can’t say that anything was too late or, conversely, ahead of its time.

I also really liked the team of teachers. For the most part, we had people who work in the relevant areas, such as specialists in computer vision, machine learning, robotics, and so on. They brought us what they know and use every day, straight from the industry. It was great.

I was also pleased with the opportunity to take additional courses, the choice of which was practically unlimited. I took, for example, several courses on graph theory from the program on modern combinatorics, and they were included in my diploma after passing the exam. It seemed like you could also take additional courses on Coursera or other platforms, but in reality, it all depends exclusively on how much free time and interest you have for studying something new.

And, of course, the remaining contacts with classmates are important. It was very interesting how people who have diverse professional experiences suddenly become students again. It’s not the same as when you just graduated from a bachelor’s program and entered a master’s program full of enthusiasm—it's a completely different level of time management.

How would you describe your motto for studying at MSAI or in life in general?

At PhysTech, there’s already a good motto: "Dare and Know." I like it; I followed it. And in life, I had a very good physics teacher at one time, and he said, "Life is beautiful and amazing, and the harder it is, the sweeter the victory." I try to follow him. Still do.

Why did you choose the MSAI master’s program for further education?

I have a long history: after school, I studied at PhysTech, not in Moscow but at the Kharkov faculty of nuclear physics. Then perestroika began, and I never finished university. At the end of the 1980s, I had a dream—to graduate from PhysTech. It turned out that I was only able to do this 35 years later, and in a different specialty.

I chose a different direction because I realized that what I was doing now did not bring me the pleasure that I would like to get from life and work. Yes, I am a manager, and yes, I have people under me, but we are in the field of accounting, which does not have broad development prospects. Everything related to machine learning and artificial intelligence is rapidly developing — that’s the future. Why not try to understand it, especially since I can still actively do something professionally for another twenty years, and I have always been somewhat close to mathematics and physics, never having abandoned them completely.

What did you like most about the program?

Initially, I was very taken with the idea of an online program because I am originally from Irkutsk and now live in Israel, so I would not have had the chance to finish PhysTech if the program were in-person.

It’s great that all the main sections necessary for understanding the field of artificial intelligence were included in the program—from the basics, like probability theory, statistics, and algorithms (the algorithms course was excellent!), to machine learning, natural language processing, and other useful disciplines.

Another major plus of the program is that it’s conducted in English. This gives students a correct understanding of terms and the opportunity to immediately start reading the latest scientific articles on artificial intelligence.

And it’s worth noting that MSAI is a "living" program; it constantly changes in an attempt to keep up with the development of the field. Some things are replaced, others are added to offer students the most current knowledge of a rapidly developing area. There probably isn’t a perfect program that can keep up with all changes, but the fact that something is changing and being updated is already a significant advantage of MSAI.

What are your professional and educational plans for the near future?

I am preparing for interviews with technology industry giants because only they have well-established work with large language models (LLM), which I would like to engage in. If it works out to go into them—great; if for some reason it doesn’t—I'll look at other directions in the field of artificial intelligence.

What advice would you give to those who are just entering the program?

Study, study, and study again! You need to work hard from morning to evening from the moment you enroll until the moment you receive your diploma because with it you can only open the door to the company where you want to work, and then you need to demonstrate your knowledge and skills.

There are two ways to apply:

1. apply through education-in-russia.com between September 15 and December 15
2. or enter the international Open Doors Olympiad at od.globaluni.ru

From August 2026, email msai@phystech.edu. The MSAI team will bring you into the process, send step-by-step instructions, and help with your application.

Important: quota applicants also go through MSAI’s own selection — an entrance assessment and an interview. And when you apply through the portal, list MIPT as your first-choice university.

Application Timeline

August 2026
Email msai@phystech.edu to register your interest. The team will bring you into the process and send detailed instructions.

September-December 2026
Apply through education-in-russia.com or enter the Open Doors Olympiad. MSAI’s own selection happens during this window too.

Spring-Summer 2027
You get the official decision on the scholarship and your admission.

September 2027
You start at MIPT.

Documents you’ll need

• passport
• a notarized Russian translation of your passport
• your Bachelor’s diploma and transcript
• a notarized Russian translation of the diploma and transcript
• a motivation letter
• a resume / CV
• letters of recommendation, if you have them

To finish enrollment, you’ll submit your original diploma and transcript to the MIPT Admissions Office in Russia.

The MSAI team is with you through prep and application. Questions about quota admission? Email msai@phystech.edu.