MVPs by interdisciplinary teams
The main goal of the Advanced Engineering School (AES) is to train principal engineers capable of assembling teams out of specialists from different industries. As heads of these teams, they would be able to apply a data-driven approach to the development of relevant products and integrate them into ITMO’s partner companies. To achieve that purpose, the school’s staff have grouped Master’s students of biotechnology, chemistry, optical engineering, and IT into teams, tasking them to develop minimum viable products (MVPs) by the end of their studies.
“Why are MVPs so important? First of all, they provide a slight guarantee that after graduation, these students will already know what they want to do – and, more importantly, where they’ll do it. Secondly, businesses often require specific products that, ideally, should be developed and maintained by the same team. At AES, the students have the opportunity to interact with companies and identify the most comfortable and productive approaches for the future, as well as develop an MVP. Some of these products will be integrated into the business pipelines of our partners: oil-and-gas corporations Gazprom Neft and Tatneft,” explains Alexander Vinogradov, head of AES and a lead researcher at ITMO’s ChemBio Cluster.
The chance to graduate from the Advanced Engineering School will be provided to 55 students of three Master’s programs: Chemistry and Artificial Intelligence, Machine Learning Engineering, and Digital Systems Engineering. As part of a network collaboration, the school has also been joined by 12 students of the AI in Biotechnology Systems program at Almetyevsk State Oil Institute.
According to Prof. Vinogradov, these programs were chosen for their relevance to the industrial partners’ interests, as well as how well they develop the students’ data processing competencies. Tatneft, for instance, has plans for development in the field of high-tech biotechnologies; Gazprom Neft set its sights on optical engineering and digital systems. But both companies also wish for the students to not only gain the necessary hard skills in their respective fields, but also to possess expertise in the application, processing, and verification of data when working with conventional algorithms and machine learning technologies.
The study process at AES
In their first year, the students will form interdisciplinary teams made up of physicists, chemists, biologists, machine learning specialists, backend and frontend developers, product managers, and so on. Their main task is to develop an industrial or entrepreneurial project and garner the support of their subject area’s PIs.
In the second year, the students will have to decide what their role should be as team members. They may choose to be engineer intrapreneurs, broker engineers, software engineers, or expert engineers. The results acquired in the development of each project will be used to produce prototypes and, eventually, MVPs.
In order to help the teams accomplish their goals, the school’s staff will conduct classes involving mentors – representatives of partner companies. For instance, in the first year the students will learn about digital competencies and data-driven approaches from the staff of major IT businesses – and about product management from top managers. In the second year, invited brokers will provide insights into business process analysis and how to find investors and clients; heads of R&D centers will advise on research procedure and the development of innovative tech products. Upon graduation, the students will be able to pick between several formats in which to present their graduation thesis: a startup, a business analysis, an engineering project, or their own product.
“One of the school’s features is that it exists as an extension of the primary educational programs. In other words, we enhance the base curriculum with minors that provide students with competencies that are essential to such specialists. The school’s graduates won’t be simply engineers and research leads, but rather principal engineers capable of organizing their teams and leading them,” says Dmitry Botov, AES’s head of education and head of the Machine Learning Engineering program.
Support for students
To provide support for the students’ promising projects, the staff will launch a stipend program available exclusively to the school’s first-year students. The program will consist of five stages: applicants will need to present their product concept, substantiate its relevance, then demonstrate a prototype version, an initial MVP, and a final MVP. What’s notable is that the funding is provided separately for each stage. For example, if a student doesn’t pass the first stage, they can still develop a prototype and rejoin the program in the third stage.
ITMO students who are not part of AES can apply to a grant contest for the formation of a product team. Ten winning teams will receive up to 1.5 million rubles for the final four months of 2022. To do that, the applicants must first assemble an interdisciplinary team familiar with approaches based on the analysis and application of massive arrays of experimental and theoretical data and then develop a scalable product.
We spoke to some of the students who have joined the Advanced Engineering School and asked them about the new educational format and their project plans.
First-year Master’s student, Digital Systems Engineering
The school has an unusual premise and approach to studying. Usually, schools merely train specialists, but here we can assemble our own teams, try out different roles, develop a viable product, and, possibly, even find a job. It’s a great opportunity since you won’t have to wait until after graduation to hone these skills.
When I studied for my Bachelor’s, I was going to become an information systems specialist. But after the first year, I switched to 3D modeling; in my fourth year, I decided to also branch out into 3D printing, making additive manufacturing the topic of my thesis. 3D printers have become very popular now, but they are still very immobile. Even the slightest agitation during printing may damage a layer and result in segmentation. I would like to help make this technology more widespread, accessible, and compatible with a larger number of materials.
First-year Master’s student, Chemistry and Artificial Intelligence
The idea for a collaboration between engineers and businesses is a great one, because it’s clearly impossible to develop all the necessary abilities and manage a product all on your own. It’s better to be responsible for your specific area. Places like AES are good for finding potential partners.
There are many things I’d like to do at the school. But for now, it’s difficult to settle on something specific: the idea must receive support from a PI in my subject area and representatives of business. The fields I am most familiar with are chemistry, medicine, and pharmaceutics, but I wouldn’t be against working in biotech or the oil industry.
First-year Master’s student, Machine Learning Engineering
I really love the idea of the school. First of all, the staff want to work with interdisciplinary teams. My personal experience shows that projects founded at the intersection of different fields tend to be of higher quality. I’m currently involved in two such projects – an agricultural one and a medical one. The other thing that appeals to me is the combination of studies and project work. This is something that’s severely lacking at universities. After graduation, students go into the business world without an understanding of how to make money and develop the right products. I’m glad that at ITMO, I’ll be able to learn that and then test my skills in practice.
Many years ago, I received an education as a mathematician and systems programmer. Now, I’ve started on a path in machine learning. I would be interested in chatting with chemistry and biotech students to discover interesting ideas.