Dr. Johannes Linder

Johannes Linder obtained his Master’s in Computer Science from the Royal Institute of Technology (KTH) in Stockholm and later pursued his PhD in Computer Science at the University of Washington in Seattle. It was in Seattle he discovered his deep interest for regulatory biology and the role that Machine Learning can have in helping us understand gene-regulatory function. Afterwards, he did a year of postdoctoral research at Stanford University before taking a temporary hiatus from traditional academia to work as a Research Scientist at Alphabet’s longevity research company Calico Life Sciences in San Franscisco. In 2026, Johannes started his research group at Helmholtz Munich in partnership with Roche.

Johannes’ research focuses on three inter-connected branches of machine learning for functional genomics: (1) regulatory sequence-to-function learning, (2) interpretability, and (3) rational design. Within the space of sequence-to-function learning, he has developed some of the most accurate models for predicting the impact of genetic variation on gene expression, mRNA processing, and more. Within the space of design, he has developed efficient methods for optimizing regulatory sequences based on the predictions of neural networks and used these to design improved non-coding regulatory sequences for various modalities, including for therapeutic applications.