About the School


The ambition of the school is to be able to help people acquire crucial knowledge and skills in many parts of the world. Not just in developed countries. Not just in developing countries. In countries at all income levels.


Of course, lack of finances is a major hurdle for many people in acquiring education. A solution to that problem that the school pursues is Fair Play Tuition , which allows students to join even if they lack finances at the moment. It’s not an exception to have in the same class a highly-paid software engineer in Silicon Valley who wants to upgrade their skills and someone in a village in a developing country who has almost no financial resources but a lot of intellectual potential.


The flexibility of the school makes it possible to acquire knowledge and skills even for students with very demanding personal circumstances. For example, it is possible to complete major parts of the curriculum while on parental leave.

Minority groups and underrepresented populations are super welcome at the school, as this helps to empower a lot of new talent.

Over time, the school aims to educate people of all ages. So far the age range was 16 to 67, but the hope is to expand this further with courses at new knowledge levels.


Currently, the school is focused on data science, statistics, and software engineering, but over time, this will expand to all subject areas that can make a difference in people’s lives such as economics or life sciences, including practical matters such as personal finance or healthy lifestyle and optimal nutrition.

 About the school’s founder

Michal Fabinger

Educator and Researcher

Ph.D. Physics (Theoretical Physics: String Theory), Stanford University

Ph.D. Economics (International Economics, Firm Behavior), Harvard University

Michal is the main person responsible for this school's operations, teaching, and cloud engineering.

Michal conducted research on high-energy theoretical physics (string theory), international trade, international finance, industrial organization, spatial economics, and other subjects. At the University of Tokyo and the Pennsylvania State University, Michal taught courses on Deep Learning, Data Science, Statistics, Asset Pricing, International Trade, International Finance, and Development Economics.

Michal started his research as an undergraduate at Charles University in Prague, collaborating on a string theory project with Petr Hořava, then at Caltech. He completed his physics Ph.D. under the supervision of Eva Silverstein at Stanford. At that time he also extensively traveled to developing countries and learned about the living conditions there.

After a short postdoc at the IAS in Princeton, Michal joined Harvard University as a Junior Fellow at the Harvard Society of Fellows. Meeting Ben Olken there convinced him that academic research can play an important role in improving conditions in developing countries. Since he felt he still had much to learn, Michal completed a Ph.D. in economics at Harvard University. His primary advisor was Gita Gopinath, now the Chief Economist at the IMF. Similarly, Michal spent a substantial amount of time on other data-intensive subjects, such as machine learning and artificial intelligence.

Traveling to the Philippines as a part of Yasuyuki Sawada's Development Economics research team convinced Michal that there is room for an independent institution/school that could solve many problems that are currently left unsolved. He believes that relative to universities, it is possible to increase the quality/relevance of education by an order of magnitude and simultaneously decrease the cost by an order of magnitude. Of course, this requires the right approach, the right technology, and a great community.

While running the school, Michal also continues to teach his Data Science, Statistics, and/or Economics courses at the University of Tokyo and conducts research there.