Introducing… Daniel van Strien

What’s your name?
Daniel van Strien

What’s your background?
I studied Economic and Social History at Glasgow University during which I also got to take courses in Philosophy, Sociology, Anthropology and Economics. I later did a master’s degree in Library Science at City University during which I became very excited about the use of new digital approaches to research and collection management.

In one sentence, what is your role on the project?
As Digital Curator for the project I will ensure that Living with Machines develops outputs (considered broadly) that will have a useful impact on a range of audiences.

What excites you about the project?
I am particularly excited about the interdisciplinary approach to research in the project. Living with Machines involves people with different skills and research methodologies and it will be exciting to see what results will emerge from bringing together these different approaches. One desirable output for the project will be the development of tools which provide easier access to data science methods to historians and social scientists. I have already seen useful examples of historians working with computational linguists to provide context which might otherwise be missing when pursuing computational approaches. I’m keen for this type of collaboration to continue to develop throughout the project.

What challenges do you see ahead?
Many! Some will be familiar challenges that come from a project involving many people and moving parts but other challenges will be more closely related to the interdisciplinary nature of the project and ensuring that people truly work together rather than alongside each other with overlapping goals. It won’t be possible for everyone to absorb everyone else’s expertise but it would be good if there was an ongoing process of learning about each other’s disciplines.

What’s the last (non-work) book you read, exhibition or performance you saw?
I am dipping in and out of Deep Learning with Python by Francois Chollet as I work through the fast.ai deep learning course.

Where can we find out more about you and your work?

I am on twitter and I hope to be contributing to this blog fairly regularly.