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18–31 August 2027 • Selwyn College, Cambridge

18–31 August 2027 • Selwyn College, Cambridge

Faculty and teaching

Our teaching method, Learning by doing, was developed at Cambridge for our own economics students. It is built on cognitive science (worked examples, guided practice and continuous feedback) and has won international awards, including a QS Reimagine Education Award chosen from over 1,600 entries in 94 countries. Here, you are taught by the Cambridge academics who created it and use it every term.

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  • Method

    Learning by doing

    Built on cognitive science, used with our own Cambridge students

  • Awards

    Teaching awards

    Recognised by QS, the European Economic Association and Cambridge

  • Teachers

    Cambridge academics

    Eight who teach Cambridge students, one for each course

  • Feedback

    Daily feedback

    On your own code and analysis, from your teachers

Meet the faculty

Eight Cambridge academics, one for each course. Select a profile to read their biography.

  • Roman Berlanger

    Teaching Associate

  • Oleg Kitov

    Associate Professor

  • Daniele Cassese

    Assistant Professor

  • Dmitrii Petrukhin

    Teaching Associate

  • MuynGun Kim

    Associate Professor

  • Ruohan Qin

    Assistant Professor

  • Vasileios Kotsidis

    Associate Professor

  • Weilong Zhang

    Associate Professor

Roman Berlanger

Teaching Associate

AI101 Foundations

Applied AI for Business and Management

Roman is a PhD student in Economics at the University of Cambridge, a researcher in the Leverhulme International Professorship Lab and a Teaching Associate at Selwyn College. His research looks at how people make decisions when problems are computationally complex. He is one of the few PhD students to be a co-investigator on a Keynes Fund research grant, for a project linking complexity, AI, firm size and inequality. He holds master’s degrees from Cambridge, HEC Paris and the Solvay Brussels School, and a bachelor’s degree in psychology. He teaches industrial organisation, and banking and finance, and has taught macroeconomics, physics and operations management at European universities.

Oleg Kitov

Associate Professor

AI201 Advanced

Deep Learning and Large Language Models

Oleg is an Associate Professor of Economics and Director of Undergraduate Admissions in Economics at the University of Cambridge, and Director of Studies, Postgraduate Tutor and a Robert Martin Fellow of Selwyn College. He developed Learning by doing, the teaching method behind this summer school. A Senior Fellow of the Higher Education Academy, he has won a major teaching prize almost every year since 2018, most recently the European Economic Association’s Senior Award for Exceptional Teaching and Cambridge’s Pilkington Prize (both 2026). Over 15 years he has taught more than 20 courses at Cambridge and Oxford, including mathematics, statistics, econometrics and macroeconomics. His research on income distribution and applied econometrics is funded by the Keynes Fund and the Isaac Newton Trust.

Daniele Cassese

Assistant Professor

DS101 Foundations

Data Science for Economics and Business

Daniele is the Isaac Newton Trust Career Development Fellow of the University of Cambridge; Director of Studies in Economics and Fellow of Trinity Hall College. For a decade, he taught microeconomics, mathematics, statistics, industrial and network economics at Cambridge and Oxford. He earned his PhD in Economics from the University of Siena, and his research explores networks and complex systems.

Dmitrii Petrukhin

Teaching Associate

DS201 Advanced

Machine Learning for Prediction

Dmitrii is a PhD student in Economics at the University of Cambridge and a Teaching Associate at Selwyn College, where he has taught since 2023. After his undergraduate degree at the LSE, he completed an Advanced Diploma in Economics and an MPhil in Economic Research at Cambridge. His research in development economics and political economy examines how politics and institutions shape development, using econometrics and machine learning. He teaches undergraduate and postgraduate econometrics and data science, and supervises econometrics at Selwyn and other Cambridge colleges.

MuynGun Kim

Associate Professor

EC101 Foundations

Macroeconomics in Theory and Practice

MuynGun is a Fellow of Selwyn College, a College Associate Professor and Director of Studies in Economics at Selwyn and Robinson colleges, and a Bye-Fellow of Lucy Cavendish College. He earned his PhD in Economics at Cambridge, where he won the Faculty of Economics’ Best Teaching Prize three times, and has taught macroeconomics, statistics and econometrics at Cambridge for over a decade. His research in applied microeconomics focuses on firm productivity, innovation and technology adoption. He is currently studying the costs and benefits of AI adoption in UK manufacturing, combining large-scale firm data with evidence from firm visits.

