Profile picture
Administrative data

Tanja Käser

tanja.kaeser@epfl.ch

EPFL IC IINFCOM ML4ED
INF 234 (Bâtiment INF)
Station 14
CH-1015 Lausanne

EPFL IC IINFCOM ML4ED
INF 234 (Bâtiment INF)
Station 14
CH-1015 Lausanne

EPFL IC IINFCOM ML4ED
INF 234 (Bâtiment INF)
Station 14
CH-1015 Lausanne

EPFL IC IINFCOM ML4ED
INF 234 (Bâtiment INF)
Station 14
CH-1015 Lausanne

EPFL IC IINFCOM ML4ED
INF 234 (Bâtiment INF)
Station 14
CH-1015 Lausanne

EPFL IC IINFCOM ML4ED
INF 234 (Bâtiment INF)
Station 14
CH-1015 Lausanne

Tanja Käser is an assistant professor at the EPFL School of Computer and Communication Sciences (IC) and head of the ML4ED laboratory. Her research lies at the intersection of machine learning, data mining, and education. She is particularly interested in creating accurate models of human behavior and learning.
Prior to joining EPFL, Tanja Käser was a senior data scientist with the Swiss Data Science Center at ETH Zurich.
Before that, she was a postdoctoral researcher with the AAALab at the Graduate School of Education of Stanford University.
Tanja Käser received her PhD degree from the Computer Science Department of ETH Zurich. In her dissertation, completed at the Computer Graphics Laboratory, she focused on user modeling and data mining in education, which was honored with the Fritz Kutter Award 2015.

Awards

Credit Suisse Award for Best Teaching 2023

2023

Teaching & PhD

PhD Students

Bahar Radmehr, Marta Knezevic, Seyed Parsa Neshaei, Ekaterina Shved, Fares Fawzi, Abhinand Shibu, Dominik Glandorf, Fatma-Betül Güres

Past EPFL PhD Students

Paola Mejia, Vinitra Swamy, Jade Maï Cock

Courses

Digital education

CS-411

This course addresses the relationship between specific technological features and the learners' cognitive processes. It also covers the methods and results of empirical studies: do student actually learn due to technologies? In fall 2025, P. Dillenbourg will co-teach this class for the last time.

Machine learning for behavioral data

CS-421

Computer environments such as educational games, interactive simulations, and web services provide large amounts of data, which can be analyzed and serve as a basis for adaptation. This course will cover the core methods of user modeling and personalization, with a focus on educational data.

Topics in Machine Learning for Education

CS-702

This seminar course covers the interdisciplinary field of machine learning for education. By reading, reviewing, and presenting research papers from recent venues, students will become familiar with core issues and techniques in the field