Engin Bumbacher

My background is in physics and theoretical neuroscience, both of which I studied at the Swiss Federal Institute of Technology Zurich. I discovered education as my desired playground during my time at the Redwood Center for Theoretical Neuroscience at UC Berkeley, and haven’t left it since. I’m starting this fall with my phd in computer science with focus on collaborative learning technology at EPFL, Switzerland.

Research interests: My long-term goal is to unravel general principles of intelligence and learning and to use these principles to design interactive, tangible technology that enables people to understand their world in profound ways and empowers them to actively engage in shaping them. I want to contribute democratizing and de-institutionalizing education by developing adaptive tools that allow learning communities to customize technologies to meet their own needs, thus making people own their education again.

I am specifically interested in the use of machine learning in education. While education is a field rich with data, obtaining high-quality data and processing them meaningfully and efficiently remains difficult. I am convinced that intelligent ways of collecting, mining, and analyzing large data sets about learning will provide us with an improved understanding of students’ thinking processes and knowledge, of their learning styles and responses to different pedagogical strategies. This would provide us with a more general view of educational principles and enable new ways of personalized education.