Faculty in focus: Slava Jankin

“That’s what excites me the most – it’s the opportunity to contribute to the common good. And that’s what I’m trying to do with my research, with my work and also with our upcoming programme as well.”

Torrencia Cardinal from the Recruitment and Admissions team had a sit-down with the person behind the Hertie School’s Data Science Lab: Slava Jankin, Professor of Data Science and Public Policy. His research and teaching are mainly centred on machine learning and natural language processing. Looking ahead to the launch of our Master of Data Science for Public Policy (MDS), we invited Slava for a brief chat about what he does, what excites him in data science and policy, and what he expects from his students. Listen to the full interview and see an abridged transcript below.

You taught Machine Learning in 2019 and will be teaching it again this coming spring. So tell me, what is the most exciting thing about this class?

I think the most exciting part about teaching machine learning is to see all kinds of different ways students find for its application in topics that are relevant for the common good. It’s anything from international development to the third sector, social care, support for elderly and vulnerable people, developing applications based on machine learning tools they learn in class and seeing how these applications can be used as solutions to tackle some of the problems that face society today. For me, the most exciting part is to see these applications develop in the course and then hopefully have them traced out into the world. And hopefully it also helps students in the future and their future jobs.

The first cohort for the Master of Data Science for Public Policy will begin their studies in fall 2021. Why do you think a strong foundation in public policy is crucial for data science/data scientists – and vice versa?

I think we have to be absolutely clear that we’re not going to train AI researchers or computer scientists. We are a policy school, and we’re looking for AI-enabled policymakers and practitioners. That’s who we want to create in the end of this two-year master’s programme. It should be policymakers, practitioners working in their fields, whatever the domain that they’re working in. Again, it can be international development, it can be social care, it can be working in international organisations or in the city or state government, or at the federal level in Germany. That doesn’t really matter, but we want to enable them with artificial intelligence tools that allow them to leverage and apply their skills much better. So, a foundation in public policy is crucial because it’s the core and the first building block along this journey. We want to have, in the end, public policy professionals, policymakers, practitioners who are leveraging the skills and who are enabled by artificial intelligence.

In what ways does the Data Science Lab collaborate with the Hertie School's Centres of Competence?

We have close collaboration with the Centre for Digital Governance at Hertie. In terms of teaching, we’re teaching together with Gerhard Hammerschmid, who is the director of the Centre for Digital Governance. We’re teaching a course on artificial intelligence in government, and we’re looking at different perspectives. On the technical side, I’m contributing to the course. And then actually how do you embed these things in government? That’s where Gerhard comes in. Now the professor in the Centre for Digital Governance is Joanna Bryson, and we’re again co-teaching a course on governance and politics of artificial intelligence – looking at slightly different issues around regulation, ethics of AI and how it works in society. We’re also working together on research, previously for the German government, for the Bundestag, on AI in the public sector. So, there are lots of opportunities, lots of things we’re working on, naturally very closely with the Centre for Digital governance. But we’re also working beyond that, with the Centre for Fundamental Rights and the upcoming sustainability centre.

What excites you about this particular subject area and what do you enjoy most about teaching students at the Hertie School?

What excites me the most is the opportunity that this area brings, and that if we leverage the skills and the tools that are coming out of machine learning and out of artificial intelligence more generally, we can apply them to so many different domains to improve the lives of people in those areas – but actually contributing to the common good. That’s what excites me the most – it’s the opportunity to contribute to the common good. And that’s what I’m trying to do with my research, with my work and also with our upcoming programme as well. And your second question about the students at Hertie, I think they’re excellent. It’s an amazing combination of the skill but also representation from different parts of the world, it’s international. And the skills, interests and previous experience that are being represented, all these things together make a really wonderful mixture. It’s a pleasure to work with Hertie students and also see them develop along the way. It just brings me joy.


What do you expect or appreciate the most from students in your classes?

I think it’s one thing: it’s curiosity. And maybe just a tiny, tiny thing to add is hard work. So curiosity and trying to learn things and continuously learning – I think this is one thing that I appreciate and expect a lot from my students. Just being interested in the things that we are discussing and studying and learning, and that is hard work. A lot of things related to machine learning, they do require hard work. So, a combination of curiosity and hard work, I think that’s wonderful.


This is an abridged transcript of an interview recorded before the Meet the Faculty event in the first week of November, where Slava Jankin presented his research. If you want to find out more about the Master of Data Science for Public Policy or the research of the Data Science Lab, we invite you to attend the launch of the MDS programme on 25 November, 4 pm CET.

In conversation