Ruohan Qin

Assistant Professor

EC201 Advanced

Microeconomics and the AI Economy

Ruohan is a College Assistant Professor, Director of Studies and Fellow in Economics at Murray Edwards College, a College Teaching Associate at Selwyn, a Bye-Fellow of Downing College and a Senior Teaching Member of Robinson College. He has taught microeconomics, labour economics, industrial organisation and the economics of networks, and has won the Faculty of Economics’ Best Teaching Prize several times. He holds postgraduate degrees from Cambridge, the LSE and the University of Nottingham. His research explores the “dark side” of economics, where weak property rights lead people to seize resources through theft, raiding and war: how conflict networks form, and which institutions can reduce conflict.

Vasileios Kotsidis

Associate Professor

FI101 Foundations

Financial Markets and Investment

Vasileios is an Associate Professor of Economics at the University of Cambridge, Director of Studies, and Fellow of Gonville & Caius College. An award-winning lecturer, he has taught microeconomics, mathematics, statistics, and political/public economics at Cambridge and LSE for over a decade. With a Nottingham PhD and Cambridge MA, his research advances traditional and evolutionary game theory applied to social interaction.

Weilong Zhang

Associate Professor

FI201 Advanced

Quantitative Finance with AI

Weilong is an Associate Professor of Economics at the University of Cambridge, Postgraduate Admissions Officer and a Fellow of Selwyn College. He holds a PhD in Economics from the University of Pennsylvania, and an MPhil in Economics and a BA in Mathematics from Renmin University of China. His research spans labour, education, family and health economics, from personality and the gender pay gap to minimum wages and universal preschool, and appears in leading journals including the Journal of Political Economy, the Review of Financial Studies and the Proceedings of the National Academy of Sciences. He is an Associate Editor of the International Economic Review. He has taught microeconomics, finance, labour and public economics, consistently receiving top evaluations.

How we teach

Learn it in the morning. Use it in the afternoon.

This is Learning by doing, the award-winning teaching method we developed at Cambridge. Each day has one goal: you see a new method in the morning, practise it with guidance, and by the afternoon your team can use it independently in your project.

  1. Morning · lecture

    We show it

    An instructor explains the theory, then applies it to data with code, step by step, so you see exactly how it works.

  2. Morning · practice class

    You practise it, with guidance

    You apply the same method, with almost the same code, to a new structured dataset, with an instructor on hand to guide you.

  3. Afternoon · team project

    You use it independently

    Your team of three applies it to unstructured data to answer your research question, with AI as your coding partner and every decision your own.

  4. Every day

    You build on it

    Tomorrow the loop starts again with a new method, so your project grows until your team presents it on the last day.

Teaching awards

What our 2026 students say

  • “Cambridge has a strong academic atmosphere, and the teachers really respect students. They’re very patient: they come round our group desks to check whether we have problems, and we can talk to them one on one.”

    Portrait of Yifei Jiang Yifei JiangEconomics and Finance, Year 2Xi’an Jiaotong-Liverpool University

  • “We have three hours of lectures and classes in the morning, then project work in the afternoon. My Cambridge Director of Studies runs our lectures. He’s great; he teaches really well!”

    Portrait of Lewis Redman Lewis RedmanEconomics, Year 1University of Cambridge

  • “The biggest difference is the teaching style. At many Chinese universities, the teacher lectures and the students listen. Here the classes are small and really interactive, with a lot of freedom, and that makes you think more critically.”

    Portrait of Changhua Quan Changhua QuanEconomics, Year 3Nanjing University

18–31 August 2027 • Selwyn College, Cambridge

Ready to join us?

From £4,900 with tuition, college room and events

  1. Priority deadline1 January 202710% off
  2. Early-bird deadline31 March 20275% off
  3. Final deadline30 June 2027
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I liked the project most, because that’s where you learn the most: you work things out for yourself and guide yourself to the finished product. It lasted only two weeks, so we pushed ourselves and got there faster.

Portrait of Rokas Valuckas Rokas ValuckasEconomicsYear 1 · University of Cambridge

What you need

  • Aged 18+Students and graduates
  • MathematicsBasic calculus and probability
  • EnglishIELTS 6.5 or equivalent
  • GPA3.2 / B+ recommended

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