Summary
Dominik Glandorf is a doctoral researcher and machine learning engineer focused on improving education through AI-driven assessment, personalized interventions, and richer interactions with knowledge. With nine years of experience spanning data science, quantitative research, and full-stack development, he blends experimental rigor (experimental design, ethics, reporting) with practical engineering (Node, React, SQL). His work ranges from predicting student dropout and dyslexia to building interoperable knowledge-exchange tools connecting systems like Anki and LogSeq. Currently a PhD student at EPFL/ETH Zürich’s ML4Education lab, he has collaborated with institutions including Yale and Tübingen, bringing interdisciplinary strength from psychology and IT-systems engineering. He is skilled at turning noisy educational signals (eye-tracking, MOOC logs) into actionable models and privacy-aware integrations. Beyond academia, his background in data journalism and production-grade software shows a rare combination of storytelling, engineering, and methodological care.
9 years of coding experience
10 years of employment as a software developer
Master, Quantitative Data Science Methods, Master, Quantitative Data Science Methods at Eberhard Karls Universität Tübingen
Bachelor of Science - BS, Psychologie, with distinction, Bachelor of Science - BS, Psychologie, with distinction at Georg-August-Universität Göttingen
Doctor of Philosophy - PhD, Joint Doctoral Program in the Learning Sciences, Doctor of Philosophy - PhD, Joint Doctoral Program in the Learning Sciences at EPFL / ETH Zürich
Bachelor of Science - BS, IT-Systems Engineering, with distinction, Bachelor of Science - BS, IT-Systems Engineering, with distinction at Hasso Plattner Institute
Visiting Graduate Student, Computer Science, Visiting Graduate Student, Computer Science at Yale University
University of Seville
Master Thesis, NLP for Education, Master Thesis, NLP for Education at ETH Zürich
German, English, Spanish, French